diff --git a/NEWS.html b/NEWS.html
index a4242be5..896d4f83 100644
--- a/NEWS.html
+++ b/NEWS.html
@@ -410,6 +410,10 @@ <h2 class="anchored" data-anchor-id="development">Development</h2>
 <ul>
 <li><code>formula</code> can now include variables on the left-hand side to indicate the subset of columns to summarize: <code>datasummary_balance(mpg + hp ~ am, data = mtcars)</code> Thanks to <a href="https://github.com/etiennebacher"><span class="citation" data-cites="etiennebacher">@etiennebacher</span></a> for feature request <a href="https://github.com/vincentarelbundock/modelsummary//issues/751">#751</a>.</li>
 </ul>
+<p>Minor:</p>
+<ul>
+<li>Unnecessary text printed to screen on some F stat computations is now suppressed.</li>
+</ul>
 <p>Bugs:</p>
 <ul>
 <li><code>escape</code> argument not respected in <code>datasummary_df()</code>. Thanks to <a href="https://github.com/adamaltmejd"><span class="citation" data-cites="adamaltmejd">@adamaltmejd</span></a> for report <a href="https://github.com/vincentarelbundock/modelsummary//issues/740">#740</a></li>
diff --git a/man/coef_rename.html b/man/coef_rename.html
index 3a2458da..b71a2bd0 100644
--- a/man/coef_rename.html
+++ b/man/coef_rename.html
@@ -459,12 +459,12 @@
   
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diff --git a/man/datasummary.html b/man/datasummary.html
index 451b8b1e..d45d0591 100644
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+window.addEventListener('load', function () { styleCell_tinytable_zirzka3ypbzffwutr59k(0, 0, 'tinytable_css_pdce4pft37miy50ry114') })
+window.addEventListener('load', function () { styleCell_tinytable_zirzka3ypbzffwutr59k(0, 1, 'tinytable_css_pdce4pft37miy50ry114') })
+window.addEventListener('load', function () { styleCell_tinytable_zirzka3ypbzffwutr59k(0, 0, 'tinytable_css_mawxccx6wt4y4ikw4jck') })
+window.addEventListener('load', function () { styleCell_tinytable_zirzka3ypbzffwutr59k(1, 0, 'tinytable_css_mawxccx6wt4y4ikw4jck') })
+window.addEventListener('load', function () { styleCell_tinytable_zirzka3ypbzffwutr59k(0, 1, 'tinytable_css_aqxykdbp3rfbr646m6e5') })
+window.addEventListener('load', function () { styleCell_tinytable_zirzka3ypbzffwutr59k(1, 1, 'tinytable_css_aqxykdbp3rfbr646m6e5') })
     </script>
 </div>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># This table uses the "mean" function as a row and the "mpg" variable as a column:</span></span>
@@ -740,11 +740,11 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_j8sb5pnt2xipeazc7cpo</title>
+<title>tinytable_mgqzua34nrdbo0v0heu9</title>
 <style>
-.table td.tinytable_css_3h0ieclok0iji7vudjxa, .table th.tinytable_css_3h0ieclok0iji7vudjxa {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_8714sqpqdpe508a8ant8, .table th.tinytable_css_8714sqpqdpe508a8ant8 {    text-align: left; }
-.table td.tinytable_css_9w4ttij76a78mhh3z8rz, .table th.tinytable_css_9w4ttij76a78mhh3z8rz {    text-align: right; }
+.table td.tinytable_css_ot6ii8nv6tqk37kglcii, .table th.tinytable_css_ot6ii8nv6tqk37kglcii {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_l0wk5moqrp5mq7y1vqwu, .table th.tinytable_css_l0wk5moqrp5mq7y1vqwu {    text-align: left; }
+.table td.tinytable_css_wp903ep0v2y4vpd1ausb, .table th.tinytable_css_wp903ep0v2y4vpd1ausb {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -756,7 +756,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_j8sb5pnt2xipeazc7cpo" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_mgqzua34nrdbo0v0heu9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mpg</th>
@@ -769,12 +769,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_et6eu08m8i3y53jn2shf(i, j, css_id) {
-        var table = document.getElementById("tinytable_j8sb5pnt2xipeazc7cpo");
+      function styleCell_tinytable_8jclipbphy9zbal64fjg(i, j, css_id) {
+        var table = document.getElementById("tinytable_mgqzua34nrdbo0v0heu9");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_j8sb5pnt2xipeazc7cpo');
+        var table = document.getElementById('tinytable_mgqzua34nrdbo0v0heu9');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -782,8 +782,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_et6eu08m8i3y53jn2shf(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_j8sb5pnt2xipeazc7cpo");
+      function spanCell_tinytable_8jclipbphy9zbal64fjg(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_mgqzua34nrdbo0v0heu9");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -810,12 +810,12 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_et6eu08m8i3y53jn2shf(0, 0, 'tinytable_css_3h0ieclok0iji7vudjxa') })
-window.addEventListener('load', function () { styleCell_tinytable_et6eu08m8i3y53jn2shf(0, 1, 'tinytable_css_3h0ieclok0iji7vudjxa') })
-window.addEventListener('load', function () { styleCell_tinytable_et6eu08m8i3y53jn2shf(0, 0, 'tinytable_css_8714sqpqdpe508a8ant8') })
-window.addEventListener('load', function () { styleCell_tinytable_et6eu08m8i3y53jn2shf(1, 0, 'tinytable_css_8714sqpqdpe508a8ant8') })
-window.addEventListener('load', function () { styleCell_tinytable_et6eu08m8i3y53jn2shf(0, 1, 'tinytable_css_9w4ttij76a78mhh3z8rz') })
-window.addEventListener('load', function () { styleCell_tinytable_et6eu08m8i3y53jn2shf(1, 1, 'tinytable_css_9w4ttij76a78mhh3z8rz') })
+window.addEventListener('load', function () { styleCell_tinytable_8jclipbphy9zbal64fjg(0, 0, 'tinytable_css_ot6ii8nv6tqk37kglcii') })
+window.addEventListener('load', function () { styleCell_tinytable_8jclipbphy9zbal64fjg(0, 1, 'tinytable_css_ot6ii8nv6tqk37kglcii') })
+window.addEventListener('load', function () { styleCell_tinytable_8jclipbphy9zbal64fjg(0, 0, 'tinytable_css_l0wk5moqrp5mq7y1vqwu') })
+window.addEventListener('load', function () { styleCell_tinytable_8jclipbphy9zbal64fjg(1, 0, 'tinytable_css_l0wk5moqrp5mq7y1vqwu') })
+window.addEventListener('load', function () { styleCell_tinytable_8jclipbphy9zbal64fjg(0, 1, 'tinytable_css_wp903ep0v2y4vpd1ausb') })
+window.addEventListener('load', function () { styleCell_tinytable_8jclipbphy9zbal64fjg(1, 1, 'tinytable_css_wp903ep0v2y4vpd1ausb') })
     </script>
 </div>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Display several variables or functions of the data using the "+"</span></span>
@@ -828,12 +828,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_e4h4flnsrkzqgnwdmcno</title>
+<title>tinytable_ydb1f98fs4e1zy5fytpa</title>
 <style>
-.table td.tinytable_css_3h3vw1xvd3f1esvgbeet, .table th.tinytable_css_3h3vw1xvd3f1esvgbeet {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_vpv6gyuwhskwnf7mz90s, .table th.tinytable_css_vpv6gyuwhskwnf7mz90s {    text-align: left; }
-.table td.tinytable_css_vw0qiy5py7ca1kw7q8nt, .table th.tinytable_css_vw0qiy5py7ca1kw7q8nt {    text-align: right; }
-.table td.tinytable_css_0akze8ytfbk2yv5v4l20, .table th.tinytable_css_0akze8ytfbk2yv5v4l20 {    text-align: right; }
+.table td.tinytable_css_n0g20x8sa1ho7deut6sf, .table th.tinytable_css_n0g20x8sa1ho7deut6sf {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_sc3fqbiu9hyaa7qu1pzz, .table th.tinytable_css_sc3fqbiu9hyaa7qu1pzz {    text-align: left; }
+.table td.tinytable_css_avw1ak3f87kl5xprgp61, .table th.tinytable_css_avw1ak3f87kl5xprgp61 {    text-align: right; }
+.table td.tinytable_css_str8y2wo0y2t5cs1rs60, .table th.tinytable_css_str8y2wo0y2t5cs1rs60 {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -845,7 +845,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_e4h4flnsrkzqgnwdmcno" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ydb1f98fs4e1zy5fytpa" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mean</th>
@@ -867,12 +867,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(i, j, css_id) {
-        var table = document.getElementById("tinytable_e4h4flnsrkzqgnwdmcno");
+      function styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(i, j, css_id) {
+        var table = document.getElementById("tinytable_ydb1f98fs4e1zy5fytpa");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_e4h4flnsrkzqgnwdmcno');
+        var table = document.getElementById('tinytable_ydb1f98fs4e1zy5fytpa');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -880,8 +880,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_z7cuqvq7hdxsn6fkygfc(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_e4h4flnsrkzqgnwdmcno");
+      function spanCell_tinytable_dx9v5w2iw4mxrvirmoqn(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ydb1f98fs4e1zy5fytpa");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -908,18 +908,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(0, 0, 'tinytable_css_3h3vw1xvd3f1esvgbeet') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(0, 1, 'tinytable_css_3h3vw1xvd3f1esvgbeet') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(0, 2, 'tinytable_css_3h3vw1xvd3f1esvgbeet') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(0, 0, 'tinytable_css_vpv6gyuwhskwnf7mz90s') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(1, 0, 'tinytable_css_vpv6gyuwhskwnf7mz90s') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(2, 0, 'tinytable_css_vpv6gyuwhskwnf7mz90s') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(0, 1, 'tinytable_css_vw0qiy5py7ca1kw7q8nt') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(1, 1, 'tinytable_css_vw0qiy5py7ca1kw7q8nt') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(2, 1, 'tinytable_css_vw0qiy5py7ca1kw7q8nt') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(0, 2, 'tinytable_css_0akze8ytfbk2yv5v4l20') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(1, 2, 'tinytable_css_0akze8ytfbk2yv5v4l20') })
-window.addEventListener('load', function () { styleCell_tinytable_z7cuqvq7hdxsn6fkygfc(2, 2, 'tinytable_css_0akze8ytfbk2yv5v4l20') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(0, 0, 'tinytable_css_n0g20x8sa1ho7deut6sf') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(0, 1, 'tinytable_css_n0g20x8sa1ho7deut6sf') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(0, 2, 'tinytable_css_n0g20x8sa1ho7deut6sf') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(0, 0, 'tinytable_css_sc3fqbiu9hyaa7qu1pzz') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(1, 0, 'tinytable_css_sc3fqbiu9hyaa7qu1pzz') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(2, 0, 'tinytable_css_sc3fqbiu9hyaa7qu1pzz') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(0, 1, 'tinytable_css_avw1ak3f87kl5xprgp61') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(1, 1, 'tinytable_css_avw1ak3f87kl5xprgp61') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(2, 1, 'tinytable_css_avw1ak3f87kl5xprgp61') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(0, 2, 'tinytable_css_str8y2wo0y2t5cs1rs60') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(1, 2, 'tinytable_css_str8y2wo0y2t5cs1rs60') })
+window.addEventListener('load', function () { styleCell_tinytable_dx9v5w2iw4mxrvirmoqn(2, 2, 'tinytable_css_str8y2wo0y2t5cs1rs60') })
     </script>
 </div>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Nest variables or statistics inside a "factor" variable using the "*" nesting</span></span>
@@ -934,13 +934,13 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_ankn7hgtxhq231dulmho</title>
+<title>tinytable_6ex7dqxtss02tkvvzr3x</title>
 <style>
-.table td.tinytable_css_v0qmk51htng8gb8fiogh, .table th.tinytable_css_v0qmk51htng8gb8fiogh {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_k5v5qksj8r650py3qe4o, .table th.tinytable_css_k5v5qksj8r650py3qe4o {    text-align: left; }
-.table td.tinytable_css_g25uy158vcdsr31ewmyb, .table th.tinytable_css_g25uy158vcdsr31ewmyb {    text-align: right; }
-.table td.tinytable_css_76frtv8q9axdpmgn5dm5, .table th.tinytable_css_76frtv8q9axdpmgn5dm5 {    text-align: right; }
-.table td.tinytable_css_vtbzlxlup101z6xlu7mb, .table th.tinytable_css_vtbzlxlup101z6xlu7mb {    text-align: right; }
+.table td.tinytable_css_5ipope58zeetkb0mbua5, .table th.tinytable_css_5ipope58zeetkb0mbua5 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_0on8ao692dt65rmxz4iw, .table th.tinytable_css_0on8ao692dt65rmxz4iw {    text-align: left; }
+.table td.tinytable_css_ji11wnv2pzn4r4vtjbxg, .table th.tinytable_css_ji11wnv2pzn4r4vtjbxg {    text-align: right; }
+.table td.tinytable_css_3b6qskcxc7x1424r6tbr, .table th.tinytable_css_3b6qskcxc7x1424r6tbr {    text-align: right; }
+.table td.tinytable_css_13vf0rfhvyjh8x3s588j, .table th.tinytable_css_13vf0rfhvyjh8x3s588j {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -952,7 +952,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_ankn7hgtxhq231dulmho" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_6ex7dqxtss02tkvvzr3x" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">4</th>
@@ -977,12 +977,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_i9rto7vm01drsov8yyal(i, j, css_id) {
-        var table = document.getElementById("tinytable_ankn7hgtxhq231dulmho");
+      function styleCell_tinytable_g2xv83gjqf5ce7gw809f(i, j, css_id) {
+        var table = document.getElementById("tinytable_6ex7dqxtss02tkvvzr3x");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_ankn7hgtxhq231dulmho');
+        var table = document.getElementById('tinytable_6ex7dqxtss02tkvvzr3x');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -990,8 +990,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_i9rto7vm01drsov8yyal(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_ankn7hgtxhq231dulmho");
+      function spanCell_tinytable_g2xv83gjqf5ce7gw809f(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_6ex7dqxtss02tkvvzr3x");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1018,22 +1018,22 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 0, 'tinytable_css_v0qmk51htng8gb8fiogh') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 1, 'tinytable_css_v0qmk51htng8gb8fiogh') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 2, 'tinytable_css_v0qmk51htng8gb8fiogh') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 3, 'tinytable_css_v0qmk51htng8gb8fiogh') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 0, 'tinytable_css_k5v5qksj8r650py3qe4o') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(1, 0, 'tinytable_css_k5v5qksj8r650py3qe4o') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(2, 0, 'tinytable_css_k5v5qksj8r650py3qe4o') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 1, 'tinytable_css_g25uy158vcdsr31ewmyb') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(1, 1, 'tinytable_css_g25uy158vcdsr31ewmyb') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(2, 1, 'tinytable_css_g25uy158vcdsr31ewmyb') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 2, 'tinytable_css_76frtv8q9axdpmgn5dm5') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(1, 2, 'tinytable_css_76frtv8q9axdpmgn5dm5') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(2, 2, 'tinytable_css_76frtv8q9axdpmgn5dm5') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(0, 3, 'tinytable_css_vtbzlxlup101z6xlu7mb') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(1, 3, 'tinytable_css_vtbzlxlup101z6xlu7mb') })
-window.addEventListener('load', function () { styleCell_tinytable_i9rto7vm01drsov8yyal(2, 3, 'tinytable_css_vtbzlxlup101z6xlu7mb') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 0, 'tinytable_css_5ipope58zeetkb0mbua5') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 1, 'tinytable_css_5ipope58zeetkb0mbua5') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 2, 'tinytable_css_5ipope58zeetkb0mbua5') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 3, 'tinytable_css_5ipope58zeetkb0mbua5') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 0, 'tinytable_css_0on8ao692dt65rmxz4iw') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(1, 0, 'tinytable_css_0on8ao692dt65rmxz4iw') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(2, 0, 'tinytable_css_0on8ao692dt65rmxz4iw') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 1, 'tinytable_css_ji11wnv2pzn4r4vtjbxg') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(1, 1, 'tinytable_css_ji11wnv2pzn4r4vtjbxg') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(2, 1, 'tinytable_css_ji11wnv2pzn4r4vtjbxg') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 2, 'tinytable_css_3b6qskcxc7x1424r6tbr') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(1, 2, 'tinytable_css_3b6qskcxc7x1424r6tbr') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(2, 2, 'tinytable_css_3b6qskcxc7x1424r6tbr') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(0, 3, 'tinytable_css_13vf0rfhvyjh8x3s588j') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(1, 3, 'tinytable_css_13vf0rfhvyjh8x3s588j') })
+window.addEventListener('load', function () { styleCell_tinytable_g2xv83gjqf5ce7gw809f(2, 3, 'tinytable_css_13vf0rfhvyjh8x3s588j') })
     </script>
 </div>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># If you don't want to convert your original data</span></span>
@@ -1047,13 +1047,13 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_8kup4jsnvz18phctuhyd</title>
+<title>tinytable_l52r23bymea3u35ybhe9</title>
 <style>
-.table td.tinytable_css_cjkw5z3oc0umo20kizd0, .table th.tinytable_css_cjkw5z3oc0umo20kizd0 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_ihbqi21oo1qdlrayw2je, .table th.tinytable_css_ihbqi21oo1qdlrayw2je {    text-align: left; }
-.table td.tinytable_css_6xo3890l4lnoc8039mfa, .table th.tinytable_css_6xo3890l4lnoc8039mfa {    text-align: right; }
-.table td.tinytable_css_62op3r5tnx0yhsy6eom2, .table th.tinytable_css_62op3r5tnx0yhsy6eom2 {    text-align: right; }
-.table td.tinytable_css_kyqdem8qd11s4q2fneav, .table th.tinytable_css_kyqdem8qd11s4q2fneav {    text-align: right; }
+.table td.tinytable_css_zzr56oe7n5zsqicd4pgb, .table th.tinytable_css_zzr56oe7n5zsqicd4pgb {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_9f4hwibvvu9vg1has78x, .table th.tinytable_css_9f4hwibvvu9vg1has78x {    text-align: left; }
+.table td.tinytable_css_mjfcjlon4p04eq657jq8, .table th.tinytable_css_mjfcjlon4p04eq657jq8 {    text-align: right; }
+.table td.tinytable_css_n12woz48fnuo8o1jd4vl, .table th.tinytable_css_n12woz48fnuo8o1jd4vl {    text-align: right; }
+.table td.tinytable_css_wlziuu9ki557oonnf0qt, .table th.tinytable_css_wlziuu9ki557oonnf0qt {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1065,7 +1065,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_8kup4jsnvz18phctuhyd" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_l52r23bymea3u35ybhe9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">4</th>
@@ -1090,12 +1090,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_xd256i670codp9wd6bqj(i, j, css_id) {
-        var table = document.getElementById("tinytable_8kup4jsnvz18phctuhyd");
+      function styleCell_tinytable_zlbxaol73ifi5zke8j49(i, j, css_id) {
+        var table = document.getElementById("tinytable_l52r23bymea3u35ybhe9");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_8kup4jsnvz18phctuhyd');
+        var table = document.getElementById('tinytable_l52r23bymea3u35ybhe9');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1103,8 +1103,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_xd256i670codp9wd6bqj(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_8kup4jsnvz18phctuhyd");
+      function spanCell_tinytable_zlbxaol73ifi5zke8j49(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_l52r23bymea3u35ybhe9");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1131,22 +1131,22 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 0, 'tinytable_css_cjkw5z3oc0umo20kizd0') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 1, 'tinytable_css_cjkw5z3oc0umo20kizd0') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 2, 'tinytable_css_cjkw5z3oc0umo20kizd0') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 3, 'tinytable_css_cjkw5z3oc0umo20kizd0') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 0, 'tinytable_css_ihbqi21oo1qdlrayw2je') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(1, 0, 'tinytable_css_ihbqi21oo1qdlrayw2je') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(2, 0, 'tinytable_css_ihbqi21oo1qdlrayw2je') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 1, 'tinytable_css_6xo3890l4lnoc8039mfa') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(1, 1, 'tinytable_css_6xo3890l4lnoc8039mfa') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(2, 1, 'tinytable_css_6xo3890l4lnoc8039mfa') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 2, 'tinytable_css_62op3r5tnx0yhsy6eom2') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(1, 2, 'tinytable_css_62op3r5tnx0yhsy6eom2') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(2, 2, 'tinytable_css_62op3r5tnx0yhsy6eom2') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(0, 3, 'tinytable_css_kyqdem8qd11s4q2fneav') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(1, 3, 'tinytable_css_kyqdem8qd11s4q2fneav') })
-window.addEventListener('load', function () { styleCell_tinytable_xd256i670codp9wd6bqj(2, 3, 'tinytable_css_kyqdem8qd11s4q2fneav') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 0, 'tinytable_css_zzr56oe7n5zsqicd4pgb') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 1, 'tinytable_css_zzr56oe7n5zsqicd4pgb') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 2, 'tinytable_css_zzr56oe7n5zsqicd4pgb') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 3, 'tinytable_css_zzr56oe7n5zsqicd4pgb') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 0, 'tinytable_css_9f4hwibvvu9vg1has78x') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(1, 0, 'tinytable_css_9f4hwibvvu9vg1has78x') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(2, 0, 'tinytable_css_9f4hwibvvu9vg1has78x') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 1, 'tinytable_css_mjfcjlon4p04eq657jq8') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(1, 1, 'tinytable_css_mjfcjlon4p04eq657jq8') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(2, 1, 'tinytable_css_mjfcjlon4p04eq657jq8') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 2, 'tinytable_css_n12woz48fnuo8o1jd4vl') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(1, 2, 'tinytable_css_n12woz48fnuo8o1jd4vl') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(2, 2, 'tinytable_css_n12woz48fnuo8o1jd4vl') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(0, 3, 'tinytable_css_wlziuu9ki557oonnf0qt') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(1, 3, 'tinytable_css_wlziuu9ki557oonnf0qt') })
+window.addEventListener('load', function () { styleCell_tinytable_zlbxaol73ifi5zke8j49(2, 3, 'tinytable_css_wlziuu9ki557oonnf0qt') })
     </script>
 </div>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># You can nest several variables or statistics inside a factor by using</span></span>
@@ -1160,19 +1160,19 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_gbjrrrssmk19dwv4pbmv</title>
+<title>tinytable_7aa4ffdw2msezxtwh3hg</title>
 <style>
-.table td.tinytable_css_qfn25x4xy8ho4pm8wf2k, .table th.tinytable_css_qfn25x4xy8ho4pm8wf2k {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_gncqyxb8qdk1jscynoom, .table th.tinytable_css_gncqyxb8qdk1jscynoom {    text-align: center; }
-.table td.tinytable_css_xxder4dyh696g6x5dpx3, .table th.tinytable_css_xxder4dyh696g6x5dpx3 {    border-bottom: solid 0.05em #d3d8dc; }
-.table td.tinytable_css_gy407fx73yk2d2nt403f, .table th.tinytable_css_gy407fx73yk2d2nt403f {    text-align: left; }
-.table td.tinytable_css_jmy76ko3n76yyo84crzd, .table th.tinytable_css_jmy76ko3n76yyo84crzd {    text-align: right; }
-.table td.tinytable_css_rox52yc4qej1nkv5hg12, .table th.tinytable_css_rox52yc4qej1nkv5hg12 {    text-align: right; }
-.table td.tinytable_css_hbb34zv4n51idaqi2aed, .table th.tinytable_css_hbb34zv4n51idaqi2aed {    text-align: right; }
-.table td.tinytable_css_i9b0jgz7tomzpyaqr8uh, .table th.tinytable_css_i9b0jgz7tomzpyaqr8uh {    text-align: right; }
-.table td.tinytable_css_ybw46qd9yqffkuiycqkz, .table th.tinytable_css_ybw46qd9yqffkuiycqkz {    text-align: right; }
-.table td.tinytable_css_qc8pzzlvprq5y7rqan95, .table th.tinytable_css_qc8pzzlvprq5y7rqan95 {    text-align: right; }
-.table td.tinytable_css_tbwts7j7wpj73lswc6b6, .table th.tinytable_css_tbwts7j7wpj73lswc6b6 {    text-align: center; }
+.table td.tinytable_css_cx9g915e2kcmoekgglxc, .table th.tinytable_css_cx9g915e2kcmoekgglxc {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_izfo31zmhsc670s0k5l0, .table th.tinytable_css_izfo31zmhsc670s0k5l0 {    text-align: center; }
+.table td.tinytable_css_0o2mf35bp62vhbmsf24u, .table th.tinytable_css_0o2mf35bp62vhbmsf24u {    border-bottom: solid 0.05em #d3d8dc; }
+.table td.tinytable_css_q6klsxyr8vna4aapazx3, .table th.tinytable_css_q6klsxyr8vna4aapazx3 {    text-align: left; }
+.table td.tinytable_css_90louibxf05hjva3py63, .table th.tinytable_css_90louibxf05hjva3py63 {    text-align: right; }
+.table td.tinytable_css_5n6955a1tk7xvtxgfcas, .table th.tinytable_css_5n6955a1tk7xvtxgfcas {    text-align: right; }
+.table td.tinytable_css_ukykp8e8bmcrwgf8g7nq, .table th.tinytable_css_ukykp8e8bmcrwgf8g7nq {    text-align: right; }
+.table td.tinytable_css_ib6ucjsdqd0uoww54ybq, .table th.tinytable_css_ib6ucjsdqd0uoww54ybq {    text-align: right; }
+.table td.tinytable_css_irhx0ialo2r81g1u5rvh, .table th.tinytable_css_irhx0ialo2r81g1u5rvh {    text-align: right; }
+.table td.tinytable_css_q2wq1omzhwymhh75sh2w, .table th.tinytable_css_q2wq1omzhwymhh75sh2w {    text-align: right; }
+.table td.tinytable_css_nuhbnkuffmny5iwgwt9h, .table th.tinytable_css_nuhbnkuffmny5iwgwt9h {    text-align: center; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1184,7 +1184,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_gbjrrrssmk19dwv4pbmv" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_7aa4ffdw2msezxtwh3hg" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -1226,12 +1226,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_9w7wrug377jfmih103it(i, j, css_id) {
-        var table = document.getElementById("tinytable_gbjrrrssmk19dwv4pbmv");
+      function styleCell_tinytable_4waddbmhi2bv0uasjaa2(i, j, css_id) {
+        var table = document.getElementById("tinytable_7aa4ffdw2msezxtwh3hg");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_gbjrrrssmk19dwv4pbmv');
+        var table = document.getElementById('tinytable_7aa4ffdw2msezxtwh3hg');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1239,8 +1239,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_9w7wrug377jfmih103it(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_gbjrrrssmk19dwv4pbmv");
+      function spanCell_tinytable_4waddbmhi2bv0uasjaa2(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_7aa4ffdw2msezxtwh3hg");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1267,58 +1267,58 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(1, 0, 'tinytable_css_qfn25x4xy8ho4pm8wf2k') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(1, 1, 'tinytable_css_qfn25x4xy8ho4pm8wf2k') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(1, 2, 'tinytable_css_qfn25x4xy8ho4pm8wf2k') })
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-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(1, 4, 'tinytable_css_qfn25x4xy8ho4pm8wf2k') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(1, 5, 'tinytable_css_qfn25x4xy8ho4pm8wf2k') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(1, 6, 'tinytable_css_qfn25x4xy8ho4pm8wf2k') })
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-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 1, 'tinytable_css_gncqyxb8qdk1jscynoom') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 2, 'tinytable_css_gncqyxb8qdk1jscynoom') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 3, 'tinytable_css_gncqyxb8qdk1jscynoom') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 4, 'tinytable_css_gncqyxb8qdk1jscynoom') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 5, 'tinytable_css_gncqyxb8qdk1jscynoom') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 6, 'tinytable_css_gncqyxb8qdk1jscynoom') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 1, 'tinytable_css_xxder4dyh696g6x5dpx3') })
-window.addEventListener('load', function () { styleCell_tinytable_9w7wrug377jfmih103it(0, 2, 'tinytable_css_xxder4dyh696g6x5dpx3') })
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     </script>
 </div>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Summarize all numeric variables with 'All()'</span></span>
@@ -1329,12 +1329,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_aal6h2bvlc135pru3kc3</title>
+<title>tinytable_pawkzn9g4hyhhyucmnml</title>
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     MathJax = {
@@ -1346,7 +1346,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_aal6h2bvlc135pru3kc3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_pawkzn9g4hyhhyucmnml" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mean</th>
@@ -1408,12 +1408,12 @@ <h4 class="anchored">
 </div>
 
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-        var table = document.getElementById("tinytable_aal6h2bvlc135pru3kc3");
+      function styleCell_tinytable_jilyaeydpb7v03wtnwkl(i, j, css_id) {
+        var table = document.getElementById("tinytable_pawkzn9g4hyhhyucmnml");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_aal6h2bvlc135pru3kc3');
+        var table = document.getElementById('tinytable_pawkzn9g4hyhhyucmnml');
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-      function spanCell_tinytable_1xjn1r2amxld01thft0x(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_aal6h2bvlc135pru3kc3");
+      function spanCell_tinytable_jilyaeydpb7v03wtnwkl(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_pawkzn9g4hyhhyucmnml");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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@@ -1449,42 +1449,42 @@ <h4 class="anchored">
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+window.addEventListener('load', function () { styleCell_tinytable_jilyaeydpb7v03wtnwkl(9, 2, 'tinytable_css_kwsqjkh0lkjq9igrdhwg') })
+window.addEventListener('load', function () { styleCell_tinytable_jilyaeydpb7v03wtnwkl(10, 2, 'tinytable_css_kwsqjkh0lkjq9igrdhwg') })
     </script>
 </div>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Define custom summary statistics. Your custom function should accept a vector</span></span>
@@ -1500,12 +1500,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_6zdkylcqp5ja4snmk9n2</title>
+<title>tinytable_4d9z84cto2ud51troo8a</title>
 <style>
-.table td.tinytable_css_lkdeg76i7ggzwd0u02ym, .table th.tinytable_css_lkdeg76i7ggzwd0u02ym {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_m47vmzhpm06i8if96ad3, .table th.tinytable_css_m47vmzhpm06i8if96ad3 {    text-align: left; }
-.table td.tinytable_css_c57vb6k7wi1pkur7z009, .table th.tinytable_css_c57vb6k7wi1pkur7z009 {    text-align: right; }
-.table td.tinytable_css_gtb6qmkg6ad1pprks4d0, .table th.tinytable_css_gtb6qmkg6ad1pprks4d0 {    text-align: right; }
+.table td.tinytable_css_r0r7ct6y2gowqfbtm4d3, .table th.tinytable_css_r0r7ct6y2gowqfbtm4d3 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_k8q3zm5giri4j1o7gl52, .table th.tinytable_css_k8q3zm5giri4j1o7gl52 {    text-align: left; }
+.table td.tinytable_css_0xxmwfvgtjigx282k4pp, .table th.tinytable_css_0xxmwfvgtjigx282k4pp {    text-align: right; }
+.table td.tinytable_css_m5u39kj559ow9oq7cqx1, .table th.tinytable_css_m5u39kj559ow9oq7cqx1 {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1517,7 +1517,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_6zdkylcqp5ja4snmk9n2" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_4d9z84cto2ud51troo8a" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">minmax</th>
@@ -1539,12 +1539,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_fd20t9a4jkdz1qbx4kqp(i, j, css_id) {
-        var table = document.getElementById("tinytable_6zdkylcqp5ja4snmk9n2");
+      function styleCell_tinytable_3yuzrdqouxzni3nwaub6(i, j, css_id) {
+        var table = document.getElementById("tinytable_4d9z84cto2ud51troo8a");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_6zdkylcqp5ja4snmk9n2');
+        var table = document.getElementById('tinytable_4d9z84cto2ud51troo8a');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1552,8 +1552,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_fd20t9a4jkdz1qbx4kqp(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_6zdkylcqp5ja4snmk9n2");
+      function spanCell_tinytable_3yuzrdqouxzni3nwaub6(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_4d9z84cto2ud51troo8a");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1580,18 +1580,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_fd20t9a4jkdz1qbx4kqp(0, 0, 'tinytable_css_lkdeg76i7ggzwd0u02ym') })
-window.addEventListener('load', function () { styleCell_tinytable_fd20t9a4jkdz1qbx4kqp(0, 1, 'tinytable_css_lkdeg76i7ggzwd0u02ym') })
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-window.addEventListener('load', function () { styleCell_tinytable_fd20t9a4jkdz1qbx4kqp(2, 2, 'tinytable_css_gtb6qmkg6ad1pprks4d0') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(0, 0, 'tinytable_css_r0r7ct6y2gowqfbtm4d3') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(0, 1, 'tinytable_css_r0r7ct6y2gowqfbtm4d3') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(0, 2, 'tinytable_css_r0r7ct6y2gowqfbtm4d3') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(0, 0, 'tinytable_css_k8q3zm5giri4j1o7gl52') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(1, 0, 'tinytable_css_k8q3zm5giri4j1o7gl52') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(2, 0, 'tinytable_css_k8q3zm5giri4j1o7gl52') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(0, 1, 'tinytable_css_0xxmwfvgtjigx282k4pp') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(1, 1, 'tinytable_css_0xxmwfvgtjigx282k4pp') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(2, 1, 'tinytable_css_0xxmwfvgtjigx282k4pp') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(0, 2, 'tinytable_css_m5u39kj559ow9oq7cqx1') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(1, 2, 'tinytable_css_m5u39kj559ow9oq7cqx1') })
+window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(2, 2, 'tinytable_css_m5u39kj559ow9oq7cqx1') })
     </script>
 </div>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># To handle missing values, you can pass arguments to your functions using</span></span>
@@ -1604,11 +1604,11 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_8pcxqgd6ff4zz0w5j404</title>
+<title>tinytable_m2i8k8eiy3qx5in92i21</title>
 <style>
-.table td.tinytable_css_236typ4scwnaskgstmtx, .table th.tinytable_css_236typ4scwnaskgstmtx {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_7e4sdg6f3232eqjeq0kv, .table th.tinytable_css_7e4sdg6f3232eqjeq0kv {    text-align: left; }
-.table td.tinytable_css_eoj8v481ctcq2zil43ss, .table th.tinytable_css_eoj8v481ctcq2zil43ss {    text-align: right; }
+.table td.tinytable_css_efi5g664bgwpamm9csum, .table th.tinytable_css_efi5g664bgwpamm9csum {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_5x6qwb8s17wigd4i99v6, .table th.tinytable_css_5x6qwb8s17wigd4i99v6 {    text-align: left; }
+.table td.tinytable_css_agvhqqkw04ezubfxh1xz, .table th.tinytable_css_agvhqqkw04ezubfxh1xz {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1620,7 +1620,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_8pcxqgd6ff4zz0w5j404" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_m2i8k8eiy3qx5in92i21" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mean</th>
@@ -1639,12 +1639,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_ve1cclrhlmmbx711v83w(i, j, css_id) {
-        var table = document.getElementById("tinytable_8pcxqgd6ff4zz0w5j404");
+      function styleCell_tinytable_pa5tzaza71aoiy212634(i, j, css_id) {
+        var table = document.getElementById("tinytable_m2i8k8eiy3qx5in92i21");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_8pcxqgd6ff4zz0w5j404');
+        var table = document.getElementById('tinytable_m2i8k8eiy3qx5in92i21');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1652,8 +1652,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_ve1cclrhlmmbx711v83w(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_8pcxqgd6ff4zz0w5j404");
+      function spanCell_tinytable_pa5tzaza71aoiy212634(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_m2i8k8eiy3qx5in92i21");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1680,14 +1680,14 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(0, 0, 'tinytable_css_236typ4scwnaskgstmtx') })
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(0, 1, 'tinytable_css_236typ4scwnaskgstmtx') })
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(0, 0, 'tinytable_css_7e4sdg6f3232eqjeq0kv') })
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(1, 0, 'tinytable_css_7e4sdg6f3232eqjeq0kv') })
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(2, 0, 'tinytable_css_7e4sdg6f3232eqjeq0kv') })
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(0, 1, 'tinytable_css_eoj8v481ctcq2zil43ss') })
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(1, 1, 'tinytable_css_eoj8v481ctcq2zil43ss') })
-window.addEventListener('load', function () { styleCell_tinytable_ve1cclrhlmmbx711v83w(2, 1, 'tinytable_css_eoj8v481ctcq2zil43ss') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(0, 0, 'tinytable_css_efi5g664bgwpamm9csum') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(0, 1, 'tinytable_css_efi5g664bgwpamm9csum') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(0, 0, 'tinytable_css_5x6qwb8s17wigd4i99v6') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(1, 0, 'tinytable_css_5x6qwb8s17wigd4i99v6') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(2, 0, 'tinytable_css_5x6qwb8s17wigd4i99v6') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(0, 1, 'tinytable_css_agvhqqkw04ezubfxh1xz') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(1, 1, 'tinytable_css_agvhqqkw04ezubfxh1xz') })
+window.addEventListener('load', function () { styleCell_tinytable_pa5tzaza71aoiy212634(2, 1, 'tinytable_css_agvhqqkw04ezubfxh1xz') })
     </script>
 </div>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># For convenience, 'modelsummary' supplies several convenience functions</span></span>
@@ -1706,12 +1706,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_q0zh8t7fojct4x3zrvat</title>
+<title>tinytable_e7pur5j2cizht4r7vfbd</title>
 <style>
-.table td.tinytable_css_35tzedmciqqbftl9g6tp, .table th.tinytable_css_35tzedmciqqbftl9g6tp {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_ozwtt6qreggg5c22uzjz, .table th.tinytable_css_ozwtt6qreggg5c22uzjz {    text-align: left; }
-.table td.tinytable_css_qg9qqqycqwt44issjr58, .table th.tinytable_css_qg9qqqycqwt44issjr58 {    text-align: right; }
-.table td.tinytable_css_ewkehug02pjrhqh3zhi9, .table th.tinytable_css_ewkehug02pjrhqh3zhi9 {    text-align: right; }
+.table td.tinytable_css_mszuwmlqowkidjlmxs21, .table th.tinytable_css_mszuwmlqowkidjlmxs21 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_8g61maggm5k1qrjzx8un, .table th.tinytable_css_8g61maggm5k1qrjzx8un {    text-align: left; }
+.table td.tinytable_css_z5oy8yl54amiqs28lm75, .table th.tinytable_css_z5oy8yl54amiqs28lm75 {    text-align: right; }
+.table td.tinytable_css_ojf2mak0xq8dg2djfqph, .table th.tinytable_css_ojf2mak0xq8dg2djfqph {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1723,7 +1723,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_q0zh8t7fojct4x3zrvat" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_e7pur5j2cizht4r7vfbd" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Mean</th>
@@ -1745,12 +1745,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_qjlonu47jy4excmg9tio(i, j, css_id) {
-        var table = document.getElementById("tinytable_q0zh8t7fojct4x3zrvat");
+      function styleCell_tinytable_hsrqont3sjmhtnvr95ej(i, j, css_id) {
+        var table = document.getElementById("tinytable_e7pur5j2cizht4r7vfbd");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_q0zh8t7fojct4x3zrvat');
+        var table = document.getElementById('tinytable_e7pur5j2cizht4r7vfbd');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1758,8 +1758,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_qjlonu47jy4excmg9tio(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_q0zh8t7fojct4x3zrvat");
+      function spanCell_tinytable_hsrqont3sjmhtnvr95ej(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_e7pur5j2cizht4r7vfbd");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1786,18 +1786,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
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-window.addEventListener('load', function () { styleCell_tinytable_qjlonu47jy4excmg9tio(1, 2, 'tinytable_css_ewkehug02pjrhqh3zhi9') })
-window.addEventListener('load', function () { styleCell_tinytable_qjlonu47jy4excmg9tio(2, 2, 'tinytable_css_ewkehug02pjrhqh3zhi9') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(0, 0, 'tinytable_css_mszuwmlqowkidjlmxs21') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(0, 1, 'tinytable_css_mszuwmlqowkidjlmxs21') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(0, 2, 'tinytable_css_mszuwmlqowkidjlmxs21') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(0, 0, 'tinytable_css_8g61maggm5k1qrjzx8un') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(1, 0, 'tinytable_css_8g61maggm5k1qrjzx8un') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(2, 0, 'tinytable_css_8g61maggm5k1qrjzx8un') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(0, 1, 'tinytable_css_z5oy8yl54amiqs28lm75') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(1, 1, 'tinytable_css_z5oy8yl54amiqs28lm75') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(2, 1, 'tinytable_css_z5oy8yl54amiqs28lm75') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(0, 2, 'tinytable_css_ojf2mak0xq8dg2djfqph') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(1, 2, 'tinytable_css_ojf2mak0xq8dg2djfqph') })
+window.addEventListener('load', function () { styleCell_tinytable_hsrqont3sjmhtnvr95ej(2, 2, 'tinytable_css_ojf2mak0xq8dg2djfqph') })
     </script>
 </div>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Save your tables to a variety of output formats:</span></span>
@@ -1832,12 +1832,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_gnaisdxehhhaacrh1vzq</title>
+<title>tinytable_vzhxuj0m159outzpq0j8</title>
 <style>
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-.table td.tinytable_css_2s15i84paxjir6yhd08l, .table th.tinytable_css_2s15i84paxjir6yhd08l {    text-align: left; }
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-.table td.tinytable_css_blavvqibciuwwj3zq785, .table th.tinytable_css_blavvqibciuwwj3zq785 {    text-align: right; }
+.table td.tinytable_css_4rhdptu6sw886vkncq32, .table th.tinytable_css_4rhdptu6sw886vkncq32 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_0lutz9i3epwrtgvt4q8u, .table th.tinytable_css_0lutz9i3epwrtgvt4q8u {    text-align: left; }
+.table td.tinytable_css_bz2h6ip8uce8ityiwedm, .table th.tinytable_css_bz2h6ip8uce8ityiwedm {    text-align: right; }
+.table td.tinytable_css_xokmbe3plkenm68ucb4f, .table th.tinytable_css_xokmbe3plkenm68ucb4f {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1849,7 +1849,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_gnaisdxehhhaacrh1vzq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_vzhxuj0m159outzpq0j8" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mean</th>
@@ -1881,12 +1881,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_b97gy397ry5oqqdz9v8t(i, j, css_id) {
-        var table = document.getElementById("tinytable_gnaisdxehhhaacrh1vzq");
+      function styleCell_tinytable_whwnk72vrx8bkumfe0fu(i, j, css_id) {
+        var table = document.getElementById("tinytable_vzhxuj0m159outzpq0j8");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_gnaisdxehhhaacrh1vzq');
+        var table = document.getElementById('tinytable_vzhxuj0m159outzpq0j8');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1894,8 +1894,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_b97gy397ry5oqqdz9v8t(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_gnaisdxehhhaacrh1vzq");
+      function spanCell_tinytable_whwnk72vrx8bkumfe0fu(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_vzhxuj0m159outzpq0j8");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1922,24 +1922,24 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
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-window.addEventListener('load', function () { styleCell_tinytable_b97gy397ry5oqqdz9v8t(4, 2, 'tinytable_css_blavvqibciuwwj3zq785') })
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+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(0, 1, 'tinytable_css_4rhdptu6sw886vkncq32') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(0, 2, 'tinytable_css_4rhdptu6sw886vkncq32') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(0, 0, 'tinytable_css_0lutz9i3epwrtgvt4q8u') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(1, 0, 'tinytable_css_0lutz9i3epwrtgvt4q8u') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(2, 0, 'tinytable_css_0lutz9i3epwrtgvt4q8u') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(3, 0, 'tinytable_css_0lutz9i3epwrtgvt4q8u') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(4, 0, 'tinytable_css_0lutz9i3epwrtgvt4q8u') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(0, 1, 'tinytable_css_bz2h6ip8uce8ityiwedm') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(1, 1, 'tinytable_css_bz2h6ip8uce8ityiwedm') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(2, 1, 'tinytable_css_bz2h6ip8uce8ityiwedm') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(3, 1, 'tinytable_css_bz2h6ip8uce8ityiwedm') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(4, 1, 'tinytable_css_bz2h6ip8uce8ityiwedm') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(0, 2, 'tinytable_css_xokmbe3plkenm68ucb4f') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(1, 2, 'tinytable_css_xokmbe3plkenm68ucb4f') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(2, 2, 'tinytable_css_xokmbe3plkenm68ucb4f') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(3, 2, 'tinytable_css_xokmbe3plkenm68ucb4f') })
+window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(4, 2, 'tinytable_css_xokmbe3plkenm68ucb4f') })
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diff --git a/man/datasummary_balance.html b/man/datasummary_balance.html
index 4a9a963a..8f504e63 100644
--- a/man/datasummary_balance.html
+++ b/man/datasummary_balance.html
@@ -683,19 +683,19 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+.table td.tinytable_css_1hx9sqnhiq6y4fxp4bt3, .table th.tinytable_css_1hx9sqnhiq6y4fxp4bt3 {    text-align: center; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -707,7 +707,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_91ln5q0l4ywxpxokynmo" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ulh2ykegwv72f064kn1v" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -822,12 +822,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_1ka1dzs905uz0x87ojn4(i, j, css_id) {
-        var table = document.getElementById("tinytable_91ln5q0l4ywxpxokynmo");
+      function styleCell_tinytable_lop34tmk1pvojfablen0(i, j, css_id) {
+        var table = document.getElementById("tinytable_ulh2ykegwv72f064kn1v");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_91ln5q0l4ywxpxokynmo');
+        var table = document.getElementById('tinytable_ulh2ykegwv72f064kn1v');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -835,8 +835,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_1ka1dzs905uz0x87ojn4(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_91ln5q0l4ywxpxokynmo");
+      function spanCell_tinytable_lop34tmk1pvojfablen0(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ulh2ykegwv72f064kn1v");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -863,113 +863,113 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
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diff --git a/man/datasummary_correlation.html b/man/datasummary_correlation.html
index 4d5fff59..b330e314 100644
--- a/man/datasummary_correlation.html
+++ b/man/datasummary_correlation.html
@@ -649,12 +649,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_uxumo5cevz6yt8nzjbqo</title>
+<title>tinytable_grzw8xi4ix63dmd78n0p</title>
 <style>
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+.table td.tinytable_css_udz6a37hg1bn3c3b63se, .table th.tinytable_css_udz6a37hg1bn3c3b63se {    text-align: left; }
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     </style>
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@@ -666,7 +666,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
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+      <table class="table table-borderless" id="tinytable_grzw8xi4ix63dmd78n0p" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Miles / Gallon</th>
@@ -688,12 +688,12 @@ <h4 class="anchored">
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     <script>
-      function styleCell_tinytable_s23zofy2vj320g9tkepk(i, j, css_id) {
-        var table = document.getElementById("tinytable_uxumo5cevz6yt8nzjbqo");
+      function styleCell_tinytable_oljnyb6jbcz5i3vf17ut(i, j, css_id) {
+        var table = document.getElementById("tinytable_grzw8xi4ix63dmd78n0p");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_uxumo5cevz6yt8nzjbqo');
+        var table = document.getElementById('tinytable_grzw8xi4ix63dmd78n0p');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -701,8 +701,8 @@ <h4 class="anchored">
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         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_s23zofy2vj320g9tkepk(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_uxumo5cevz6yt8nzjbqo");
+      function spanCell_tinytable_oljnyb6jbcz5i3vf17ut(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_grzw8xi4ix63dmd78n0p");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -729,18 +729,18 @@ <h4 class="anchored">
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+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(0, 0, 'tinytable_css_udz6a37hg1bn3c3b63se') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(1, 0, 'tinytable_css_udz6a37hg1bn3c3b63se') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(2, 0, 'tinytable_css_udz6a37hg1bn3c3b63se') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(0, 1, 'tinytable_css_qe3s1m6fv7dam7f2vmla') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(1, 1, 'tinytable_css_qe3s1m6fv7dam7f2vmla') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(2, 1, 'tinytable_css_qe3s1m6fv7dam7f2vmla') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(0, 2, 'tinytable_css_lwi5vt6a0qpi9fickwnq') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(1, 2, 'tinytable_css_lwi5vt6a0qpi9fickwnq') })
+window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(2, 2, 'tinytable_css_lwi5vt6a0qpi9fickwnq') })
     </script>
 </div>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># clean variable names (tidyverse)</span></span>
@@ -755,12 +755,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_0lq7vw69y2dma1zk5jm1</title>
+<title>tinytable_5ox0wtvehi0xjlxiovka</title>
 <style>
-.table td.tinytable_css_evx5cnrtud43d6j4sav2, .table th.tinytable_css_evx5cnrtud43d6j4sav2 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_i11i4ghkfqtvlp3v4pik, .table th.tinytable_css_i11i4ghkfqtvlp3v4pik {    text-align: left; }
-.table td.tinytable_css_76sgkkbizu1p83u73qp4, .table th.tinytable_css_76sgkkbizu1p83u73qp4 {    text-align: right; }
-.table td.tinytable_css_xkd0sahm2ns0epfzsrtl, .table th.tinytable_css_xkd0sahm2ns0epfzsrtl {    text-align: right; }
+.table td.tinytable_css_eip4v9e9q9a2yedh0wmd, .table th.tinytable_css_eip4v9e9q9a2yedh0wmd {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_pitbl1dpdr79jp3nokm7, .table th.tinytable_css_pitbl1dpdr79jp3nokm7 {    text-align: left; }
+.table td.tinytable_css_gms3zeu9bm8sj695k0j2, .table th.tinytable_css_gms3zeu9bm8sj695k0j2 {    text-align: right; }
+.table td.tinytable_css_uo9laj6hhg54d2u01fv4, .table th.tinytable_css_uo9laj6hhg54d2u01fv4 {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -772,7 +772,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_0lq7vw69y2dma1zk5jm1" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_5ox0wtvehi0xjlxiovka" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Miles / Gallon</th>
@@ -794,12 +794,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_ninasxsp4fp9tex1aet2(i, j, css_id) {
-        var table = document.getElementById("tinytable_0lq7vw69y2dma1zk5jm1");
+      function styleCell_tinytable_c3w4ikxy2qliuhuhiiib(i, j, css_id) {
+        var table = document.getElementById("tinytable_5ox0wtvehi0xjlxiovka");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_0lq7vw69y2dma1zk5jm1');
+        var table = document.getElementById('tinytable_5ox0wtvehi0xjlxiovka');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -807,8 +807,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_ninasxsp4fp9tex1aet2(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_0lq7vw69y2dma1zk5jm1");
+      function spanCell_tinytable_c3w4ikxy2qliuhuhiiib(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_5ox0wtvehi0xjlxiovka");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -835,18 +835,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(0, 0, 'tinytable_css_evx5cnrtud43d6j4sav2') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(0, 1, 'tinytable_css_evx5cnrtud43d6j4sav2') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(0, 2, 'tinytable_css_evx5cnrtud43d6j4sav2') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(0, 0, 'tinytable_css_i11i4ghkfqtvlp3v4pik') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(1, 0, 'tinytable_css_i11i4ghkfqtvlp3v4pik') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(2, 0, 'tinytable_css_i11i4ghkfqtvlp3v4pik') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(0, 1, 'tinytable_css_76sgkkbizu1p83u73qp4') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(1, 1, 'tinytable_css_76sgkkbizu1p83u73qp4') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(2, 1, 'tinytable_css_76sgkkbizu1p83u73qp4') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(0, 2, 'tinytable_css_xkd0sahm2ns0epfzsrtl') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(1, 2, 'tinytable_css_xkd0sahm2ns0epfzsrtl') })
-window.addEventListener('load', function () { styleCell_tinytable_ninasxsp4fp9tex1aet2(2, 2, 'tinytable_css_xkd0sahm2ns0epfzsrtl') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(0, 0, 'tinytable_css_eip4v9e9q9a2yedh0wmd') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(0, 1, 'tinytable_css_eip4v9e9q9a2yedh0wmd') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(0, 2, 'tinytable_css_eip4v9e9q9a2yedh0wmd') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(0, 0, 'tinytable_css_pitbl1dpdr79jp3nokm7') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(1, 0, 'tinytable_css_pitbl1dpdr79jp3nokm7') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(2, 0, 'tinytable_css_pitbl1dpdr79jp3nokm7') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(0, 1, 'tinytable_css_gms3zeu9bm8sj695k0j2') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(1, 1, 'tinytable_css_gms3zeu9bm8sj695k0j2') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(2, 1, 'tinytable_css_gms3zeu9bm8sj695k0j2') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(0, 2, 'tinytable_css_uo9laj6hhg54d2u01fv4') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(1, 2, 'tinytable_css_uo9laj6hhg54d2u01fv4') })
+window.addEventListener('load', function () { styleCell_tinytable_c3w4ikxy2qliuhuhiiib(2, 2, 'tinytable_css_uo9laj6hhg54d2u01fv4') })
     </script>
 </div>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># alternative methods</span></span>
@@ -857,12 +857,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_m8s2wmb2x469c6omtj5w</title>
+<title>tinytable_xqd02z3d30gyj47o4kgz</title>
 <style>
-.table td.tinytable_css_t1od3ssr3ghgsmjju65f, .table th.tinytable_css_t1od3ssr3ghgsmjju65f {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_1mh1m349beadr11xq9v8, .table th.tinytable_css_1mh1m349beadr11xq9v8 {    text-align: left; }
-.table td.tinytable_css_8fiu6gyd50z02qiny7h9, .table th.tinytable_css_8fiu6gyd50z02qiny7h9 {    text-align: right; }
-.table td.tinytable_css_4772z4kapkkdk7j3078c, .table th.tinytable_css_4772z4kapkkdk7j3078c {    text-align: right; }
+.table td.tinytable_css_g5dfgc1mfxviaazdsmre, .table th.tinytable_css_g5dfgc1mfxviaazdsmre {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_euwln17my2r2d6jxwjrw, .table th.tinytable_css_euwln17my2r2d6jxwjrw {    text-align: left; }
+.table td.tinytable_css_7wq5bsbtbptpcbkgko3e, .table th.tinytable_css_7wq5bsbtbptpcbkgko3e {    text-align: right; }
+.table td.tinytable_css_wrm9xp02kg5spcrqg5m7, .table th.tinytable_css_wrm9xp02kg5spcrqg5m7 {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -874,7 +874,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_m8s2wmb2x469c6omtj5w" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_xqd02z3d30gyj47o4kgz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Miles / Gallon</th>
@@ -896,12 +896,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(i, j, css_id) {
-        var table = document.getElementById("tinytable_m8s2wmb2x469c6omtj5w");
+      function styleCell_tinytable_z5dz1c4ib2hfpuootvx4(i, j, css_id) {
+        var table = document.getElementById("tinytable_xqd02z3d30gyj47o4kgz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_m8s2wmb2x469c6omtj5w');
+        var table = document.getElementById('tinytable_xqd02z3d30gyj47o4kgz');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -909,8 +909,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_3ez06m0jcw0g5qu4k3qa(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_m8s2wmb2x469c6omtj5w");
+      function spanCell_tinytable_z5dz1c4ib2hfpuootvx4(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_xqd02z3d30gyj47o4kgz");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -937,18 +937,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(0, 0, 'tinytable_css_t1od3ssr3ghgsmjju65f') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(0, 1, 'tinytable_css_t1od3ssr3ghgsmjju65f') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(0, 2, 'tinytable_css_t1od3ssr3ghgsmjju65f') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(0, 0, 'tinytable_css_1mh1m349beadr11xq9v8') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(1, 0, 'tinytable_css_1mh1m349beadr11xq9v8') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(2, 0, 'tinytable_css_1mh1m349beadr11xq9v8') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(0, 1, 'tinytable_css_8fiu6gyd50z02qiny7h9') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(1, 1, 'tinytable_css_8fiu6gyd50z02qiny7h9') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(2, 1, 'tinytable_css_8fiu6gyd50z02qiny7h9') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(0, 2, 'tinytable_css_4772z4kapkkdk7j3078c') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(1, 2, 'tinytable_css_4772z4kapkkdk7j3078c') })
-window.addEventListener('load', function () { styleCell_tinytable_3ez06m0jcw0g5qu4k3qa(2, 2, 'tinytable_css_4772z4kapkkdk7j3078c') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(0, 0, 'tinytable_css_g5dfgc1mfxviaazdsmre') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(0, 1, 'tinytable_css_g5dfgc1mfxviaazdsmre') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(0, 2, 'tinytable_css_g5dfgc1mfxviaazdsmre') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(0, 0, 'tinytable_css_euwln17my2r2d6jxwjrw') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(1, 0, 'tinytable_css_euwln17my2r2d6jxwjrw') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(2, 0, 'tinytable_css_euwln17my2r2d6jxwjrw') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(0, 1, 'tinytable_css_7wq5bsbtbptpcbkgko3e') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(1, 1, 'tinytable_css_7wq5bsbtbptpcbkgko3e') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(2, 1, 'tinytable_css_7wq5bsbtbptpcbkgko3e') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(0, 2, 'tinytable_css_wrm9xp02kg5spcrqg5m7') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(1, 2, 'tinytable_css_wrm9xp02kg5spcrqg5m7') })
+window.addEventListener('load', function () { styleCell_tinytable_z5dz1c4ib2hfpuootvx4(2, 2, 'tinytable_css_wrm9xp02kg5spcrqg5m7') })
     </script>
 </div>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># custom function</span></span>
@@ -960,12 +960,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_p4tpm2o5kg4lqqcba3gf</title>
+<title>tinytable_2olu6wk3h3qqthxs8g0z</title>
 <style>
-.table td.tinytable_css_4nxvgosth46eg9mgmj1z, .table th.tinytable_css_4nxvgosth46eg9mgmj1z {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_523d3zbiauhvp3w5aw87, .table th.tinytable_css_523d3zbiauhvp3w5aw87 {    text-align: left; }
-.table td.tinytable_css_r31uzqpmcmgimfygofd8, .table th.tinytable_css_r31uzqpmcmgimfygofd8 {    text-align: right; }
-.table td.tinytable_css_2sh8cbf60iw5w5ef8f1m, .table th.tinytable_css_2sh8cbf60iw5w5ef8f1m {    text-align: right; }
+.table td.tinytable_css_cntctshukqz6kzmyja4b, .table th.tinytable_css_cntctshukqz6kzmyja4b {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_f7qrtmmynrzoyz66m8nh, .table th.tinytable_css_f7qrtmmynrzoyz66m8nh {    text-align: left; }
+.table td.tinytable_css_pj44atcwo4xsmaehxkdk, .table th.tinytable_css_pj44atcwo4xsmaehxkdk {    text-align: right; }
+.table td.tinytable_css_lpda02jahh2c8ptk1jou, .table th.tinytable_css_lpda02jahh2c8ptk1jou {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -977,7 +977,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_p4tpm2o5kg4lqqcba3gf" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_2olu6wk3h3qqthxs8g0z" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Miles / Gallon</th>
@@ -999,12 +999,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_q7v41sb1r5urvcejhpuj(i, j, css_id) {
-        var table = document.getElementById("tinytable_p4tpm2o5kg4lqqcba3gf");
+      function styleCell_tinytable_jz7orhtfuuobo48whm3x(i, j, css_id) {
+        var table = document.getElementById("tinytable_2olu6wk3h3qqthxs8g0z");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_p4tpm2o5kg4lqqcba3gf');
+        var table = document.getElementById('tinytable_2olu6wk3h3qqthxs8g0z');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1012,8 +1012,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_q7v41sb1r5urvcejhpuj(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_p4tpm2o5kg4lqqcba3gf");
+      function spanCell_tinytable_jz7orhtfuuobo48whm3x(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_2olu6wk3h3qqthxs8g0z");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1040,18 +1040,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(0, 0, 'tinytable_css_4nxvgosth46eg9mgmj1z') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(0, 1, 'tinytable_css_4nxvgosth46eg9mgmj1z') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(0, 2, 'tinytable_css_4nxvgosth46eg9mgmj1z') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(0, 0, 'tinytable_css_523d3zbiauhvp3w5aw87') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(1, 0, 'tinytable_css_523d3zbiauhvp3w5aw87') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(2, 0, 'tinytable_css_523d3zbiauhvp3w5aw87') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(0, 1, 'tinytable_css_r31uzqpmcmgimfygofd8') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(1, 1, 'tinytable_css_r31uzqpmcmgimfygofd8') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(2, 1, 'tinytable_css_r31uzqpmcmgimfygofd8') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(0, 2, 'tinytable_css_2sh8cbf60iw5w5ef8f1m') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(1, 2, 'tinytable_css_2sh8cbf60iw5w5ef8f1m') })
-window.addEventListener('load', function () { styleCell_tinytable_q7v41sb1r5urvcejhpuj(2, 2, 'tinytable_css_2sh8cbf60iw5w5ef8f1m') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(0, 0, 'tinytable_css_cntctshukqz6kzmyja4b') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(0, 1, 'tinytable_css_cntctshukqz6kzmyja4b') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(0, 2, 'tinytable_css_cntctshukqz6kzmyja4b') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(0, 0, 'tinytable_css_f7qrtmmynrzoyz66m8nh') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(1, 0, 'tinytable_css_f7qrtmmynrzoyz66m8nh') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(2, 0, 'tinytable_css_f7qrtmmynrzoyz66m8nh') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(0, 1, 'tinytable_css_pj44atcwo4xsmaehxkdk') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(1, 1, 'tinytable_css_pj44atcwo4xsmaehxkdk') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(2, 1, 'tinytable_css_pj44atcwo4xsmaehxkdk') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(0, 2, 'tinytable_css_lpda02jahh2c8ptk1jou') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(1, 2, 'tinytable_css_lpda02jahh2c8ptk1jou') })
+window.addEventListener('load', function () { styleCell_tinytable_jz7orhtfuuobo48whm3x(2, 2, 'tinytable_css_lpda02jahh2c8ptk1jou') })
     </script>
 </div>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># rename columns alphabetically and include a footnote for reference</span></span>
@@ -1067,12 +1067,12 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_589y8mj80a8n4892byot</title>
+<title>tinytable_unbyis56d7465brso0gh</title>
 <style>
-.table td.tinytable_css_dc4a5hvt5xl7fzvicbuc, .table th.tinytable_css_dc4a5hvt5xl7fzvicbuc {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_u084z2u6xm5zymz35n1e, .table th.tinytable_css_u084z2u6xm5zymz35n1e {    text-align: left; }
-.table td.tinytable_css_9mgzeevypi3avrt5mh4t, .table th.tinytable_css_9mgzeevypi3avrt5mh4t {    text-align: right; }
-.table td.tinytable_css_q7dlss9sg943qccjgr4k, .table th.tinytable_css_q7dlss9sg943qccjgr4k {    text-align: right; }
+.table td.tinytable_css_og1dfp1i4gevksp3gpba, .table th.tinytable_css_og1dfp1i4gevksp3gpba {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_n2ce7wjuw2tp3utjf1oq, .table th.tinytable_css_n2ce7wjuw2tp3utjf1oq {    text-align: left; }
+.table td.tinytable_css_a03zv05blahd2ts1iu59, .table th.tinytable_css_a03zv05blahd2ts1iu59 {    text-align: right; }
+.table td.tinytable_css_uecvpkf0k56t18sb1sox, .table th.tinytable_css_uecvpkf0k56t18sb1sox {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1084,7 +1084,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_589y8mj80a8n4892byot" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_unbyis56d7465brso0gh" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(a)</th>
@@ -1107,12 +1107,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_8dw5iyqe2cc7urvnal2s(i, j, css_id) {
-        var table = document.getElementById("tinytable_589y8mj80a8n4892byot");
+      function styleCell_tinytable_4pb9eopwj3vxxcks9s4r(i, j, css_id) {
+        var table = document.getElementById("tinytable_unbyis56d7465brso0gh");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_589y8mj80a8n4892byot');
+        var table = document.getElementById('tinytable_unbyis56d7465brso0gh');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1120,8 +1120,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_8dw5iyqe2cc7urvnal2s(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_589y8mj80a8n4892byot");
+      function spanCell_tinytable_4pb9eopwj3vxxcks9s4r(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_unbyis56d7465brso0gh");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1148,18 +1148,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(0, 0, 'tinytable_css_dc4a5hvt5xl7fzvicbuc') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(0, 1, 'tinytable_css_dc4a5hvt5xl7fzvicbuc') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(0, 2, 'tinytable_css_dc4a5hvt5xl7fzvicbuc') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(0, 0, 'tinytable_css_u084z2u6xm5zymz35n1e') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(1, 0, 'tinytable_css_u084z2u6xm5zymz35n1e') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(2, 0, 'tinytable_css_u084z2u6xm5zymz35n1e') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(0, 1, 'tinytable_css_9mgzeevypi3avrt5mh4t') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(1, 1, 'tinytable_css_9mgzeevypi3avrt5mh4t') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(2, 1, 'tinytable_css_9mgzeevypi3avrt5mh4t') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(0, 2, 'tinytable_css_q7dlss9sg943qccjgr4k') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(1, 2, 'tinytable_css_q7dlss9sg943qccjgr4k') })
-window.addEventListener('load', function () { styleCell_tinytable_8dw5iyqe2cc7urvnal2s(2, 2, 'tinytable_css_q7dlss9sg943qccjgr4k') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(0, 0, 'tinytable_css_og1dfp1i4gevksp3gpba') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(0, 1, 'tinytable_css_og1dfp1i4gevksp3gpba') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(0, 2, 'tinytable_css_og1dfp1i4gevksp3gpba') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(0, 0, 'tinytable_css_n2ce7wjuw2tp3utjf1oq') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(1, 0, 'tinytable_css_n2ce7wjuw2tp3utjf1oq') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(2, 0, 'tinytable_css_n2ce7wjuw2tp3utjf1oq') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(0, 1, 'tinytable_css_a03zv05blahd2ts1iu59') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(1, 1, 'tinytable_css_a03zv05blahd2ts1iu59') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(2, 1, 'tinytable_css_a03zv05blahd2ts1iu59') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(0, 2, 'tinytable_css_uecvpkf0k56t18sb1sox') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(1, 2, 'tinytable_css_uecvpkf0k56t18sb1sox') })
+window.addEventListener('load', function () { styleCell_tinytable_4pb9eopwj3vxxcks9s4r(2, 2, 'tinytable_css_uecvpkf0k56t18sb1sox') })
     </script>
 </div>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># `datasummary_correlation_format`: custom function with formatting</span></span>
@@ -1181,13 +1181,13 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_geji1n86vqcdz133icad</title>
+<title>tinytable_290nz6nezudmuuclwueb</title>
 <style>
-.table td.tinytable_css_bjgedd8rionojf34zmmy, .table th.tinytable_css_bjgedd8rionojf34zmmy {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_axynebnctyydezii6wj6, .table th.tinytable_css_axynebnctyydezii6wj6 {    text-align: left; }
-.table td.tinytable_css_gq3k5f8vmht79pk7c32s, .table th.tinytable_css_gq3k5f8vmht79pk7c32s {    text-align: right; }
-.table td.tinytable_css_vf7eshobv0xt8orgs9ir, .table th.tinytable_css_vf7eshobv0xt8orgs9ir {    text-align: right; }
-.table td.tinytable_css_xm0eoitqkdx978bpymv6, .table th.tinytable_css_xm0eoitqkdx978bpymv6 {    text-align: right; }
+.table td.tinytable_css_cx8wx6k8uzqhhekk4utz, .table th.tinytable_css_cx8wx6k8uzqhhekk4utz {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_2e7brduli350l2veo4lw, .table th.tinytable_css_2e7brduli350l2veo4lw {    text-align: left; }
+.table td.tinytable_css_43bedjcbk0v97nylxokj, .table th.tinytable_css_43bedjcbk0v97nylxokj {    text-align: right; }
+.table td.tinytable_css_39souty24vu33n5hmany, .table th.tinytable_css_39souty24vu33n5hmany {    text-align: right; }
+.table td.tinytable_css_2qxly9fvk0dngioecmv9, .table th.tinytable_css_2qxly9fvk0dngioecmv9 {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1199,7 +1199,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_geji1n86vqcdz133icad" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_290nz6nezudmuuclwueb" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mpg</th>
@@ -1230,12 +1230,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_fqjlt80bip1o7ntajm6r(i, j, css_id) {
-        var table = document.getElementById("tinytable_geji1n86vqcdz133icad");
+      function styleCell_tinytable_dax3yx6eorf8726dd67o(i, j, css_id) {
+        var table = document.getElementById("tinytable_290nz6nezudmuuclwueb");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_geji1n86vqcdz133icad');
+        var table = document.getElementById('tinytable_290nz6nezudmuuclwueb');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1243,8 +1243,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_fqjlt80bip1o7ntajm6r(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_geji1n86vqcdz133icad");
+      function spanCell_tinytable_dax3yx6eorf8726dd67o(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_290nz6nezudmuuclwueb");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1271,26 +1271,26 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 0, 'tinytable_css_bjgedd8rionojf34zmmy') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 1, 'tinytable_css_bjgedd8rionojf34zmmy') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 2, 'tinytable_css_bjgedd8rionojf34zmmy') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 3, 'tinytable_css_bjgedd8rionojf34zmmy') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 0, 'tinytable_css_axynebnctyydezii6wj6') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(1, 0, 'tinytable_css_axynebnctyydezii6wj6') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(2, 0, 'tinytable_css_axynebnctyydezii6wj6') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(3, 0, 'tinytable_css_axynebnctyydezii6wj6') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 1, 'tinytable_css_gq3k5f8vmht79pk7c32s') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(1, 1, 'tinytable_css_gq3k5f8vmht79pk7c32s') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(2, 1, 'tinytable_css_gq3k5f8vmht79pk7c32s') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(3, 1, 'tinytable_css_gq3k5f8vmht79pk7c32s') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 2, 'tinytable_css_vf7eshobv0xt8orgs9ir') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(1, 2, 'tinytable_css_vf7eshobv0xt8orgs9ir') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(2, 2, 'tinytable_css_vf7eshobv0xt8orgs9ir') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(3, 2, 'tinytable_css_vf7eshobv0xt8orgs9ir') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(0, 3, 'tinytable_css_xm0eoitqkdx978bpymv6') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(1, 3, 'tinytable_css_xm0eoitqkdx978bpymv6') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(2, 3, 'tinytable_css_xm0eoitqkdx978bpymv6') })
-window.addEventListener('load', function () { styleCell_tinytable_fqjlt80bip1o7ntajm6r(3, 3, 'tinytable_css_xm0eoitqkdx978bpymv6') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 0, 'tinytable_css_cx8wx6k8uzqhhekk4utz') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 1, 'tinytable_css_cx8wx6k8uzqhhekk4utz') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 2, 'tinytable_css_cx8wx6k8uzqhhekk4utz') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 3, 'tinytable_css_cx8wx6k8uzqhhekk4utz') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 0, 'tinytable_css_2e7brduli350l2veo4lw') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(1, 0, 'tinytable_css_2e7brduli350l2veo4lw') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(2, 0, 'tinytable_css_2e7brduli350l2veo4lw') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(3, 0, 'tinytable_css_2e7brduli350l2veo4lw') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 1, 'tinytable_css_43bedjcbk0v97nylxokj') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(1, 1, 'tinytable_css_43bedjcbk0v97nylxokj') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(2, 1, 'tinytable_css_43bedjcbk0v97nylxokj') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(3, 1, 'tinytable_css_43bedjcbk0v97nylxokj') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 2, 'tinytable_css_39souty24vu33n5hmany') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(1, 2, 'tinytable_css_39souty24vu33n5hmany') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(2, 2, 'tinytable_css_39souty24vu33n5hmany') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(3, 2, 'tinytable_css_39souty24vu33n5hmany') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(0, 3, 'tinytable_css_2qxly9fvk0dngioecmv9') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(1, 3, 'tinytable_css_2qxly9fvk0dngioecmv9') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(2, 3, 'tinytable_css_2qxly9fvk0dngioecmv9') })
+window.addEventListener('load', function () { styleCell_tinytable_dax3yx6eorf8726dd67o(3, 3, 'tinytable_css_2qxly9fvk0dngioecmv9') })
     </script>
 </div>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># use kableExtra and psych to color significant cells</span></span>
@@ -1326,13 +1326,13 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_zm9d2mpq2l3qj3jk0kjo</title>
+<title>tinytable_mq9s8yxqc31x4l6kzddz</title>
 <style>
-.table td.tinytable_css_zleeh3cnqafu8y4txbz2, .table th.tinytable_css_zleeh3cnqafu8y4txbz2 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_4c4c4f0gr8wmogkdutn2, .table th.tinytable_css_4c4c4f0gr8wmogkdutn2 {    text-align: left; }
-.table td.tinytable_css_bczjdh51odwpk5c84mu7, .table th.tinytable_css_bczjdh51odwpk5c84mu7 {    text-align: right; }
-.table td.tinytable_css_devf7i332d1gqtk926z5, .table th.tinytable_css_devf7i332d1gqtk926z5 {    text-align: right; }
-.table td.tinytable_css_yx89zj8ag5esl1sldtss, .table th.tinytable_css_yx89zj8ag5esl1sldtss {    text-align: right; }
+.table td.tinytable_css_zchl1ggqpwiuuupgz0ak, .table th.tinytable_css_zchl1ggqpwiuuupgz0ak {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_pfyu84lqwtn5nbkd4k0j, .table th.tinytable_css_pfyu84lqwtn5nbkd4k0j {    text-align: left; }
+.table td.tinytable_css_d2s9nr6aoxxs5jswk7nx, .table th.tinytable_css_d2s9nr6aoxxs5jswk7nx {    text-align: right; }
+.table td.tinytable_css_ijqqt64cklkhq0azinj5, .table th.tinytable_css_ijqqt64cklkhq0azinj5 {    text-align: right; }
+.table td.tinytable_css_9r6rnta365ckghnsh9pz, .table th.tinytable_css_9r6rnta365ckghnsh9pz {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1344,7 +1344,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_zm9d2mpq2l3qj3jk0kjo" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_mq9s8yxqc31x4l6kzddz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">vs</th>
@@ -1375,12 +1375,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_npfizmnyjsqmm6ty2dks(i, j, css_id) {
-        var table = document.getElementById("tinytable_zm9d2mpq2l3qj3jk0kjo");
+      function styleCell_tinytable_y8d6rfprennbmjp9rgpg(i, j, css_id) {
+        var table = document.getElementById("tinytable_mq9s8yxqc31x4l6kzddz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_zm9d2mpq2l3qj3jk0kjo');
+        var table = document.getElementById('tinytable_mq9s8yxqc31x4l6kzddz');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1388,8 +1388,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_npfizmnyjsqmm6ty2dks(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_zm9d2mpq2l3qj3jk0kjo");
+      function spanCell_tinytable_y8d6rfprennbmjp9rgpg(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_mq9s8yxqc31x4l6kzddz");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1416,26 +1416,26 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 0, 'tinytable_css_zleeh3cnqafu8y4txbz2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 1, 'tinytable_css_zleeh3cnqafu8y4txbz2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 2, 'tinytable_css_zleeh3cnqafu8y4txbz2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 3, 'tinytable_css_zleeh3cnqafu8y4txbz2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 0, 'tinytable_css_4c4c4f0gr8wmogkdutn2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(1, 0, 'tinytable_css_4c4c4f0gr8wmogkdutn2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(2, 0, 'tinytable_css_4c4c4f0gr8wmogkdutn2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(3, 0, 'tinytable_css_4c4c4f0gr8wmogkdutn2') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 1, 'tinytable_css_bczjdh51odwpk5c84mu7') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(1, 1, 'tinytable_css_bczjdh51odwpk5c84mu7') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(2, 1, 'tinytable_css_bczjdh51odwpk5c84mu7') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(3, 1, 'tinytable_css_bczjdh51odwpk5c84mu7') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 2, 'tinytable_css_devf7i332d1gqtk926z5') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(1, 2, 'tinytable_css_devf7i332d1gqtk926z5') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(2, 2, 'tinytable_css_devf7i332d1gqtk926z5') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(3, 2, 'tinytable_css_devf7i332d1gqtk926z5') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(0, 3, 'tinytable_css_yx89zj8ag5esl1sldtss') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(1, 3, 'tinytable_css_yx89zj8ag5esl1sldtss') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(2, 3, 'tinytable_css_yx89zj8ag5esl1sldtss') })
-window.addEventListener('load', function () { styleCell_tinytable_npfizmnyjsqmm6ty2dks(3, 3, 'tinytable_css_yx89zj8ag5esl1sldtss') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 0, 'tinytable_css_zchl1ggqpwiuuupgz0ak') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 1, 'tinytable_css_zchl1ggqpwiuuupgz0ak') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 2, 'tinytable_css_zchl1ggqpwiuuupgz0ak') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 3, 'tinytable_css_zchl1ggqpwiuuupgz0ak') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 0, 'tinytable_css_pfyu84lqwtn5nbkd4k0j') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(1, 0, 'tinytable_css_pfyu84lqwtn5nbkd4k0j') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(2, 0, 'tinytable_css_pfyu84lqwtn5nbkd4k0j') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(3, 0, 'tinytable_css_pfyu84lqwtn5nbkd4k0j') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 1, 'tinytable_css_d2s9nr6aoxxs5jswk7nx') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(1, 1, 'tinytable_css_d2s9nr6aoxxs5jswk7nx') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(2, 1, 'tinytable_css_d2s9nr6aoxxs5jswk7nx') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(3, 1, 'tinytable_css_d2s9nr6aoxxs5jswk7nx') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 2, 'tinytable_css_ijqqt64cklkhq0azinj5') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(1, 2, 'tinytable_css_ijqqt64cklkhq0azinj5') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(2, 2, 'tinytable_css_ijqqt64cklkhq0azinj5') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(3, 2, 'tinytable_css_ijqqt64cklkhq0azinj5') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(0, 3, 'tinytable_css_9r6rnta365ckghnsh9pz') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(1, 3, 'tinytable_css_9r6rnta365ckghnsh9pz') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(2, 3, 'tinytable_css_9r6rnta365ckghnsh9pz') })
+window.addEventListener('load', function () { styleCell_tinytable_y8d6rfprennbmjp9rgpg(3, 3, 'tinytable_css_9r6rnta365ckghnsh9pz') })
     </script>
 </div>
 </div>
diff --git a/man/datasummary_correlation_format.html b/man/datasummary_correlation_format.html
index bf431297..6c02b81d 100644
--- a/man/datasummary_correlation_format.html
+++ b/man/datasummary_correlation_format.html
@@ -477,13 +477,13 @@
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_6wii9nlcrleb5hdx32gx</title>
+<title>tinytable_392nnoahgseq58zawjya</title>
 <style>
-.table td.tinytable_css_btbassg2uaeytyawab1d, .table th.tinytable_css_btbassg2uaeytyawab1d {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_exkyfyopqompcxxzdzf3, .table th.tinytable_css_exkyfyopqompcxxzdzf3 {    text-align: left; }
-.table td.tinytable_css_iif5xhdxa91pdewe4a0f, .table th.tinytable_css_iif5xhdxa91pdewe4a0f {    text-align: right; }
-.table td.tinytable_css_tyxcfuyi0gylbs4777pg, .table th.tinytable_css_tyxcfuyi0gylbs4777pg {    text-align: right; }
-.table td.tinytable_css_8ikh34zjwtvnaittpjh7, .table th.tinytable_css_8ikh34zjwtvnaittpjh7 {    text-align: right; }
+.table td.tinytable_css_q12byrj0ugo8vtofttv9, .table th.tinytable_css_q12byrj0ugo8vtofttv9 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_8rnis71od4u8rzddcm8o, .table th.tinytable_css_8rnis71od4u8rzddcm8o {    text-align: left; }
+.table td.tinytable_css_yisyuz86kq3p17q4pca4, .table th.tinytable_css_yisyuz86kq3p17q4pca4 {    text-align: right; }
+.table td.tinytable_css_dqbrchbx0baumic82mxo, .table th.tinytable_css_dqbrchbx0baumic82mxo {    text-align: right; }
+.table td.tinytable_css_v7xt5savhf1efcaumj4v, .table th.tinytable_css_v7xt5savhf1efcaumj4v {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -495,7 +495,7 @@
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_6wii9nlcrleb5hdx32gx" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_392nnoahgseq58zawjya" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mpg</th>
@@ -526,12 +526,12 @@
 </div>
 
     <script>
-      function styleCell_tinytable_6h532tou394ijkyxil2d(i, j, css_id) {
-        var table = document.getElementById("tinytable_6wii9nlcrleb5hdx32gx");
+      function styleCell_tinytable_wt317nyi4k2unr4jplgs(i, j, css_id) {
+        var table = document.getElementById("tinytable_392nnoahgseq58zawjya");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_6wii9nlcrleb5hdx32gx');
+        var table = document.getElementById('tinytable_392nnoahgseq58zawjya');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -539,8 +539,8 @@
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_6h532tou394ijkyxil2d(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_6wii9nlcrleb5hdx32gx");
+      function spanCell_tinytable_wt317nyi4k2unr4jplgs(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_392nnoahgseq58zawjya");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -567,26 +567,26 @@
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 0, 'tinytable_css_btbassg2uaeytyawab1d') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 1, 'tinytable_css_btbassg2uaeytyawab1d') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 2, 'tinytable_css_btbassg2uaeytyawab1d') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 3, 'tinytable_css_btbassg2uaeytyawab1d') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 0, 'tinytable_css_exkyfyopqompcxxzdzf3') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(1, 0, 'tinytable_css_exkyfyopqompcxxzdzf3') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(2, 0, 'tinytable_css_exkyfyopqompcxxzdzf3') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(3, 0, 'tinytable_css_exkyfyopqompcxxzdzf3') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 1, 'tinytable_css_iif5xhdxa91pdewe4a0f') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(1, 1, 'tinytable_css_iif5xhdxa91pdewe4a0f') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(2, 1, 'tinytable_css_iif5xhdxa91pdewe4a0f') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(3, 1, 'tinytable_css_iif5xhdxa91pdewe4a0f') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 2, 'tinytable_css_tyxcfuyi0gylbs4777pg') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(1, 2, 'tinytable_css_tyxcfuyi0gylbs4777pg') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(2, 2, 'tinytable_css_tyxcfuyi0gylbs4777pg') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(3, 2, 'tinytable_css_tyxcfuyi0gylbs4777pg') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(0, 3, 'tinytable_css_8ikh34zjwtvnaittpjh7') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(1, 3, 'tinytable_css_8ikh34zjwtvnaittpjh7') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(2, 3, 'tinytable_css_8ikh34zjwtvnaittpjh7') })
-window.addEventListener('load', function () { styleCell_tinytable_6h532tou394ijkyxil2d(3, 3, 'tinytable_css_8ikh34zjwtvnaittpjh7') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 0, 'tinytable_css_q12byrj0ugo8vtofttv9') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 1, 'tinytable_css_q12byrj0ugo8vtofttv9') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 2, 'tinytable_css_q12byrj0ugo8vtofttv9') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 3, 'tinytable_css_q12byrj0ugo8vtofttv9') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 0, 'tinytable_css_8rnis71od4u8rzddcm8o') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(1, 0, 'tinytable_css_8rnis71od4u8rzddcm8o') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(2, 0, 'tinytable_css_8rnis71od4u8rzddcm8o') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(3, 0, 'tinytable_css_8rnis71od4u8rzddcm8o') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 1, 'tinytable_css_yisyuz86kq3p17q4pca4') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(1, 1, 'tinytable_css_yisyuz86kq3p17q4pca4') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(2, 1, 'tinytable_css_yisyuz86kq3p17q4pca4') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(3, 1, 'tinytable_css_yisyuz86kq3p17q4pca4') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 2, 'tinytable_css_dqbrchbx0baumic82mxo') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(1, 2, 'tinytable_css_dqbrchbx0baumic82mxo') })
+window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(2, 2, 'tinytable_css_dqbrchbx0baumic82mxo') })
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diff --git a/man/datasummary_crosstab.html b/man/datasummary_crosstab.html
index 8d76684b..3ad9361b 100644
--- a/man/datasummary_crosstab.html
+++ b/man/datasummary_crosstab.html
@@ -664,15 +664,15 @@ <h4 class="anchored">
   
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-<title>tinytable_5jo7uscatnl9bo050jwr</title>
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-      <table class="table table-borderless" id="tinytable_5jo7uscatnl9bo050jwr" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_lf0vwp3ves3ldpvhgfhj" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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+      function styleCell_tinytable_xve8kn9x4y36oon52q75(i, j, css_id) {
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     </script>
 </div>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># crosstab of two variables, showing counts only and no totals</span></span>
@@ -874,14 +874,14 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_rg2ln5m4hvlf9490t7zi</title>
+<title>tinytable_663m5e7737wydockz4f6</title>
 <style>
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+.table td.tinytable_css_ghytopoo3o4vifuddcet, .table th.tinytable_css_ghytopoo3o4vifuddcet {    text-align: left; }
+.table td.tinytable_css_ey803fvm761ultoe2rk5, .table th.tinytable_css_ey803fvm761ultoe2rk5 {    text-align: left; }
+.table td.tinytable_css_hzrn4jrb74ug6gg0opbw, .table th.tinytable_css_hzrn4jrb74ug6gg0opbw {    text-align: right; }
+.table td.tinytable_css_r858lye49tq7p6aq0z5m, .table th.tinytable_css_r858lye49tq7p6aq0z5m {    text-align: right; }
+.table td.tinytable_css_iuvcymis8kstybm22leb, .table th.tinytable_css_iuvcymis8kstybm22leb {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -893,7 +893,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_rg2ln5m4hvlf9490t7zi" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_663m5e7737wydockz4f6" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">cyl</th>
                 <th scope="col"> </th>
@@ -928,12 +928,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_bdfamihi0zw66yvp7uqy(i, j, css_id) {
-        var table = document.getElementById("tinytable_rg2ln5m4hvlf9490t7zi");
+      function styleCell_tinytable_7w86ierjqmrh93xko8kc(i, j, css_id) {
+        var table = document.getElementById("tinytable_663m5e7737wydockz4f6");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_rg2ln5m4hvlf9490t7zi');
+        var table = document.getElementById('tinytable_663m5e7737wydockz4f6');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -941,8 +941,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_bdfamihi0zw66yvp7uqy(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_rg2ln5m4hvlf9490t7zi");
+      function spanCell_tinytable_7w86ierjqmrh93xko8kc(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_663m5e7737wydockz4f6");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -969,31 +969,31 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
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+window.addEventListener('load', function () { styleCell_tinytable_7w86ierjqmrh93xko8kc(2, 3, 'tinytable_css_r858lye49tq7p6aq0z5m') })
+window.addEventListener('load', function () { styleCell_tinytable_7w86ierjqmrh93xko8kc(3, 3, 'tinytable_css_r858lye49tq7p6aq0z5m') })
+window.addEventListener('load', function () { styleCell_tinytable_7w86ierjqmrh93xko8kc(0, 4, 'tinytable_css_iuvcymis8kstybm22leb') })
+window.addEventListener('load', function () { styleCell_tinytable_7w86ierjqmrh93xko8kc(1, 4, 'tinytable_css_iuvcymis8kstybm22leb') })
+window.addEventListener('load', function () { styleCell_tinytable_7w86ierjqmrh93xko8kc(2, 4, 'tinytable_css_iuvcymis8kstybm22leb') })
+window.addEventListener('load', function () { styleCell_tinytable_7w86ierjqmrh93xko8kc(3, 4, 'tinytable_css_iuvcymis8kstybm22leb') })
     </script>
 </div>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># crosstab of three variables</span></span>
@@ -1004,16 +1004,16 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_t3qllvgnzo3q4mc43anw</title>
+<title>tinytable_6aeigmuvtiy8r06xetaa</title>
 <style>
-.table td.tinytable_css_eqgsi9bypkyoutpi0gbt, .table th.tinytable_css_eqgsi9bypkyoutpi0gbt {    border-bottom: solid 0.1em #d3d8dc; }
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-.table td.tinytable_css_vs9h59fzqwhi3w2c4u2v, .table th.tinytable_css_vs9h59fzqwhi3w2c4u2v {    text-align: right; }
-.table td.tinytable_css_uvozw3wjpr6obn2mu60y, .table th.tinytable_css_uvozw3wjpr6obn2mu60y {    text-align: right; }
-.table td.tinytable_css_bjorhsp9dhdbno11gxn0, .table th.tinytable_css_bjorhsp9dhdbno11gxn0 {    text-align: right; }
-.table td.tinytable_css_kwi17i6l7uxui7vg0uo8, .table th.tinytable_css_kwi17i6l7uxui7vg0uo8 {    text-align: right; }
+.table td.tinytable_css_e98z9vil52a6p6y5cird, .table th.tinytable_css_e98z9vil52a6p6y5cird {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_lywrhmij9apmy6276l3n, .table th.tinytable_css_lywrhmij9apmy6276l3n {    text-align: left; }
+.table td.tinytable_css_9u594msq761ycqq1si01, .table th.tinytable_css_9u594msq761ycqq1si01 {    text-align: left; }
+.table td.tinytable_css_vw8ptz53orgt4qe1lgzb, .table th.tinytable_css_vw8ptz53orgt4qe1lgzb {    text-align: left; }
+.table td.tinytable_css_a9io080d6l0179oh64z9, .table th.tinytable_css_a9io080d6l0179oh64z9 {    text-align: right; }
+.table td.tinytable_css_iflbq6ey8nh2kpgqhyly, .table th.tinytable_css_iflbq6ey8nh2kpgqhyly {    text-align: right; }
+.table td.tinytable_css_zsk3idgdrw3wsep6579b, .table th.tinytable_css_zsk3idgdrw3wsep6579b {    text-align: right; }
+.table td.tinytable_css_d9jnianuy7qqqm0xrh2k, .table th.tinytable_css_d9jnianuy7qqqm0xrh2k {    text-align: right; }
     </style>
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     MathJax = {
@@ -1025,7 +1025,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_t3qllvgnzo3q4mc43anw" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_6aeigmuvtiy8r06xetaa" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">am</th>
                 <th scope="col">cyl</th>
@@ -1167,12 +1167,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_kga5ddipa266x5cik4dv(i, j, css_id) {
-        var table = document.getElementById("tinytable_t3qllvgnzo3q4mc43anw");
+      function styleCell_tinytable_5gzph0z6zm87cnomtyd7(i, j, css_id) {
+        var table = document.getElementById("tinytable_6aeigmuvtiy8r06xetaa");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_t3qllvgnzo3q4mc43anw');
+        var table = document.getElementById('tinytable_6aeigmuvtiy8r06xetaa');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1180,8 +1180,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_kga5ddipa266x5cik4dv(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_t3qllvgnzo3q4mc43anw");
+      function spanCell_tinytable_5gzph0z6zm87cnomtyd7(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_6aeigmuvtiy8r06xetaa");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1208,118 +1208,118 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
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     </script>
 </div>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># crosstab with two variables and column percentages </span></span>
@@ -1330,14 +1330,14 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_4me5sif2mhvkh3gw6o2s</title>
+<title>tinytable_a07ln2dlnp2nawgi6t0p</title>
 <style>
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-.table td.tinytable_css_iiwjduvy0gjkiyev8wg5, .table th.tinytable_css_iiwjduvy0gjkiyev8wg5 {    text-align: right; }
+.table td.tinytable_css_ip1zaadraqojfzfggxrj, .table th.tinytable_css_ip1zaadraqojfzfggxrj {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_uq4bsfpepd531txkfflr, .table th.tinytable_css_uq4bsfpepd531txkfflr {    text-align: left; }
+.table td.tinytable_css_f3ay2yrbt2oxfhddw11o, .table th.tinytable_css_f3ay2yrbt2oxfhddw11o {    text-align: left; }
+.table td.tinytable_css_5km4tlez1znmmg2qko6o, .table th.tinytable_css_5km4tlez1znmmg2qko6o {    text-align: right; }
+.table td.tinytable_css_8zbeff3f5aoogffesltu, .table th.tinytable_css_8zbeff3f5aoogffesltu {    text-align: right; }
+.table td.tinytable_css_v9v5pzk0h2yw4co1s1y4, .table th.tinytable_css_v9v5pzk0h2yw4co1s1y4 {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1349,7 +1349,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_4me5sif2mhvkh3gw6o2s" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_a07ln2dlnp2nawgi6t0p" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">am</th>
                 <th scope="col"> </th>
@@ -1377,12 +1377,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_smgxdu4yrwnjkjh93mmc(i, j, css_id) {
-        var table = document.getElementById("tinytable_4me5sif2mhvkh3gw6o2s");
+      function styleCell_tinytable_k0th350euycljdtznfa0(i, j, css_id) {
+        var table = document.getElementById("tinytable_a07ln2dlnp2nawgi6t0p");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_4me5sif2mhvkh3gw6o2s');
+        var table = document.getElementById('tinytable_a07ln2dlnp2nawgi6t0p');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1390,8 +1390,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_smgxdu4yrwnjkjh93mmc(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_4me5sif2mhvkh3gw6o2s");
+      function spanCell_tinytable_k0th350euycljdtznfa0(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_a07ln2dlnp2nawgi6t0p");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1418,26 +1418,26 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
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diff --git a/man/dvnames.html b/man/dvnames.html
index b25f1a6f..e2597984 100644
--- a/man/dvnames.html
+++ b/man/dvnames.html
@@ -428,13 +428,13 @@
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_z26tymp7i2bbybq52sa0</title>
+<title>tinytable_u2hh31udbdeemx98iz83</title>
 <style>
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-.table td.tinytable_css_st901jnp1tkizfl40nte, .table th.tinytable_css_st901jnp1tkizfl40nte {    text-align: center; }
-.table td.tinytable_css_etq4ezkjvn16e99gz2ea, .table th.tinytable_css_etq4ezkjvn16e99gz2ea {    text-align: center; }
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+.table td.tinytable_css_fwhrj3xokpuapeq289z8, .table th.tinytable_css_fwhrj3xokpuapeq289z8 {    text-align: left; }
+.table td.tinytable_css_9ikuuyywrjdw4pe6szp6, .table th.tinytable_css_9ikuuyywrjdw4pe6szp6 {    text-align: center; }
+.table td.tinytable_css_2jbkfnc528vhf74gevic, .table th.tinytable_css_2jbkfnc528vhf74gevic {    text-align: center; }
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@@ -446,7 +446,7 @@
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+      <table class="table table-borderless" id="tinytable_u2hh31udbdeemx98iz83" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
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@@ -528,12 +528,12 @@
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-        var table = document.getElementById("tinytable_z26tymp7i2bbybq52sa0");
+      function styleCell_tinytable_yc3kxc0z2u95wvt6dapt(i, j, css_id) {
+        var table = document.getElementById("tinytable_u2hh31udbdeemx98iz83");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_z26tymp7i2bbybq52sa0');
+        var table = document.getElementById('tinytable_u2hh31udbdeemx98iz83');
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         newCell.setAttribute("colspan", colspan);
@@ -541,8 +541,8 @@
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-        var table = document.getElementById("tinytable_z26tymp7i2bbybq52sa0");
+      function spanCell_tinytable_yc3kxc0z2u95wvt6dapt(i, j, rowspan, colspan) {
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+window.addEventListener('load', function () { styleCell_tinytable_yc3kxc0z2u95wvt6dapt(4, 2, 'tinytable_css_2jbkfnc528vhf74gevic') })
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+window.addEventListener('load', function () { styleCell_tinytable_yc3kxc0z2u95wvt6dapt(6, 0, 'tinytable_css_9kcgml1pnigy37pjydf7') })
+window.addEventListener('load', function () { styleCell_tinytable_yc3kxc0z2u95wvt6dapt(6, 1, 'tinytable_css_9kcgml1pnigy37pjydf7') })
+window.addEventListener('load', function () { styleCell_tinytable_yc3kxc0z2u95wvt6dapt(6, 2, 'tinytable_css_9kcgml1pnigy37pjydf7') })
     </script>
 </div>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># With dvnames, they are "mpg" and "mpg"</span></span>
@@ -630,13 +630,13 @@
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_5fvwbd5zmtygbbzx5xpe</title>
+<title>tinytable_f852o222qpbpp0nismmp</title>
 <style>
-.table td.tinytable_css_290jwmdua2i7139c36j1, .table th.tinytable_css_290jwmdua2i7139c36j1 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_bxkqg861ipggvjmhg14i, .table th.tinytable_css_bxkqg861ipggvjmhg14i {    text-align: left; }
-.table td.tinytable_css_yqcdo9g3q7lxitgwh0mi, .table th.tinytable_css_yqcdo9g3q7lxitgwh0mi {    text-align: center; }
-.table td.tinytable_css_wqzsjabkhmjom9fs2n5v, .table th.tinytable_css_wqzsjabkhmjom9fs2n5v {    text-align: center; }
-.table td.tinytable_css_8hiusvc6l4iy8ofxi039, .table th.tinytable_css_8hiusvc6l4iy8ofxi039 {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_w3madv23jk793q0vuyzb, .table th.tinytable_css_w3madv23jk793q0vuyzb {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_os0fzorv88w1hizlfmpm, .table th.tinytable_css_os0fzorv88w1hizlfmpm {    text-align: left; }
+.table td.tinytable_css_wjxb5e6u5o1xsglfd0td, .table th.tinytable_css_wjxb5e6u5o1xsglfd0td {    text-align: center; }
+.table td.tinytable_css_5gqbb79krjlp5scbex99, .table th.tinytable_css_5gqbb79krjlp5scbex99 {    text-align: center; }
+.table td.tinytable_css_052qfhbkf9oislyzeaoj, .table th.tinytable_css_052qfhbkf9oislyzeaoj {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -648,7 +648,7 @@
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_5fvwbd5zmtygbbzx5xpe" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_f852o222qpbpp0nismmp" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mpg</th>
@@ -730,12 +730,12 @@
 </div>
 
     <script>
-      function styleCell_tinytable_iagxc9npednvvs0m1nk3(i, j, css_id) {
-        var table = document.getElementById("tinytable_5fvwbd5zmtygbbzx5xpe");
+      function styleCell_tinytable_7g6ktv5vzso0u1iarh20(i, j, css_id) {
+        var table = document.getElementById("tinytable_f852o222qpbpp0nismmp");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_5fvwbd5zmtygbbzx5xpe');
+        var table = document.getElementById('tinytable_f852o222qpbpp0nismmp');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -743,8 +743,8 @@
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_iagxc9npednvvs0m1nk3(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_5fvwbd5zmtygbbzx5xpe");
+      function spanCell_tinytable_7g6ktv5vzso0u1iarh20(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_f852o222qpbpp0nismmp");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -771,57 +771,57 @@
         targetCell.colSpan = colspan;
       }
 
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diff --git a/search.json b/search.json
index 050348d4..f4688a61 100644
--- a/search.json
+++ b/search.json
@@ -1,138 +1,123 @@
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-    "text": "Version 3, 29 June 2007\nCopyright © 2007 Free Software Foundation, Inc. &lt;http://fsf.org/&gt;\nEveryone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.\n\n\n\nThe GNU General Public License is a free, copyleft license for software and other kinds of works.\nThe licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.\nWhen we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.\nTo protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.\nFor example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.\nDevelopers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.\nFor the developers’ and authors’ protection, the GPL clearly explains that there is no warranty for this free software. For both users’ and authors’ sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.\nSome devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users’ freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.\nFinally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.\nThe precise terms and conditions for copying, distribution and modification follow.\n\n\n\n\n\n“This License” refers to version 3 of the GNU General Public License.\n“Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.\n“The Program” refers to any copyrightable work licensed under this License. Each licensee is addressed as “you”. “Licensees” and “recipients” may be individuals or organizations.\nTo “modify” a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a “modified version” of the earlier work or a work “based on” the earlier work.\nA “covered work” means either the unmodified Program or a work based on the Program.\nTo “propagate” a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well.\nTo “convey” a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying.\nAn interactive user interface displays “Appropriate Legal Notices” to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion.\n\n\n\nThe “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work.\nA “Standard Interface” means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language.\nThe “System Libraries” of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A “Major Component”, in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it.\nThe “Corresponding Source” for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities. However, it does not include the work’s System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work. For example, Corresponding Source includes interface definition files associated with source files for the work, and the source code for shared libraries and dynamically linked subprograms that the work is specifically designed to require, such as by intimate data communication or control flow between those subprograms and other parts of the work.\nThe Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source.\nThe Corresponding Source for a work in source code form is that same work.\n\n\n\nAll rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law.\nYou may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you.\nConveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary.\n\n\n\nNo covered work shall be deemed part of an effective technological measure under any applicable law fulfilling obligations under article 11 of the WIPO copyright treaty adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention of such measures.\nWhen you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work’s users, your or third parties’ legal rights to forbid circumvention of technological measures.\n\n\n\nYou may convey verbatim copies of the Program’s source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program.\nYou may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee.\n\n\n\nYou may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions:\n\na) The work must carry prominent notices stating that you modified it, and giving a relevant date.\nb) The work must carry prominent notices stating that it is released under this License and any conditions added under section 7. This requirement modifies the requirement in section 4 to “keep intact all notices”.\nc) You must license the entire work, as a whole, under this License to anyone who comes into possession of a copy. This License will therefore apply, along with any applicable section 7 additional terms, to the whole of the work, and all its parts, regardless of how they are packaged. This License gives no permission to license the work in any other way, but it does not invalidate such permission if you have separately received it.\nd) If the work has interactive user interfaces, each must display Appropriate Legal Notices; however, if the Program has interactive interfaces that do not display Appropriate Legal Notices, your work need not make them do so.\n\nA compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an “aggregate” if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation’s users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate.\n\n\n\nYou may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways:\n\na) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by the Corresponding Source fixed on a durable physical medium customarily used for software interchange.\nb) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge.\nc) Convey individual copies of the object code with a copy of the written offer to provide the Corresponding Source. This alternative is allowed only occasionally and noncommercially, and only if you received the object code with such an offer, in accord with subsection 6b.\nd) Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements.\ne) Convey the object code using peer-to-peer transmission, provided you inform other peers where the object code and Corresponding Source of the work are being offered to the general public at no charge under subsection 6d.\n\nA separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work.\nA “User Product” is either (1) a “consumer product”, which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, “normally used” refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product.\n“Installation Information” for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made.\nIf you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM).\nThe requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.\nCorresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.\n\n\n\n“Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.\nWhen you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.\nNotwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:\n\na) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or\nb) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or\nc) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or\nd) Limiting the use for publicity purposes of names of licensors or authors of the material; or\ne) Declining to grant rights under trademark law for use of some trade names, trademarks, or service marks; or\nf) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors.\n\nAll other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.\nIf you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.\nAdditional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.\n\n\n\nYou may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).\nHowever, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.\nMoreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.\nTermination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.\n\n\n\nYou are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.\n\n\n\nEach time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.\nAn “entity transaction” is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party’s predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.\nYou may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.\n\n\n\nA “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor’s “contributor version”.\nA contributor’s “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.\nEach contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor’s essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.\nIn the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.\nIf you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient’s use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.\nIf, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.\nA patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.\nNothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.\n\n\n\nIf conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.\n\n\n\nNotwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.\n\n\n\nThe Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.\nEach version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.\nIf the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.\nLater license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.\n\n\n\nTHERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n\n\nIN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\n\n\nIf the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.\nEND OF TERMS AND CONDITIONS\n\n\n\n\nIf you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.\nTo do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.\n&lt;one line to give the program's name and a brief idea of what it does.&gt;\nCopyright (C) 2019 Vincent Arel-Bundock\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see &lt;http://www.gnu.org/licenses/&gt;.\nAlso add information on how to contact you by electronic and paper mail.\nIf the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:\nmodelsummary Copyright (C) 2019 Vincent Arel-Bundock\nThis program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.\nThis is free software, and you are welcome to redistribute it\nunder certain conditions; type 'show c' for details.\nThe hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.\nYou should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see &lt;http://www.gnu.org/licenses/&gt;.\nThe GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read &lt;http://www.gnu.org/philosophy/why-not-lgpl.html&gt;.",
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-    "text": "Version 3, 29 June 2007\nCopyright © 2007 Free Software Foundation, Inc. &lt;http://fsf.org/&gt;\nEveryone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.",
+    "text": "To customize the appearance of tables, modelsummary supports five of the most popular table-making packages:\nUsers are encouraged to visit these websites to determine which package suits their needs best.\nTo create customized tables, we proceed as follows:\nTo illustrate, we download data from the Rdatasets repository and we estimate 5 models:\nlibrary(modelsummary)\n\nurl &lt;- \"https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv\"\ndat &lt;- read.csv(url, na.strings = \"\")\n\nmodels &lt;- list(\n    I = lm(Donations ~ Literacy, data = dat),\n    II = lm(Crime_pers ~ Literacy, data = dat),\n    III = lm(Crime_prop ~ Literacy + Clergy, data = dat),\n    IV = glm(Crime_pers ~ Literacy + Clergy, family = poisson, data = dat),\n    V = glm(Donations ~ Literacy + Clergy, family = poisson, data = dat)\n)\nIn the rest of this vignette, we will customize tables using tools tinytable and gt. The same process can be used to customize kableExtra, flextable, huxtable, and DT tables.",
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-    "text": "The GNU General Public License is a free, copyleft license for software and other kinds of works.\nThe licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.\nWhen we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.\nTo protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.\nFor example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.\nDevelopers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.\nFor the developers’ and authors’ protection, the GPL clearly explains that there is no warranty for this free software. For both users’ and authors’ sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.\nSome devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users’ freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.\nFinally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.\nThe precise terms and conditions for copying, distribution and modification follow.",
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+    "text": "Themes: Data Frame\nA particularly flexible strategy is to apply a theme to the dataframe output format. To illustrate, recall that setting output=\"dataframe\" produces a data frame with a lot of extraneous meta information. To produce a nice table, we have to process that output a bit:\n\nmod &lt;- lm(mpg ~ hp + drat, mtcars)\n\nmodelsummary(mod, output = \"dataframe\")\n\n        part        term statistic     (1)\n1  estimates (Intercept)  estimate  10.790\n2  estimates (Intercept) std.error (5.078)\n3  estimates          hp  estimate  -0.052\n4  estimates          hp std.error (0.009)\n5  estimates        drat  estimate   4.698\n6  estimates        drat std.error (1.192)\n7        gof    Num.Obs.                32\n8        gof          R2             0.741\n9        gof     R2 Adj.             0.723\n10       gof         AIC             169.5\n11       gof         BIC             175.4\n12       gof    Log.Lik.           -80.752\n13       gof           F            41.522\n14       gof        RMSE              3.02\n\n\nmodelsummary supports the DT table-making package out of the box. But for the sake of illustration, imagine we want to create a table using the DT package with specific customization and options, in a repeatable fashion. To do this, we can create a theming function:\n\nlibrary(DT)\n\ntheme_df &lt;- function(tab) {\n    out &lt;- tab\n    out$term[out$statistic == \"modelsummary_tmp2\"] &lt;- \" \"\n    out$part &lt;- out$statistic &lt;- NULL\n    colnames(out)[1] &lt;- \" \"\n    datatable(out, rownames = FALSE,\n              options = list(pageLength = 30))\n}\n\noptions(\"modelsummary_theme_dataframe\" = theme_df)\nmodelsummary(mod, output = \"dataframe\")\n\n\n\n\n\nRestore default theme:\n\noptions(\"modelsummary_theme_dataframe\" = NULL)",
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     "section": "",
-    "text": "“This License” refers to version 3 of the GNU General Public License.\n“Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.\n“The Program” refers to any copyrightable work licensed under this License. Each licensee is addressed as “you”. “Licensees” and “recipients” may be individuals or organizations.\nTo “modify” a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a “modified version” of the earlier work or a work “based on” the earlier work.\nA “covered work” means either the unmodified Program or a work based on the Program.\nTo “propagate” a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. 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You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.\nNothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.\n\n\n\nIf conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.\n\n\n\nNotwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.\n\n\n\nThe Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.\nEach version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.\nIf the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.\nLater license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.\n\n\n\nTHERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n\n\nIN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\n\n\nIf the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.\nEND OF TERMS AND CONDITIONS",
+    "text": "To begin, load the modelsummary package and download data from the Rdatasets archive:\n\nlibrary(modelsummary)\nlibrary(tinytable)\n\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'\ndat &lt;- read.csv(url) \ndat$Small &lt;- dat$Pop1831 &gt; median(dat$Pop1831)\ndat &lt;- dat[, \n  c(\"Donations\", \"Literacy\", \"Commerce\", \"Crime_pers\", \"Crime_prop\", \"Clergy\", \"Small\")\n]\n\nData Summaries\nQuick overview of the data:\n\ndatasummary_skim(dat)\n\nWarning: The `replace_na` argument was renamed `replace`.\n\n\n \n\n  \n    \n\ntinytable_5s5s0ho4i0d11mopqcm0\n\n\n      \n\n \n                Unique\n                Missing Pct.\n                Mean\n                SD\n                Min\n                Median\n                Max\n                Histogram\n              \n\n\nDonations\n                  85\n                  0\n                  7075.5\n                  5834.6\n                  1246.0\n                  5020.0\n                  37015.0\n                  \n                \n\nLiteracy\n                  50\n                  0\n                  39.3\n                  17.4\n                  12.0\n                  38.0\n                  74.0\n                  \n                \n\nCommerce\n                  84\n                  0\n                  42.8\n                  25.0\n                  1.0\n                  42.5\n                  86.0\n                  \n                \n\nCrime_pers\n                  85\n                  0\n                  19754.4\n                  7504.7\n                  2199.0\n                  18748.5\n                  37014.0\n                  \n                \n\nCrime_prop\n                  86\n                  0\n                  7843.1\n                  3051.4\n                  1368.0\n                  7595.0\n                  20235.0\n                  \n                \n\nClergy\n                  85\n                  0\n                  43.4\n                  25.0\n                  1.0\n                  43.5\n                  86.0\n                  \n                \n\nSmall\n                  N\n                  %\n                  \n                  \n                  \n                  \n                  \n                  \n                \n\nFALSE\n                  43\n                  50.0\n                  \n                  \n                  \n                  \n                  \n                  \n                \n\nTRUE\n                  43\n                  50.0\n                  \n                  \n                  \n                  \n                  \n                  \n                \n\n\n\n\n    \n\n\nBalance table (aka “Table 1”) with differences in means by subgroups:\n\ndatasummary_balance(~Small, dat)\n\n \n\n  \n    \n\ntinytable_l9y4jxu9shckkefbll5k\n\n\n      \n\n\n \nFALSE (N=43)\nTRUE (N=43)\n \n \n\n\n \n                Mean\n                Std. Dev.\n                Mean\n                Std. Dev.\n                Diff. in Means\n                Std. Error\n              \n\n\n\nDonations \n                  7258.5 \n                  6194.1\n                  6892.6 \n                  5519.0\n                  -365.9 \n                  1265.2\n                \n\nLiteracy  \n                  37.9   \n                  19.1  \n                  40.6   \n                  15.6  \n                  2.7    \n                  3.8   \n                \n\nCommerce  \n                  42.7   \n                  24.6  \n                  43.0   \n                  25.7  \n                  0.3    \n                  5.4   \n                \n\nCrime_pers\n                  18040.6\n                  7638.4\n                  21468.2\n                  7044.3\n                  3427.7 \n                  1584.6\n                \n\nCrime_prop\n                  8422.5 \n                  3406.7\n                  7263.7 \n                  2559.3\n                  -1158.8\n                  649.8 \n                \n\nClergy    \n                  39.1   \n                  26.7  \n                  47.7   \n                  22.7  \n                  8.6    \n                  5.3   \n                \n\n\n\n\n    \n\n\nCorrelation table:\n\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_8zpjnjnivhvk23crvyu3\n\n\n      \n\n \n                Donations\n                Literacy\n                Commerce\n                Crime_pers\n                Crime_prop\n                Clergy\n              \n\n\nDonations \n                  1   \n                  .   \n                  .   \n                  .  \n                  .   \n                  .\n                \n\nLiteracy  \n                  -.13\n                  1   \n                  .   \n                  .  \n                  .   \n                  .\n                \n\nCommerce  \n                  .30 \n                  -.58\n                  1   \n                  .  \n                  .   \n                  .\n                \n\nCrime_pers\n                  -.04\n                  -.04\n                  .05 \n                  1  \n                  .   \n                  .\n                \n\nCrime_prop\n                  -.13\n                  -.37\n                  .41 \n                  .27\n                  1   \n                  .\n                \n\nClergy    \n                  .09 \n                  -.17\n                  -.12\n                  .26\n                  -.07\n                  1\n                \n\n\n\n\n    \n\n\nTwo variables and two statistics, nested in subgroups:\n\ndatasummary(Literacy + Commerce ~ Small * (mean + sd), dat)\n\n \n\n  \n    \n\ntinytable_h3xk51iw9talhwi0q5um\n\n\n      \n\n\n \nFALSE\nTRUE\n\n\n \n                mean\n                sd\n                mean\n                sd\n              \n\n\n\nLiteracy\n                  37.88\n                  19.08\n                  40.63\n                  15.57\n                \n\nCommerce\n                  42.65\n                  24.59\n                  42.95\n                  25.75\n                \n\n\n\n\n    \n\n\nModel Summaries\nEstimate a linear model and display the results:\n\nmod &lt;- lm(Donations ~ Crime_prop, data = dat)\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_fa6zc8q2mjc0ixwi1rfq\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  9065.287  \n                \n\n           \n                  (1738.926)\n                \n\nCrime_prop \n                  -0.254    \n                \n\n           \n                  (0.207)   \n                \n\nNum.Obs.   \n                  86        \n                \n\nR2         \n                  0.018     \n                \n\nR2 Adj.    \n                  0.006     \n                \n\nAIC        \n                  1739.0    \n                \n\nBIC        \n                  1746.4    \n                \n\nLog.Lik.   \n                  -866.516  \n                \n\nF          \n                  1.505     \n                \n\nRMSE       \n                  5749.29   \n                \n\n\n\n\n    \n\n\nNow estimate five regression models, display the results side-by-side, and use the group_tt() function from the tinytable package to add column labels:\n\nlibrary(tinytable)\n\nmodels &lt;- list(\n    \"I\" = lm(Donations ~ Literacy + Clergy, data = dat),\n    \"II\" = lm(Crime_pers ~ Literacy + Clergy, data = dat),\n    \"III\" = lm(Crime_prop ~ Literacy + Clergy, data = dat),\n    \"IV\" = glm(Crime_pers ~ Literacy + Commerce, family = poisson, data = dat),\n    \"V\" = glm(Donations ~ Literacy + Commerce, family = poisson, data = dat)\n)\n\nmodelsummary(models, stars = TRUE, gof_omit = \"IC|Adj|F|RMSE|Log\") |&gt;\n    group_tt(j = list(\"Linear\" = 2:4, \"Poisson\" = 5:6))\n\n \n\n  \n    \n\ntinytable_quyfgid783myjatx00rr\n\n\n      \n\n\n \nLinear\nPoisson\n\n\n \n                I\n                II\n                III\n                IV\n                V\n              \n\n+ p \n\n\n(Intercept)\n                  7948.667***\n                  16259.384***\n                  11243.544***\n                  9.876***\n                  8.241***\n                \n\n           \n                  (2078.276) \n                  (2611.140)  \n                  (1011.240)  \n                  (0.003) \n                  (0.006) \n                \n\nLiteracy   \n                  -39.121    \n                  3.680       \n                  -68.507***  \n                  0.000***\n                  0.003***\n                \n\n           \n                  (37.052)   \n                  (46.552)    \n                  (18.029)    \n                  (0.000) \n                  (0.000) \n                \n\nClergy     \n                  15.257     \n                  77.148*     \n                  -16.376     \n                          \n                          \n                \n\n           \n                  (25.735)   \n                  (32.334)    \n                  (12.522)    \n                          \n                          \n                \n\nCommerce   \n                             \n                              \n                              \n                  0.001***\n                  0.011***\n                \n\n           \n                             \n                              \n                              \n                  (0.000) \n                  (0.000) \n                \n\nNum.Obs.   \n                  86         \n                  86          \n                  86          \n                  86      \n                  86      \n                \n\nR2         \n                  0.020      \n                  0.065       \n                  0.152       \n                          \n                          \n                \n\n\n\n\n    \n\n\nNow, save it to a Microsoft Word document:\n\nmodelsummary(models, output = \"table.docx\")\n\nAnd draw a coefficient plot:\n\nols &lt;- models[1:3]\nmodelplot(ols, coef_omit = \"Intercept\")",
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-    "text": "If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.\nTo do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.\n&lt;one line to give the program's name and a brief idea of what it does.&gt;\nCopyright (C) 2019 Vincent Arel-Bundock\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see &lt;http://www.gnu.org/licenses/&gt;.\nAlso add information on how to contact you by electronic and paper mail.\nIf the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:\nmodelsummary Copyright (C) 2019 Vincent Arel-Bundock\nThis program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.\nThis is free software, and you are welcome to redistribute it\nunder certain conditions; type 'show c' for details.\nThe hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.\nYou should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see &lt;http://www.gnu.org/licenses/&gt;.\nThe GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read &lt;http://www.gnu.org/philosophy/why-not-lgpl.html&gt;.",
+    "text": "modelplot is a function from the modelsummary package. It allows you to plot model estimates and confidence intervals. It makes it easy to subset, rename, reorder, and customize plots using same mechanics as in modelsummary.\nTo illustrate how the function works, we fit a linear model to data about the Palmer Penguins:\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv'\ndat &lt;- read.csv(url)\n\n# rescale mm -&gt; cm\ndat$bill_length_cm &lt;- dat$bill_length_mm / 10\ndat$flipper_length_cm &lt;- dat$flipper_length_mm / 10\n\nmod &lt;- lm(bill_length_cm ~ flipper_length_cm + species, data = dat)\nThen, we load the modelsummary library and call modelplot:\nlibrary(modelsummary)\n\nmodelplot(mod)",
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-    "text": "Convenience function to tabulate counts, cell percentages, and row/column percentages for categorical variables. See the Details section for a description of the internal design. For more complex cross tabulations, use datasummary directly. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_crosstab(\n  formula,\n  statistic = 1 ~ 1 + N + Percent(\"row\"),\n  data,\n  output = \"default\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  sparse_header = TRUE,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nformula\n\n\nA two-sided formula to describe the table: rows ~ columns, where rows and columns are variables in the data. Rows and columns may contain interactions, e.g., var1 * var2 ~ var3.\n\n\n\n\nstatistic\n\n\nA formula of the form 1 ~ 1 + N + Percent(“row”). The left-hand side may only be empty or contain a 1 to include row totals. The right-hand side may contain: 1 for column totals, N for counts, Percent() for cell percentages, Percent(“row”) for row percentages, Percent(“col”) for column percentages.\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nsparse_header\n\n\nTRUE or FALSE. TRUE eliminates column headers which have a unique label across all columns, except for the row immediately above the data. FALSE keeps all headers. The order in which terms are entered in the formula determines the order in which headers appear. For example, x~mean*z will print the mean-related header above the z-related header.’\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\ndatasummary_crosstab is a wrapper around the datasummary function. This wrapper works by creating a customized formula and by feeding it to datasummary. The customized formula comes in two parts.\nFirst, we take a two-sided formula supplied by the formula argument. All variables of that formula are wrapped in a Factor() call to ensure that the variables are treated as categorical.\nSecond, the statistic argument gives a two-sided formula which specifies the statistics to include in the table. datasummary_crosstab modifies this formula automatically to include \"clean\" labels.\nFinally, the formula and statistic formulas are combined into a single formula which is fed directly to the datasummary function to produce the table.\nVariables in formula are automatically wrapped in Factor().\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# crosstab of two variables, showing counts, row percentages, and row/column totals\ndatasummary_crosstab(cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_5jo7uscatnl9bo050jwr\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n4  \n                  N    \n                  1   \n                  8   \n                  2   \n                  11   \n                \n\n   \n                  % row\n                  9.1 \n                  72.7\n                  18.2\n                  100.0\n                \n\n6  \n                  N    \n                  2   \n                  4   \n                  1   \n                  7    \n                \n\n   \n                  % row\n                  28.6\n                  57.1\n                  14.3\n                  100.0\n                \n\n8  \n                  N    \n                  12  \n                  0   \n                  2   \n                  14   \n                \n\n   \n                  % row\n                  85.7\n                  0.0 \n                  14.3\n                  100.0\n                \n\nAll\n                  N    \n                  15  \n                  12  \n                  5   \n                  32   \n                \n\n   \n                  % row\n                  46.9\n                  37.5\n                  15.6\n                  100.0\n                \n\n\n\n\n    \n\n# crosstab of two variables, showing counts only and no totals\ndatasummary_crosstab(cyl ~ gear, statistic = ~ N, data = mtcars)\n\n \n\n  \n    \n\ntinytable_rg2ln5m4hvlf9490t7zi\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n              \n\n\n4\n                  N\n                  1 \n                  8\n                  2\n                \n\n6\n                  N\n                  2 \n                  4\n                  1\n                \n\n8\n                  N\n                  12\n                  0\n                  2\n                \n\n\n\n\n    \n\n# crosstab of three variables\n  datasummary_crosstab(am * cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_t3qllvgnzo3q4mc43anw\n\n\n      \n\nam\n                cyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n0\n                  4  \n                  N    \n                  1    \n                  2   \n                  0    \n                  3    \n                \n\n \n                     \n                  % row\n                  33.3 \n                  66.7\n                  0.0  \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  2    \n                  2   \n                  0    \n                  4    \n                \n\n \n                     \n                  % row\n                  50.0 \n                  50.0\n                  0.0  \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  12   \n                  0   \n                  0    \n                  12   \n                \n\n \n                     \n                  % row\n                  100.0\n                  0.0 \n                  0.0  \n                  100.0\n                \n\n1\n                  4  \n                  N    \n                  0    \n                  6   \n                  2    \n                  8    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  75.0\n                  25.0 \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  0    \n                  2   \n                  1    \n                  3    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  66.7\n                  33.3 \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  0    \n                  0   \n                  2    \n                  2    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  0.0 \n                  100.0\n                  100.0\n                \n\n \n                  All\n                  N    \n                  15   \n                  12  \n                  5    \n                  32   \n                \n\n \n                     \n                  % row\n                  46.9 \n                  37.5\n                  15.6 \n                  100.0\n                \n\n\n\n\n    \n\n# crosstab with two variables and column percentages \ndatasummary_crosstab(am ~ gear, statistic = ~ Percent(\"col\"), data = mtcars)\n\n \n\n  \n    \n\ntinytable_4me5sif2mhvkh3gw6o2s\n\n\n      \n\nam\n                 \n                3\n                4\n                5\n              \n\n\n0\n                  % col\n                  100.0\n                  33.3\n                  0.0  \n                \n\n1\n                  % col\n                  0.0  \n                  66.7\n                  100.0",
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+    "text": "Conditional colors and shape\nNote: This section requires a version of modelsummary greater than 1.2.0 or the development version.\nIn a very nice Stack Overflow answer, Allan Cameron shows how we can use the aes() function from ggplot2 to add conditional aesthetics. For example, if we want to display statistically significant coefficients in a different color:\n\nlibrary(ggplot2)\nmod &lt;- lm(hp ~ factor(gear) + factor(cyl), data = mtcars)\n\nmodelplot(mod, coef_rename = TRUE) +\n  aes(color = ifelse(p.value &lt; 0.001, \"Significant\", \"Not significant\")) +\n  scale_color_manual(values = c(\"grey\", \"black\"))",
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-    "text": "Convenience function to tabulate counts, cell percentages, and row/column percentages for categorical variables. See the Details section for a description of the internal design. For more complex cross tabulations, use datasummary directly. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_crosstab(\n  formula,\n  statistic = 1 ~ 1 + N + Percent(\"row\"),\n  data,\n  output = \"default\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  sparse_header = TRUE,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nformula\n\n\nA two-sided formula to describe the table: rows ~ columns, where rows and columns are variables in the data. Rows and columns may contain interactions, e.g., var1 * var2 ~ var3.\n\n\n\n\nstatistic\n\n\nA formula of the form 1 ~ 1 + N + Percent(“row”). The left-hand side may only be empty or contain a 1 to include row totals. The right-hand side may contain: 1 for column totals, N for counts, Percent() for cell percentages, Percent(“row”) for row percentages, Percent(“col”) for column percentages.\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nsparse_header\n\n\nTRUE or FALSE. TRUE eliminates column headers which have a unique label across all columns, except for the row immediately above the data. FALSE keeps all headers. The order in which terms are entered in the formula determines the order in which headers appear. For example, x~mean*z will print the mean-related header above the z-related header.’\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\ndatasummary_crosstab is a wrapper around the datasummary function. This wrapper works by creating a customized formula and by feeding it to datasummary. The customized formula comes in two parts.\nFirst, we take a two-sided formula supplied by the formula argument. All variables of that formula are wrapped in a Factor() call to ensure that the variables are treated as categorical.\nSecond, the statistic argument gives a two-sided formula which specifies the statistics to include in the table. datasummary_crosstab modifies this formula automatically to include \"clean\" labels.\nFinally, the formula and statistic formulas are combined into a single formula which is fed directly to the datasummary function to produce the table.\nVariables in formula are automatically wrapped in Factor().\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# crosstab of two variables, showing counts, row percentages, and row/column totals\ndatasummary_crosstab(cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_5jo7uscatnl9bo050jwr\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n4  \n                  N    \n                  1   \n                  8   \n                  2   \n                  11   \n                \n\n   \n                  % row\n                  9.1 \n                  72.7\n                  18.2\n                  100.0\n                \n\n6  \n                  N    \n                  2   \n                  4   \n                  1   \n                  7    \n                \n\n   \n                  % row\n                  28.6\n                  57.1\n                  14.3\n                  100.0\n                \n\n8  \n                  N    \n                  12  \n                  0   \n                  2   \n                  14   \n                \n\n   \n                  % row\n                  85.7\n                  0.0 \n                  14.3\n                  100.0\n                \n\nAll\n                  N    \n                  15  \n                  12  \n                  5   \n                  32   \n                \n\n   \n                  % row\n                  46.9\n                  37.5\n                  15.6\n                  100.0\n                \n\n\n\n\n    \n\n# crosstab of two variables, showing counts only and no totals\ndatasummary_crosstab(cyl ~ gear, statistic = ~ N, data = mtcars)\n\n \n\n  \n    \n\ntinytable_rg2ln5m4hvlf9490t7zi\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n              \n\n\n4\n                  N\n                  1 \n                  8\n                  2\n                \n\n6\n                  N\n                  2 \n                  4\n                  1\n                \n\n8\n                  N\n                  12\n                  0\n                  2\n                \n\n\n\n\n    \n\n# crosstab of three variables\n  datasummary_crosstab(am * cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_t3qllvgnzo3q4mc43anw\n\n\n      \n\nam\n                cyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n0\n                  4  \n                  N    \n                  1    \n                  2   \n                  0    \n                  3    \n                \n\n \n                     \n                  % row\n                  33.3 \n                  66.7\n                  0.0  \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  2    \n                  2   \n                  0    \n                  4    \n                \n\n \n                     \n                  % row\n                  50.0 \n                  50.0\n                  0.0  \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  12   \n                  0   \n                  0    \n                  12   \n                \n\n \n                     \n                  % row\n                  100.0\n                  0.0 \n                  0.0  \n                  100.0\n                \n\n1\n                  4  \n                  N    \n                  0    \n                  6   \n                  2    \n                  8    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  75.0\n                  25.0 \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  0    \n                  2   \n                  1    \n                  3    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  66.7\n                  33.3 \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  0    \n                  0   \n                  2    \n                  2    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  0.0 \n                  100.0\n                  100.0\n                \n\n \n                  All\n                  N    \n                  15   \n                  12  \n                  5    \n                  32   \n                \n\n \n                     \n                  % row\n                  46.9 \n                  37.5\n                  15.6 \n                  100.0\n                \n\n\n\n\n    \n\n# crosstab with two variables and column percentages \ndatasummary_crosstab(am ~ gear, statistic = ~ Percent(\"col\"), data = mtcars)\n\n \n\n  \n    \n\ntinytable_4me5sif2mhvkh3gw6o2s\n\n\n      \n\nam\n                 \n                3\n                4\n                5\n              \n\n\n0\n                  % col\n                  100.0\n                  33.3\n                  0.0  \n                \n\n1\n                  % col\n                  0.0  \n                  66.7\n                  100.0",
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+    "text": "Example: Using facets to compare models\nCustomizing plots with ggplot2 makes modelplot very flexible. For example, imagine you want to compare the coefficients of three models with different dependent variables. First, we load the packages and estimate our models:\n\nlibrary(ggplot2)\nlibrary(modelsummary)\n\nmodels &lt;- list(\n  lm(vs ~ carb + mpg + cyl, data = mtcars),\n  lm(disp ~ carb + mpg + cyl, data = mtcars),\n  lm(hp ~ carb + mpg + cyl, data = mtcars))\n\nThen, we use the dvnames function to rename our list with names matching the the dependent variable in each model:\n\nmodels &lt;- dvnames(models)\n\nBy calling modelplot with the draw=FALSE argument, we see the raw data used to draw the plot. Here, we see that there is a model column:\n\nmodelplot(models, draw = FALSE)\n\n          term model     estimate    std.error      conf.low    conf.high      p.value\n1  (Intercept)    vs   2.41742511   0.67622094    1.03224931   3.80260091 1.296718e-03\n5  (Intercept)  disp 112.57276339 114.86315481 -122.71374324 347.85927003 3.354494e-01\n9  (Intercept)    hp -10.56116383  68.75946117 -151.40853516 130.28620751 8.790301e-01\n2         carb    vs  -0.06945116   0.03943402   -0.15022810   0.01132577 8.912324e-02\n6         carb  disp -12.30144724   6.69827859  -26.02224894   1.41935446 7.692105e-02\n10        carb    hp  17.75593287   4.00972816    9.54237706  25.96948867 1.320972e-04\n3          mpg    vs  -0.01513960   0.01716410   -0.05029868   0.02001947 3.852593e-01\n7          mpg  disp  -7.14964651   2.91550156  -13.12178072  -1.17751230 2.068858e-02\n11         mpg    hp  -1.00486469   1.74527956   -4.57990780   2.57017842 5.693755e-01\n4          cyl    vs  -0.23926135   0.05687969   -0.35577411  -0.12274859 2.410214e-04\n8          cyl  disp  47.90105842   9.66160634   28.11015499  67.69196184 3.111898e-05\n12         cyl    hp  20.60581208   5.78363747    8.75856779  32.45305638 1.338485e-03\n\n\nFinally, we use the model column as our identifier in ggplot2’s facet_grid command to display models side by side:\n\nmodelplot(models, color = \"black\") + facet_grid(~model)",
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-    "text": "By default, this data frame is passed to the ‘gof_map’ argument of the ‘modelsummary’ function. Users can modify this data frame to customize the list of statistics to display and their format. See example below.\n\ngof_map\n\n\ndata.frame with 4 columns of character data: raw, clean, fmt, omit\n\n\nlibrary(modelsummary)\n\nif (identical(Sys.getenv(\"pkgdown\"),  \"true\")) {\n\nlibrary(modelsummary)\nmod &lt;- lm(wt ~ drat, data = mtcars)\ngm &lt;- modelsummary::gof_map\ngm$omit[gm$raw == 'deviance'] &lt;- FALSE\ngm$fmt[gm$raw == 'r.squared'] &lt;- \"%.5f\"\nmodelsummary(mod, gof_map = gm)\n}",
+    "text": "Citation\nTo cite modelsummary in publications use:\n\n  Arel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.”\n  _Journal of Statistical Software_, *103*(1), 1-23.\n  doi:10.18637/jss.v103.i01 &lt;https://doi.org/10.18637/jss.v103.i01&gt;.\n\nA BibTeX entry for LaTeX users is\n\n  @Article{,\n    title = {{modelsummary}: Data and Model Summaries in {R}},\n    author = {Vincent Arel-Bundock},\n    journal = {Journal of Statistical Software},\n    year = {2022},\n    volume = {103},\n    number = {1},\n    pages = {1--23},\n    doi = {10.18637/jss.v103.i01},\n  }",
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-    "text": "By default, this data frame is passed to the ‘gof_map’ argument of the ‘modelsummary’ function. Users can modify this data frame to customize the list of statistics to display and their format. See example below.\n\ngof_map\n\n\ndata.frame with 4 columns of character data: raw, clean, fmt, omit\n\n\nlibrary(modelsummary)\n\nif (identical(Sys.getenv(\"pkgdown\"),  \"true\")) {\n\nlibrary(modelsummary)\nmod &lt;- lm(wt ~ drat, data = mtcars)\ngm &lt;- modelsummary::gof_map\ngm$omit[gm$raw == 'deviance'] &lt;- FALSE\ngm$fmt[gm$raw == 'r.squared'] &lt;- \"%.5f\"\nmodelsummary(mod, gof_map = gm)\n}",
+    "text": "Update modelsummary and its dependencies to the latest R-Universe or CRAN versions. The R session needs to be restarted after install.\n\n\n\nupdate_modelsummary(source = \"development\")\n\n\n\n\n\n\n\nsource\n\n\none of two strings: \"development\" or \"cran\"",
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-    "text": "A unified approach to extract results from a wide variety of models. For some models get_estimates attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_estimates(\n  model,\n  conf_level = 0.95,\n  vcov = NULL,\n  shape = NULL,\n  coef_rename = FALSE,\n  ...\n)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.",
+    "text": "Update modelsummary and its dependencies to the latest R-Universe or CRAN versions. The R session needs to be restarted after install.\n\n\n\nupdate_modelsummary(source = \"development\")\n\n\n\n\n\n\n\nsource\n\n\none of two strings: \"development\" or \"cran\"",
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-    "text": "A unified approach to extract results from a wide variety of models. For some models get_estimates attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_estimates(\n  model,\n  conf_level = 0.95,\n  vcov = NULL,\n  shape = NULL,\n  coef_rename = FALSE,\n  ...\n)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.",
+    "text": "Rounding using scientific notation\n\n\n\nfmt_sci(digits = 3, ...)\n\n\n\n\n\n\n\ndigits\n\n\na positive integer indicating how many significant digits are to be used for numeric and complex x.\n\n\n\n\n…\n\n\nadditional arguments passed to format().",
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     "text": "Rounding using scientific notation\n\n\n\nfmt_sci(digits = 3, ...)\n\n\n\n\n\n\n\ndigits\n\n\na positive integer indicating how many significant digits are to be used for numeric and complex x.\n\n\n\n\n…\n\n\nadditional arguments passed to format().",
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-    "text": "Rounding using scientific notation\n\n\n\nfmt_sci(digits = 3, ...)\n\n\n\n\n\n\n\ndigits\n\n\na positive integer indicating how many significant digits are to be used for numeric and complex x.\n\n\n\n\n…\n\n\nadditional arguments passed to format().",
+    "text": "Summary tables using 2-sided formulae: crosstabs, frequencies, table 1s and more.\nDescription\ndatasummary can use any summary function which produces one numeric or character value per variable. The examples section of this documentation shows how to define custom summary functions.\nmodelsummary also supplies several shortcut summary functions which can be used in datasummary() formulas: Min, Max, Mean, Median, Var, SD, NPercent, NUnique, Ncol, P0, P25, P50, P75, P100.\nSee the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\nUsage\ndatasummary(\n  formula,\n  data,\n  output = \"default\",\n  fmt = 2,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  sparse_header = TRUE,\n  escape = TRUE,\n  ...\n)\n\nArguments\n\n\n\nformula\n\n\nA two-sided formula to describe the table: rows ~ columns. See the Examples section for a mini-tutorial and the Details section for more resources. Grouping/nesting variables can appear on both sides of the formula, but all summary functions must be on one side.\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nsparse_header\n\n\nTRUE or FALSE. TRUE eliminates column headers which have a unique label across all columns, except for the row immediately above the data. FALSE keeps all headers. The order in which terms are entered in the formula determines the order in which headers appear. For example, x~mean*z will print the mean-related header above the z-related header.’\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\nDetails\nVisit the ‘modelsummary’ website for more usage examples: https://modelsummary.com\nThe ‘datasummary’ function is a thin wrapper around the ‘tabular’ function from the ‘tables’ package. More details about table-making formulas can be found in the ‘tables’ package documentation: ?tables::tabular\nHierarchical or \"nested\" column labels are only available for these output formats: tinytable, kableExtra, gt, html, rtf, and LaTeX. When saving tables to other formats, nested labels will be combined to a \"flat\" header.\nGlobal Options\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nModel labels: default column names\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nTable-making packages\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nTable themes\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nModel extraction functions\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nFormatting numeric entries\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nLaTeX preamble\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\nExamples\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# The left-hand side of the formula describes rows, and the right-hand side\n# describes columns. This table uses the \"mpg\" variable as a row and the \"mean\"\n# function as a column:\n\ndatasummary(mpg ~ mean, data = mtcars)\n\n \n\n  \n    \n\ntinytable_cb8fc5hsp369x94z5o0a\n\n\n      \n\n \n                mean\n              \n\nmpg\n                  20.09\n                \n\n\n\n    \n\n# This table uses the \"mean\" function as a row and the \"mpg\" variable as a column:\n\ndatasummary(mean ~ mpg, data = mtcars)\n\n \n\n  \n    \n\ntinytable_mgqzua34nrdbo0v0heu9\n\n\n      \n\n \n                mpg\n              \n\nmean\n                  20.09\n                \n\n\n\n    \n\n# Display several variables or functions of the data using the \"+\"\n# concatenation operator. This table has 2 rows and 2 columns:\n\ndatasummary(hp + mpg ~ mean + sd, data = mtcars)\n\n \n\n  \n    \n\ntinytable_ydb1f98fs4e1zy5fytpa\n\n\n      \n\n \n                mean\n                sd\n              \n\n\nhp \n                  146.69\n                  68.56\n                \n\nmpg\n                  20.09 \n                  6.03 \n                \n\n\n\n\n    \n\n# Nest variables or statistics inside a \"factor\" variable using the \"*\" nesting\n# operator. This table shows the mean of \"hp\" and \"mpg\" for each value of\n# \"cyl\":\n\nmtcars$cyl &lt;- as.factor(mtcars$cyl)\ndatasummary(hp + mpg ~ cyl * mean, data = mtcars)\n\n \n\n  \n    \n\ntinytable_6ex7dqxtss02tkvvzr3x\n\n\n      \n\n \n                4\n                6\n                8\n              \n\n\nhp \n                  82.64\n                  122.29\n                  209.21\n                \n\nmpg\n                  26.66\n                  19.74 \n                  15.10 \n                \n\n\n\n\n    \n\n# If you don't want to convert your original data\n# to factors, you can use the 'Factor()'\n# function inside 'datasummary' to obtain an identical result:\n\ndatasummary(hp + mpg ~ Factor(cyl) * mean, data = mtcars)\n\n \n\n  \n    \n\ntinytable_l52r23bymea3u35ybhe9\n\n\n      \n\n \n                4\n                6\n                8\n              \n\n\nhp \n                  82.64\n                  122.29\n                  209.21\n                \n\nmpg\n                  26.66\n                  19.74 \n                  15.10 \n                \n\n\n\n\n    \n\n# You can nest several variables or statistics inside a factor by using\n# parentheses. This table shows the mean and the standard deviation for each\n# subset of \"cyl\":\n\ndatasummary(hp + mpg ~ cyl * (mean + sd), data = mtcars)\n\n \n\n  \n    \n\ntinytable_7aa4ffdw2msezxtwh3hg\n\n\n      \n\n\n \n4\n6\n8\n\n\n \n                mean\n                sd\n                mean\n                sd\n                mean\n                sd\n              \n\n\n\nhp \n                  82.64\n                  20.93\n                  122.29\n                  24.26\n                  209.21\n                  50.98\n                \n\nmpg\n                  26.66\n                  4.51 \n                  19.74 \n                  1.45 \n                  15.10 \n                  2.56 \n                \n\n\n\n\n    \n\n# Summarize all numeric variables with 'All()'\ndatasummary(All(mtcars) ~ mean + sd, data = mtcars)\n\n \n\n  \n    \n\ntinytable_pawkzn9g4hyhhyucmnml\n\n\n      \n\n \n                mean\n                sd\n              \n\n\nmpg \n                  20.09 \n                  6.03  \n                \n\ndisp\n                  230.72\n                  123.94\n                \n\nhp  \n                  146.69\n                  68.56 \n                \n\ndrat\n                  3.60  \n                  0.53  \n                \n\nwt  \n                  3.22  \n                  0.98  \n                \n\nqsec\n                  17.85 \n                  1.79  \n                \n\nvs  \n                  0.44  \n                  0.50  \n                \n\nam  \n                  0.41  \n                  0.50  \n                \n\ngear\n                  3.69  \n                  0.74  \n                \n\ncarb\n                  2.81  \n                  1.62  \n                \n\n\n\n\n    \n\n# Define custom summary statistics. Your custom function should accept a vector\n# of numeric values and return a single numeric or string value:\n\nminmax &lt;- function(x) sprintf(\"[%.2f, %.2f]\", min(x), max(x))\nmean_na &lt;- function(x) mean(x, na.rm = TRUE)\n\ndatasummary(hp + mpg ~ minmax + mean_na, data = mtcars)\n\n \n\n  \n    \n\ntinytable_4d9z84cto2ud51troo8a\n\n\n      \n\n \n                minmax\n                mean_na\n              \n\n\nhp \n                  [52.00, 335.00]\n                  146.69\n                \n\nmpg\n                  [10.40, 33.90] \n                  20.09 \n                \n\n\n\n\n    \n\n# To handle missing values, you can pass arguments to your functions using\n# '*Arguments()'\n\ndatasummary(hp + mpg ~ mean * Arguments(na.rm = TRUE), data = mtcars)\n\n \n\n  \n    \n\ntinytable_m2i8k8eiy3qx5in92i21\n\n\n      \n\n \n                mean\n              \n\n\nhp \n                  146.69\n                \n\nmpg\n                  20.09 \n                \n\n\n\n\n    \n\n# For convenience, 'modelsummary' supplies several convenience functions\n# with the argument `na.rm=TRUE` by default: Mean, Median, Min, Max, SD, Var,\n# P0, P25, P50, P75, P100, NUnique, Histogram\n\n#datasummary(hp + mpg ~ Mean + SD + Histogram, data = mtcars)\n\n# These functions also accept a 'fmt' argument which allows you to\n# round/format the results\n\ndatasummary(hp + mpg ~ Mean * Arguments(fmt = \"%.3f\") + SD * Arguments(fmt = \"%.1f\"), data = mtcars)\n\n \n\n  \n    \n\ntinytable_e7pur5j2cizht4r7vfbd\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nhp \n                  146.688\n                  68.6\n                \n\nmpg\n                  20.091 \n                  6.0 \n                \n\n\n\n\n    \n\n# Save your tables to a variety of output formats:\nf &lt;- hp + mpg ~ Mean + SD\n#datasummary(f, data = mtcars, output = 'table.html')\n#datasummary(f, data = mtcars, output = 'table.tex')\n#datasummary(f, data = mtcars, output = 'table.md')\n#datasummary(f, data = mtcars, output = 'table.docx')\n#datasummary(f, data = mtcars, output = 'table.pptx')\n#datasummary(f, data = mtcars, output = 'table.jpg')\n#datasummary(f, data = mtcars, output = 'table.png')\n\n# Display human-readable code\n#datasummary(f, data = mtcars, output = 'html')\n#datasummary(f, data = mtcars, output = 'markdown')\n#datasummary(f, data = mtcars, output = 'latex')\n\n# Return a table object to customize using a table-making package\n#datasummary(f, data = mtcars, output = 'tinytable')\n#datasummary(f, data = mtcars, output = 'gt')\n#datasummary(f, data = mtcars, output = 'kableExtra')\n#datasummary(f, data = mtcars, output = 'flextable')\n#datasummary(f, data = mtcars, output = 'huxtable')\n\n# add_rows\nnew_rows &lt;- data.frame(a = 1:2, b = 2:3, c = 4:5)\nattr(new_rows, 'position') &lt;- c(1, 3)\ndatasummary(mpg + hp ~ mean + sd, data = mtcars, add_rows = new_rows)\n\n \n\n  \n    \n\ntinytable_vzhxuj0m159outzpq0j8\n\n\n      \n\n \n                mean\n                sd\n              \n\n\n1.00\n                  2.00  \n                  4.00 \n                \n\nmpg \n                  20.09 \n                  6.03 \n                \n\n2.00\n                  3.00  \n                  5.00 \n                \n\nhp  \n                  146.69\n                  68.56",
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-    "text": "The number of decimal digits to keep after the decimal is assessed\n\n\n\nfmt_significant(digits = 3, ...)\n\n\n\n\n\n\n\ndigits\n\n\nNumber of significant digits to keep.\n\n\n\n\n…\n\n\nAdditional arguments are passed to the format() function (e.g., big.marks, scientific). See ?format",
+    "text": "Convenience function to tabulate counts, cell percentages, and row/column percentages for categorical variables. See the Details section for a description of the internal design. For more complex cross tabulations, use datasummary directly. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_crosstab(\n  formula,\n  statistic = 1 ~ 1 + N + Percent(\"row\"),\n  data,\n  output = \"default\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  sparse_header = TRUE,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nformula\n\n\nA two-sided formula to describe the table: rows ~ columns, where rows and columns are variables in the data. Rows and columns may contain interactions, e.g., var1 * var2 ~ var3.\n\n\n\n\nstatistic\n\n\nA formula of the form 1 ~ 1 + N + Percent(“row”). The left-hand side may only be empty or contain a 1 to include row totals. The right-hand side may contain: 1 for column totals, N for counts, Percent() for cell percentages, Percent(“row”) for row percentages, Percent(“col”) for column percentages.\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nsparse_header\n\n\nTRUE or FALSE. TRUE eliminates column headers which have a unique label across all columns, except for the row immediately above the data. FALSE keeps all headers. The order in which terms are entered in the formula determines the order in which headers appear. For example, x~mean*z will print the mean-related header above the z-related header.’\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\ndatasummary_crosstab is a wrapper around the datasummary function. This wrapper works by creating a customized formula and by feeding it to datasummary. The customized formula comes in two parts.\nFirst, we take a two-sided formula supplied by the formula argument. All variables of that formula are wrapped in a Factor() call to ensure that the variables are treated as categorical.\nSecond, the statistic argument gives a two-sided formula which specifies the statistics to include in the table. datasummary_crosstab modifies this formula automatically to include \"clean\" labels.\nFinally, the formula and statistic formulas are combined into a single formula which is fed directly to the datasummary function to produce the table.\nVariables in formula are automatically wrapped in Factor().\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# crosstab of two variables, showing counts, row percentages, and row/column totals\ndatasummary_crosstab(cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_lf0vwp3ves3ldpvhgfhj\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n4  \n                  N    \n                  1   \n                  8   \n                  2   \n                  11   \n                \n\n   \n                  % row\n                  9.1 \n                  72.7\n                  18.2\n                  100.0\n                \n\n6  \n                  N    \n                  2   \n                  4   \n                  1   \n                  7    \n                \n\n   \n                  % row\n                  28.6\n                  57.1\n                  14.3\n                  100.0\n                \n\n8  \n                  N    \n                  12  \n                  0   \n                  2   \n                  14   \n                \n\n   \n                  % row\n                  85.7\n                  0.0 \n                  14.3\n                  100.0\n                \n\nAll\n                  N    \n                  15  \n                  12  \n                  5   \n                  32   \n                \n\n   \n                  % row\n                  46.9\n                  37.5\n                  15.6\n                  100.0\n                \n\n\n\n\n    \n\n# crosstab of two variables, showing counts only and no totals\ndatasummary_crosstab(cyl ~ gear, statistic = ~ N, data = mtcars)\n\n \n\n  \n    \n\ntinytable_663m5e7737wydockz4f6\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n              \n\n\n4\n                  N\n                  1 \n                  8\n                  2\n                \n\n6\n                  N\n                  2 \n                  4\n                  1\n                \n\n8\n                  N\n                  12\n                  0\n                  2\n                \n\n\n\n\n    \n\n# crosstab of three variables\n  datasummary_crosstab(am * cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_6aeigmuvtiy8r06xetaa\n\n\n      \n\nam\n                cyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n0\n                  4  \n                  N    \n                  1    \n                  2   \n                  0    \n                  3    \n                \n\n \n                     \n                  % row\n                  33.3 \n                  66.7\n                  0.0  \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  2    \n                  2   \n                  0    \n                  4    \n                \n\n \n                     \n                  % row\n                  50.0 \n                  50.0\n                  0.0  \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  12   \n                  0   \n                  0    \n                  12   \n                \n\n \n                     \n                  % row\n                  100.0\n                  0.0 \n                  0.0  \n                  100.0\n                \n\n1\n                  4  \n                  N    \n                  0    \n                  6   \n                  2    \n                  8    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  75.0\n                  25.0 \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  0    \n                  2   \n                  1    \n                  3    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  66.7\n                  33.3 \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  0    \n                  0   \n                  2    \n                  2    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  0.0 \n                  100.0\n                  100.0\n                \n\n \n                  All\n                  N    \n                  15   \n                  12  \n                  5    \n                  32   \n                \n\n \n                     \n                  % row\n                  46.9 \n                  37.5\n                  15.6 \n                  100.0\n                \n\n\n\n\n    \n\n# crosstab with two variables and column percentages \ndatasummary_crosstab(am ~ gear, statistic = ~ Percent(\"col\"), data = mtcars)\n\n \n\n  \n    \n\ntinytable_a07ln2dlnp2nawgi6t0p\n\n\n      \n\nam\n                 \n                3\n                4\n                5\n              \n\n\n0\n                  % col\n                  100.0\n                  33.3\n                  0.0  \n                \n\n1\n                  % col\n                  0.0  \n                  66.7\n                  100.0",
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-    "text": "The number of decimal digits to keep after the decimal is assessed\n\n\n\nfmt_significant(digits = 3, ...)\n\n\n\n\n\n\n\ndigits\n\n\nNumber of significant digits to keep.\n\n\n\n\n…\n\n\nAdditional arguments are passed to the format() function (e.g., big.marks, scientific). See ?format",
+    "text": "Convenience function to tabulate counts, cell percentages, and row/column percentages for categorical variables. See the Details section for a description of the internal design. For more complex cross tabulations, use datasummary directly. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_crosstab(\n  formula,\n  statistic = 1 ~ 1 + N + Percent(\"row\"),\n  data,\n  output = \"default\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  sparse_header = TRUE,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nformula\n\n\nA two-sided formula to describe the table: rows ~ columns, where rows and columns are variables in the data. Rows and columns may contain interactions, e.g., var1 * var2 ~ var3.\n\n\n\n\nstatistic\n\n\nA formula of the form 1 ~ 1 + N + Percent(“row”). The left-hand side may only be empty or contain a 1 to include row totals. The right-hand side may contain: 1 for column totals, N for counts, Percent() for cell percentages, Percent(“row”) for row percentages, Percent(“col”) for column percentages.\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nsparse_header\n\n\nTRUE or FALSE. TRUE eliminates column headers which have a unique label across all columns, except for the row immediately above the data. FALSE keeps all headers. The order in which terms are entered in the formula determines the order in which headers appear. For example, x~mean*z will print the mean-related header above the z-related header.’\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\ndatasummary_crosstab is a wrapper around the datasummary function. This wrapper works by creating a customized formula and by feeding it to datasummary. The customized formula comes in two parts.\nFirst, we take a two-sided formula supplied by the formula argument. All variables of that formula are wrapped in a Factor() call to ensure that the variables are treated as categorical.\nSecond, the statistic argument gives a two-sided formula which specifies the statistics to include in the table. datasummary_crosstab modifies this formula automatically to include \"clean\" labels.\nFinally, the formula and statistic formulas are combined into a single formula which is fed directly to the datasummary function to produce the table.\nVariables in formula are automatically wrapped in Factor().\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# crosstab of two variables, showing counts, row percentages, and row/column totals\ndatasummary_crosstab(cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_lf0vwp3ves3ldpvhgfhj\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n4  \n                  N    \n                  1   \n                  8   \n                  2   \n                  11   \n                \n\n   \n                  % row\n                  9.1 \n                  72.7\n                  18.2\n                  100.0\n                \n\n6  \n                  N    \n                  2   \n                  4   \n                  1   \n                  7    \n                \n\n   \n                  % row\n                  28.6\n                  57.1\n                  14.3\n                  100.0\n                \n\n8  \n                  N    \n                  12  \n                  0   \n                  2   \n                  14   \n                \n\n   \n                  % row\n                  85.7\n                  0.0 \n                  14.3\n                  100.0\n                \n\nAll\n                  N    \n                  15  \n                  12  \n                  5   \n                  32   \n                \n\n   \n                  % row\n                  46.9\n                  37.5\n                  15.6\n                  100.0\n                \n\n\n\n\n    \n\n# crosstab of two variables, showing counts only and no totals\ndatasummary_crosstab(cyl ~ gear, statistic = ~ N, data = mtcars)\n\n \n\n  \n    \n\ntinytable_663m5e7737wydockz4f6\n\n\n      \n\ncyl\n                 \n                3\n                4\n                5\n              \n\n\n4\n                  N\n                  1 \n                  8\n                  2\n                \n\n6\n                  N\n                  2 \n                  4\n                  1\n                \n\n8\n                  N\n                  12\n                  0\n                  2\n                \n\n\n\n\n    \n\n# crosstab of three variables\n  datasummary_crosstab(am * cyl ~ gear, data = mtcars)\n\n \n\n  \n    \n\ntinytable_6aeigmuvtiy8r06xetaa\n\n\n      \n\nam\n                cyl\n                 \n                3\n                4\n                5\n                All\n              \n\n\n0\n                  4  \n                  N    \n                  1    \n                  2   \n                  0    \n                  3    \n                \n\n \n                     \n                  % row\n                  33.3 \n                  66.7\n                  0.0  \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  2    \n                  2   \n                  0    \n                  4    \n                \n\n \n                     \n                  % row\n                  50.0 \n                  50.0\n                  0.0  \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  12   \n                  0   \n                  0    \n                  12   \n                \n\n \n                     \n                  % row\n                  100.0\n                  0.0 \n                  0.0  \n                  100.0\n                \n\n1\n                  4  \n                  N    \n                  0    \n                  6   \n                  2    \n                  8    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  75.0\n                  25.0 \n                  100.0\n                \n\n \n                  6  \n                  N    \n                  0    \n                  2   \n                  1    \n                  3    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  66.7\n                  33.3 \n                  100.0\n                \n\n \n                  8  \n                  N    \n                  0    \n                  0   \n                  2    \n                  2    \n                \n\n \n                     \n                  % row\n                  0.0  \n                  0.0 \n                  100.0\n                  100.0\n                \n\n \n                  All\n                  N    \n                  15   \n                  12  \n                  5    \n                  32   \n                \n\n \n                     \n                  % row\n                  46.9 \n                  37.5\n                  15.6 \n                  100.0\n                \n\n\n\n\n    \n\n# crosstab with two variables and column percentages \ndatasummary_crosstab(am ~ gear, statistic = ~ Percent(\"col\"), data = mtcars)\n\n \n\n  \n    \n\ntinytable_a07ln2dlnp2nawgi6t0p\n\n\n      \n\nam\n                 \n                3\n                4\n                5\n              \n\n\n0\n                  % col\n                  100.0\n                  33.3\n                  0.0  \n                \n\n1\n                  % col\n                  0.0  \n                  66.7\n                  100.0",
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-    "text": "The names of the variables displayed in the correlation table are the names of the columns in the data. You can rename those columns (with or without spaces) to produce a table of human-readable variables. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_correlation(\n  data,\n  output = \"default\",\n  method = \"pearson\",\n  fmt = 2,\n  align = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  title = NULL,\n  notes = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nmethod\n\n\ncharacter or function\n\n\ncharacter: \"pearson\", \"kendall\", \"spearman\", or \"pearspear\" (Pearson correlations above and Spearman correlations below the diagonal)\n\n\nfunction: takes a data.frame with numeric columns and returns a square matrix or data.frame with unique row.names and colnames corresponding to variable names. Note that the datasummary_correlation_format can often be useful for formatting the output of custom correlation functions.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nother parameters are passed through to the table-making packages.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# clean variable names (base R)\ndat &lt;- mtcars[, c(\"mpg\", \"hp\")]\ncolnames(dat) &lt;- c(\"Miles / Gallon\", \"Horse Power\")\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_uxumo5cevz6yt8nzjbqo\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# clean variable names (tidyverse)\nlibrary(tidyverse)\ndat &lt;- mtcars %&gt;%\n  select(`Miles / Gallon` = mpg,\n         `Horse Power` = hp)\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_0lq7vw69y2dma1zk5jm1\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# alternative methods\ndatasummary_correlation(dat, method = \"pearspear\")\n\n \n\n  \n    \n\ntinytable_m8s2wmb2x469c6omtj5w\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  -.78\n                \n\nHorse Power   \n                  -.89\n                  1   \n                \n\n\n\n\n    \n\n# custom function\ncor_fun &lt;- function(x) cor(x, method = \"kendall\")\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_p4tpm2o5kg4lqqcba3gf\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1.00\n                  -.74\n                \n\nHorse Power   \n                  -.74\n                  1.00\n                \n\n\n\n\n    \n\n# rename columns alphabetically and include a footnote for reference\nnote &lt;- sprintf(\"(%s) %s\", letters[1:ncol(dat)], colnames(dat))\nnote &lt;- paste(note, collapse = \"; \")\n\ncolnames(dat) &lt;- sprintf(\"(%s)\", letters[1:ncol(dat)])\n\ndatasummary_correlation(dat, notes = note)\n\n \n\n  \n    \n\ntinytable_589y8mj80a8n4892byot\n\n\n      \n\n \n                (a)\n                (b)\n              \n(a) Miles / Gallon; (b) Horse Power\n\n\n(a)\n                  1   \n                  .\n                \n\n(b)\n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# `datasummary_correlation_format`: custom function with formatting\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_geji1n86vqcdz133icad\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .\n                \n\n\n\n\n    \n\n# use kableExtra and psych to color significant cells\nlibrary(psych)\nlibrary(kableExtra)\n\ndat &lt;- mtcars[, c(\"vs\", \"hp\", \"gear\")]\n\ncor_fun &lt;- function(dat) {\n  # compute correlations and format them\n  correlations &lt;- data.frame(cor(dat))\n  correlations &lt;- datasummary_correlation_format(correlations, fmt = 2)\n\n  # calculate pvalues using the `psych` package\n  pvalues &lt;- psych::corr.test(dat)$p\n\n  # use `kableExtra::cell_spec` to color significant cells\n  for (i in 1:nrow(correlations)) {\n    for (j in 1:ncol(correlations)) {\n      if (pvalues[i, j] &lt; 0.05 && i != j) {\n        correlations[i, j] &lt;- cell_spec(correlations[i, j], background = \"pink\")\n      }\n    }\n  }\n  return(correlations)\n}\n\n# The `escape=FALSE` is important here!\ndatasummary_correlation(dat, method = cor_fun, escape = FALSE)\n\n \n\n  \n    \n\ntinytable_zm9d2mpq2l3qj3jk0kjo\n\n\n      \n\n \n                vs\n                hp\n                gear\n              \n\n\nvs  \n                  1.00                                                                                                                         \n                  -.72\n                  .21 \n                \n\nhp  \n                  -.72\n                  1.00                                                                                                                         \n                  -.13\n                \n\ngear\n                  .21                                                                                                                          \n                  -.13                                                                                                                         \n                  1.00",
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-    "text": "The names of the variables displayed in the correlation table are the names of the columns in the data. You can rename those columns (with or without spaces) to produce a table of human-readable variables. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_correlation(\n  data,\n  output = \"default\",\n  method = \"pearson\",\n  fmt = 2,\n  align = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  title = NULL,\n  notes = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nmethod\n\n\ncharacter or function\n\n\ncharacter: \"pearson\", \"kendall\", \"spearman\", or \"pearspear\" (Pearson correlations above and Spearman correlations below the diagonal)\n\n\nfunction: takes a data.frame with numeric columns and returns a square matrix or data.frame with unique row.names and colnames corresponding to variable names. Note that the datasummary_correlation_format can often be useful for formatting the output of custom correlation functions.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nother parameters are passed through to the table-making packages.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# clean variable names (base R)\ndat &lt;- mtcars[, c(\"mpg\", \"hp\")]\ncolnames(dat) &lt;- c(\"Miles / Gallon\", \"Horse Power\")\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_uxumo5cevz6yt8nzjbqo\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# clean variable names (tidyverse)\nlibrary(tidyverse)\ndat &lt;- mtcars %&gt;%\n  select(`Miles / Gallon` = mpg,\n         `Horse Power` = hp)\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_0lq7vw69y2dma1zk5jm1\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# alternative methods\ndatasummary_correlation(dat, method = \"pearspear\")\n\n \n\n  \n    \n\ntinytable_m8s2wmb2x469c6omtj5w\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  -.78\n                \n\nHorse Power   \n                  -.89\n                  1   \n                \n\n\n\n\n    \n\n# custom function\ncor_fun &lt;- function(x) cor(x, method = \"kendall\")\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_p4tpm2o5kg4lqqcba3gf\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1.00\n                  -.74\n                \n\nHorse Power   \n                  -.74\n                  1.00\n                \n\n\n\n\n    \n\n# rename columns alphabetically and include a footnote for reference\nnote &lt;- sprintf(\"(%s) %s\", letters[1:ncol(dat)], colnames(dat))\nnote &lt;- paste(note, collapse = \"; \")\n\ncolnames(dat) &lt;- sprintf(\"(%s)\", letters[1:ncol(dat)])\n\ndatasummary_correlation(dat, notes = note)\n\n \n\n  \n    \n\ntinytable_589y8mj80a8n4892byot\n\n\n      \n\n \n                (a)\n                (b)\n              \n(a) Miles / Gallon; (b) Horse Power\n\n\n(a)\n                  1   \n                  .\n                \n\n(b)\n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# `datasummary_correlation_format`: custom function with formatting\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_geji1n86vqcdz133icad\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .\n                \n\n\n\n\n    \n\n# use kableExtra and psych to color significant cells\nlibrary(psych)\nlibrary(kableExtra)\n\ndat &lt;- mtcars[, c(\"vs\", \"hp\", \"gear\")]\n\ncor_fun &lt;- function(dat) {\n  # compute correlations and format them\n  correlations &lt;- data.frame(cor(dat))\n  correlations &lt;- datasummary_correlation_format(correlations, fmt = 2)\n\n  # calculate pvalues using the `psych` package\n  pvalues &lt;- psych::corr.test(dat)$p\n\n  # use `kableExtra::cell_spec` to color significant cells\n  for (i in 1:nrow(correlations)) {\n    for (j in 1:ncol(correlations)) {\n      if (pvalues[i, j] &lt; 0.05 && i != j) {\n        correlations[i, j] &lt;- cell_spec(correlations[i, j], background = \"pink\")\n      }\n    }\n  }\n  return(correlations)\n}\n\n# The `escape=FALSE` is important here!\ndatasummary_correlation(dat, method = cor_fun, escape = FALSE)\n\n \n\n  \n    \n\ntinytable_zm9d2mpq2l3qj3jk0kjo\n\n\n      \n\n \n                vs\n                hp\n                gear\n              \n\n\nvs  \n                  1.00                                                                                                                         \n                  -.72\n                  .21 \n                \n\nhp  \n                  -.72\n                  1.00                                                                                                                         \n                  -.13\n                \n\ngear\n                  .21                                                                                                                          \n                  -.13                                                                                                                         \n                  1.00",
+    "text": "Rounding with decimal digits on a per-statistic basis in the fmt argument for modelsummary()\n\n\n\nfmt_statistic(..., default = 3)\n\n\n\n\n\n\n\n…\n\n\nStatistic names and fmt value\n\n\n\n\ndefault\n\n\nNumber of decimal digits to keep for unspecified terms",
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     "title": "modelsummary: Data and Model Summaries in R",
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-    "text": "Summary tables using 2-sided formulae: crosstabs, frequencies, table 1s and more.\nDescription\ndatasummary can use any summary function which produces one numeric or character value per variable. The examples section of this documentation shows how to define custom summary functions.\nmodelsummary also supplies several shortcut summary functions which can be used in datasummary() formulas: Min, Max, Mean, Median, Var, SD, NPercent, NUnique, Ncol, P0, P25, P50, P75, P100.\nSee the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\nUsage\ndatasummary(\n  formula,\n  data,\n  output = \"default\",\n  fmt = 2,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  sparse_header = TRUE,\n  escape = TRUE,\n  ...\n)\n\nArguments\n\n\n\nformula\n\n\nA two-sided formula to describe the table: rows ~ columns. See the Examples section for a mini-tutorial and the Details section for more resources. Grouping/nesting variables can appear on both sides of the formula, but all summary functions must be on one side.\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nsparse_header\n\n\nTRUE or FALSE. TRUE eliminates column headers which have a unique label across all columns, except for the row immediately above the data. FALSE keeps all headers. The order in which terms are entered in the formula determines the order in which headers appear. For example, x~mean*z will print the mean-related header above the z-related header.’\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\nDetails\nVisit the ‘modelsummary’ website for more usage examples: https://modelsummary.com\nThe ‘datasummary’ function is a thin wrapper around the ‘tabular’ function from the ‘tables’ package. More details about table-making formulas can be found in the ‘tables’ package documentation: ?tables::tabular\nHierarchical or \"nested\" column labels are only available for these output formats: tinytable, kableExtra, gt, html, rtf, and LaTeX. When saving tables to other formats, nested labels will be combined to a \"flat\" header.\nGlobal Options\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nModel labels: default column names\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nTable-making packages\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nTable themes\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nModel extraction functions\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nFormatting numeric entries\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nLaTeX preamble\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\nExamples\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# The left-hand side of the formula describes rows, and the right-hand side\n# describes columns. This table uses the \"mpg\" variable as a row and the \"mean\"\n# function as a column:\n\ndatasummary(mpg ~ mean, data = mtcars)\n\n \n\n  \n    \n\ntinytable_cu9a1pnklzv2p6yfeaxd\n\n\n      \n\n \n                mean\n              \n\nmpg\n                  20.09\n                \n\n\n\n    \n\n# This table uses the \"mean\" function as a row and the \"mpg\" variable as a column:\n\ndatasummary(mean ~ mpg, data = mtcars)\n\n \n\n  \n    \n\ntinytable_j8sb5pnt2xipeazc7cpo\n\n\n      \n\n \n                mpg\n              \n\nmean\n                  20.09\n                \n\n\n\n    \n\n# Display several variables or functions of the data using the \"+\"\n# concatenation operator. This table has 2 rows and 2 columns:\n\ndatasummary(hp + mpg ~ mean + sd, data = mtcars)\n\n \n\n  \n    \n\ntinytable_e4h4flnsrkzqgnwdmcno\n\n\n      \n\n \n                mean\n                sd\n              \n\n\nhp \n                  146.69\n                  68.56\n                \n\nmpg\n                  20.09 \n                  6.03 \n                \n\n\n\n\n    \n\n# Nest variables or statistics inside a \"factor\" variable using the \"*\" nesting\n# operator. This table shows the mean of \"hp\" and \"mpg\" for each value of\n# \"cyl\":\n\nmtcars$cyl &lt;- as.factor(mtcars$cyl)\ndatasummary(hp + mpg ~ cyl * mean, data = mtcars)\n\n \n\n  \n    \n\ntinytable_ankn7hgtxhq231dulmho\n\n\n      \n\n \n                4\n                6\n                8\n              \n\n\nhp \n                  82.64\n                  122.29\n                  209.21\n                \n\nmpg\n                  26.66\n                  19.74 \n                  15.10 \n                \n\n\n\n\n    \n\n# If you don't want to convert your original data\n# to factors, you can use the 'Factor()'\n# function inside 'datasummary' to obtain an identical result:\n\ndatasummary(hp + mpg ~ Factor(cyl) * mean, data = mtcars)\n\n \n\n  \n    \n\ntinytable_8kup4jsnvz18phctuhyd\n\n\n      \n\n \n                4\n                6\n                8\n              \n\n\nhp \n                  82.64\n                  122.29\n                  209.21\n                \n\nmpg\n                  26.66\n                  19.74 \n                  15.10 \n                \n\n\n\n\n    \n\n# You can nest several variables or statistics inside a factor by using\n# parentheses. This table shows the mean and the standard deviation for each\n# subset of \"cyl\":\n\ndatasummary(hp + mpg ~ cyl * (mean + sd), data = mtcars)\n\n \n\n  \n    \n\ntinytable_gbjrrrssmk19dwv4pbmv\n\n\n      \n\n\n \n4\n6\n8\n\n\n \n                mean\n                sd\n                mean\n                sd\n                mean\n                sd\n              \n\n\n\nhp \n                  82.64\n                  20.93\n                  122.29\n                  24.26\n                  209.21\n                  50.98\n                \n\nmpg\n                  26.66\n                  4.51 \n                  19.74 \n                  1.45 \n                  15.10 \n                  2.56 \n                \n\n\n\n\n    \n\n# Summarize all numeric variables with 'All()'\ndatasummary(All(mtcars) ~ mean + sd, data = mtcars)\n\n \n\n  \n    \n\ntinytable_aal6h2bvlc135pru3kc3\n\n\n      \n\n \n                mean\n                sd\n              \n\n\nmpg \n                  20.09 \n                  6.03  \n                \n\ndisp\n                  230.72\n                  123.94\n                \n\nhp  \n                  146.69\n                  68.56 \n                \n\ndrat\n                  3.60  \n                  0.53  \n                \n\nwt  \n                  3.22  \n                  0.98  \n                \n\nqsec\n                  17.85 \n                  1.79  \n                \n\nvs  \n                  0.44  \n                  0.50  \n                \n\nam  \n                  0.41  \n                  0.50  \n                \n\ngear\n                  3.69  \n                  0.74  \n                \n\ncarb\n                  2.81  \n                  1.62  \n                \n\n\n\n\n    \n\n# Define custom summary statistics. Your custom function should accept a vector\n# of numeric values and return a single numeric or string value:\n\nminmax &lt;- function(x) sprintf(\"[%.2f, %.2f]\", min(x), max(x))\nmean_na &lt;- function(x) mean(x, na.rm = TRUE)\n\ndatasummary(hp + mpg ~ minmax + mean_na, data = mtcars)\n\n \n\n  \n    \n\ntinytable_6zdkylcqp5ja4snmk9n2\n\n\n      \n\n \n                minmax\n                mean_na\n              \n\n\nhp \n                  [52.00, 335.00]\n                  146.69\n                \n\nmpg\n                  [10.40, 33.90] \n                  20.09 \n                \n\n\n\n\n    \n\n# To handle missing values, you can pass arguments to your functions using\n# '*Arguments()'\n\ndatasummary(hp + mpg ~ mean * Arguments(na.rm = TRUE), data = mtcars)\n\n \n\n  \n    \n\ntinytable_8pcxqgd6ff4zz0w5j404\n\n\n      \n\n \n                mean\n              \n\n\nhp \n                  146.69\n                \n\nmpg\n                  20.09 \n                \n\n\n\n\n    \n\n# For convenience, 'modelsummary' supplies several convenience functions\n# with the argument `na.rm=TRUE` by default: Mean, Median, Min, Max, SD, Var,\n# P0, P25, P50, P75, P100, NUnique, Histogram\n\n#datasummary(hp + mpg ~ Mean + SD + Histogram, data = mtcars)\n\n# These functions also accept a 'fmt' argument which allows you to\n# round/format the results\n\ndatasummary(hp + mpg ~ Mean * Arguments(fmt = \"%.3f\") + SD * Arguments(fmt = \"%.1f\"), data = mtcars)\n\n \n\n  \n    \n\ntinytable_q0zh8t7fojct4x3zrvat\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nhp \n                  146.688\n                  68.6\n                \n\nmpg\n                  20.091 \n                  6.0 \n                \n\n\n\n\n    \n\n# Save your tables to a variety of output formats:\nf &lt;- hp + mpg ~ Mean + SD\n#datasummary(f, data = mtcars, output = 'table.html')\n#datasummary(f, data = mtcars, output = 'table.tex')\n#datasummary(f, data = mtcars, output = 'table.md')\n#datasummary(f, data = mtcars, output = 'table.docx')\n#datasummary(f, data = mtcars, output = 'table.pptx')\n#datasummary(f, data = mtcars, output = 'table.jpg')\n#datasummary(f, data = mtcars, output = 'table.png')\n\n# Display human-readable code\n#datasummary(f, data = mtcars, output = 'html')\n#datasummary(f, data = mtcars, output = 'markdown')\n#datasummary(f, data = mtcars, output = 'latex')\n\n# Return a table object to customize using a table-making package\n#datasummary(f, data = mtcars, output = 'tinytable')\n#datasummary(f, data = mtcars, output = 'gt')\n#datasummary(f, data = mtcars, output = 'kableExtra')\n#datasummary(f, data = mtcars, output = 'flextable')\n#datasummary(f, data = mtcars, output = 'huxtable')\n\n# add_rows\nnew_rows &lt;- data.frame(a = 1:2, b = 2:3, c = 4:5)\nattr(new_rows, 'position') &lt;- c(1, 3)\ndatasummary(mpg + hp ~ mean + sd, data = mtcars, add_rows = new_rows)\n\n \n\n  \n    \n\ntinytable_gnaisdxehhhaacrh1vzq\n\n\n      \n\n \n                mean\n                sd\n              \n\n\n1.00\n                  2.00  \n                  4.00 \n                \n\nmpg \n                  20.09 \n                  6.03 \n                \n\n2.00\n                  3.00  \n                  5.00 \n                \n\nhp  \n                  146.69\n                  68.56",
+    "text": "Rounding with decimal digits in the fmt argument\n\n\n\nfmt_decimal(digits = 3, pdigits = NULL, ...)\n\n\n\n\n\n\n\ndigits\n\n\nNumber of decimal digits to keep, including trailing zeros.\n\n\n\n\npdigits\n\n\nNumber of decimal digits to keep for p values. If NULL, the value of digits is used.\n\n\n\n\n…\n\n\nAdditional arguments are passed to the format() function (e.g., big.marks, scientific). See ?format",
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-    "text": "Balance table: Summary statistics for different subsets of the data (e.g., control and treatment groups)\nDescription\nCreates balance tables with summary statistics for different subsets of the data (e.g., control and treatment groups). It can also be used to create summary tables for full data sets. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\nUsage\ndatasummary_balance(\n  formula,\n  data,\n  output = \"default\",\n  fmt = fmt_decimal(digits = 1, pdigits = 3),\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  stars = FALSE,\n  add_columns = NULL,\n  add_rows = NULL,\n  dinm = TRUE,\n  dinm_statistic = \"std.error\",\n  escape = TRUE,\n  ...\n)\n\nArguments\n\n\n\nformula\n\n\n\n\n~1: show summary statistics for the full dataset\n\n\none-sided formula: with the \"condition\" or \"column\" variable on the right-hand side.\n\n\ntwo-side formula: with the subset of variables to summarize on the left-hand side and the condition variable on the right-hand side.\n\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble). If this data includes columns called \"blocks\", \"clusters\", and/or \"weights\", the \"estimatr\" package will consider them when calculating the difference in means. If there is a weights column, the reported mean and standard errors will also be weighted.\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nstars\n\n\nto indicate statistical significance\n\n\nFALSE (default): no significance stars.\n\n\nTRUE: +=.1, =.05, =.01, =0.001\n\n\nNamed numeric vector for custom stars such as c(’*’ = .1, ‘+’ = .05)\n\n\nNote: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use \"glue strings\" with stars.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\ndinm\n\n\nTRUE calculates a difference in means with uncertainty estimates. This option is only available if the estimatr package is installed. If data includes columns named \"blocks\", \"clusters\", or \"weights\", this information will be taken into account automatically by estimatr::difference_in_means.\n\n\n\n\ndinm_statistic\n\n\nstring: \"std.error\" or \"p.value\"\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\nGlobal Options\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nModel labels: default column names\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nTable-making packages\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nTable themes\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nModel extraction functions\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nFormatting numeric entries\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nLaTeX preamble\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\nExamples\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\ndatasummary_balance(~am, mtcars)\n\n \n\n  \n    \n\ntinytable_91ln5q0l4ywxpxokynmo\n\n\n      \n\n\n \n0\n1\n \n \n\n\n \n                Mean\n                Std. Dev.\n                Mean\n                Std. Dev.\n                Diff. in Means\n                Std. Error\n              \n\n\n\nmpg \n                  17.1 \n                  3.8  \n                  24.4 \n                  6.2 \n                  7.2   \n                  1.9 \n                \n\ncyl \n                  6.9  \n                  1.5  \n                  5.1  \n                  1.6 \n                  -1.9  \n                  0.6 \n                \n\ndisp\n                  290.4\n                  110.2\n                  143.5\n                  87.2\n                  -146.8\n                  35.0\n                \n\nhp  \n                  160.3\n                  53.9 \n                  126.8\n                  84.1\n                  -33.4 \n                  26.4\n                \n\ndrat\n                  3.3  \n                  0.4  \n                  4.0  \n                  0.4 \n                  0.8   \n                  0.1 \n                \n\nwt  \n                  3.8  \n                  0.8  \n                  2.4  \n                  0.6 \n                  -1.4  \n                  0.2 \n                \n\nqsec\n                  18.2 \n                  1.8  \n                  17.4 \n                  1.8 \n                  -0.8  \n                  0.6 \n                \n\nvs  \n                  0.4  \n                  0.5  \n                  0.5  \n                  0.5 \n                  0.2   \n                  0.2 \n                \n\ngear\n                  3.2  \n                  0.4  \n                  4.4  \n                  0.5 \n                  1.2   \n                  0.2 \n                \n\ncarb\n                  2.7  \n                  1.1  \n                  2.9  \n                  2.2 \n                  0.2   \n                  0.7",
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-    "text": "Mostly for internal use, but can be useful when users supply a function to the method argument of datasummary_correlation.\n\ndatasummary_correlation_format(\n  x,\n  fmt,\n  leading_zero = FALSE,\n  diagonal = NULL,\n  upper_triangle = NULL\n)\n\n\n\n\n\nx\n\n\nsquare numeric matrix\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nleading_zero\n\n\nboolean. If FALSE, leading zeros are removed\n\n\n\n\ndiagonal\n\n\ncharacter or NULL. If character, all elements of the diagonal are replaced by the same character (e.g., \"1\").\n\n\n\n\nupper_triangle\n\n\ncharacter or NULL. If character, all elements of the upper triangle are replaced by the same character (e.g., \"\" or \".\").\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_6wii9nlcrleb5hdx32gx\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .",
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-    "text": "Mostly for internal use, but can be useful when users supply a function to the method argument of datasummary_correlation.\n\ndatasummary_correlation_format(\n  x,\n  fmt,\n  leading_zero = FALSE,\n  diagonal = NULL,\n  upper_triangle = NULL\n)\n\n\n\n\n\nx\n\n\nsquare numeric matrix\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nleading_zero\n\n\nboolean. If FALSE, leading zeros are removed\n\n\n\n\ndiagonal\n\n\ncharacter or NULL. If character, all elements of the diagonal are replaced by the same character (e.g., \"1\").\n\n\n\n\nupper_triangle\n\n\ncharacter or NULL. If character, all elements of the upper triangle are replaced by the same character (e.g., \"\" or \".\").\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_6wii9nlcrleb5hdx32gx\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .",
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-    "text": "Draw a table from a data.frame\n\n\n\ndatasummary_df(\n  data,\n  output = \"default\",\n  fmt = 2,\n  align = NULL,\n  hrule = NULL,\n  title = NULL,\n  notes = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nhrule\n\n\nposition of horizontal rules (integer vector)\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\n\n\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’"
+    "text": "A convenience function for use with a regression model or list of regression models. Returns a named list of models, where the names are the models’ respective dependent variables. Pass your list of models to dvnames before sending to modelsummary to automatically get dependent variable-titled columns.\n\ndvnames(models, number = FALSE, strip = FALSE, fill = \"Model\")\n\n\n\n\n\nmodels\n\n\nA regression model or list of regression models\n\n\n\n\nnumber\n\n\nShould the models be numbered (1), (2), etc., in addition to their dependent variable names?\n\n\n\n\nstrip\n\n\nboolean FALSE returns the dependent variable names as they appear in the model. TRUE returns the dependent variable names as they appear in the data, without transformations.\n\n\n\n\nfill\n\n\nIf insight::find_response() cannot find a response, the column title to use in its place. Set to ’ ’ to leave blank.\n\n\n\n\nlibrary(modelsummary)\n\n\nm1 &lt;- lm(mpg ~ hp, data = mtcars)\nm2 &lt;- lm(mpg ~ hp + wt, data = mtcars)\n\n# Without dvnames, column names are (1) and (2)\nmodelsummary(list(m1, m2))\n\n \n\n  \n    \n\ntinytable_u2hh31udbdeemx98iz83\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47   \n                \n\n\n\n\n    \n\n# With dvnames, they are \"mpg\" and \"mpg\"\nmodelsummary(dvnames(list(m1,m2)))\n\n \n\n  \n    \n\ntinytable_f852o222qpbpp0nismmp\n\n\n      \n\n \n                mpg\n                mpg \n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47",
+    "crumbs": [
+      "Get started",
+      "Functions",
+      "Utilities",
+      "`dvnames`"
+    ]
   },
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     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "text": "Draw a table from a data.frame\n\n\n\ndatasummary_df(\n  data,\n  output = \"default\",\n  fmt = 2,\n  align = NULL,\n  hrule = NULL,\n  title = NULL,\n  notes = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nhrule\n\n\nposition of horizontal rules (integer vector)\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\n\n\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’"
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-    "text": "library(modelsummary)\n\nUnsupported models: modelsummary_list\n\nThe simplest way to summarize an unsupported model is to create a modelsummary_list object. This approach is super flexible, but it requires manual intervention, and it can become tedious if you need to summarize many models. The next section shows how to add formal support for an unsupported model type.\nA modelsummary_list is a list with two element that conform to the broom package specification: tidy and glance. tidy is a data.frame with at least three columns: term, estimate, and std.error. glance is a data.frame with only a single row, and where each column will be displayed at the bottom of the table in the goodness-of-fit section. Finally, we wrap those two elements in a list and assign it a modelsummary_list class:\n\nti &lt;- data.frame(\n  term = c(\"coef1\", \"coef2\", \"coef3\"),\n  estimate = 1:3,\n  std.error = c(pi, exp(1), sqrt(2)))\n\ngl &lt;- data.frame(\n  stat1 = \"blah\",\n  stat2 = \"blah blah\")\n\nmod &lt;- list(\n  tidy = ti,\n  glance = gl)\nclass(mod) &lt;- \"modelsummary_list\"\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_f7k6a6z0fmzcdwvwlapd\n\n\n      \n\n \n                (1)\n              \n\n\ncoef1\n                  1.000    \n                \n\n     \n                  (3.142)  \n                \n\ncoef2\n                  2.000    \n                \n\n     \n                  (2.718)  \n                \n\ncoef3\n                  3.000    \n                \n\n     \n                  (1.414)  \n                \n\nstat1\n                  blah     \n                \n\nstat2\n                  blah blah\n                \n\n\n\n\n    \n\n\nUnsupported models: glance and tidy\n\nmodelsummary relies on two functions from the broom package to extract model information: tidy and glance. If broom doesn’t support the type of model you are trying to summarize, modelsummary won’t support it out of the box. Thankfully, it is extremely easy to add support for most models using custom methods.\nFor example, models produced by the MCMCglmm package are not currently supported by broom. To add support, you simply need to create a tidy and a glance method:\n# load packages and data\nlibrary(modelsummary)\nlibrary(MCMCglmm)\ndata(PlodiaPO)\n\n# add custom functions to extract estimates (tidy) and goodness-of-fit (glance) information\ntidy.MCMCglmm &lt;- function(x, ...) {\n    s &lt;- summary(x, ...)\n    ret &lt;- data.frame(\n      term      = row.names(s$solutions),\n      estimate  = s$solutions[, 1],\n      conf.low  = s$solutions[, 2],\n      conf.high = s$solutions[, 3])\n    ret\n}\n\nglance.MCMCglmm &lt;- function(x, ...) {\n    ret &lt;- data.frame(\n      dic = x$DIC,\n      n   = nrow(x$X))\n    ret\n}\n\n# estimate a simple model\nmodel &lt;- MCMCglmm(PO ~ 1 + plate, random = ~ FSfamily, data = PlodiaPO, verbose=FALSE, pr=TRUE)\n\n# summarize the model\nmodelsummary(model, statistic = 'conf.int')\nThree important things to note.\nFirst, the methods are named tidy.MCMCglmm and glance.MCMCglmm because the model object I am trying to summarize is of class MCMCglmm. You can find the class of a model by running: class(model).\nSecond, both of the methods include the ellipsis ... argument.\nThird, in the example above we used the statistic = 'conf.int' argument. This is because the tidy method produces conf.low and conf.high columns. In most cases, users will define std.error column in their custom tidy methods, so the statistic argument will need to be adjusted.\nIf you create new tidy and glance methods, please consider contributing them to broom so that the rest of the community can benefit from your work: https://github.com/tidymodels/broom\nModifying information: tidy_custom and glance_custom\n\nUsers may want to include more information than is made available by the default extractor function. For example, models produced by the MASS::polr do not produce p values by default, which means that we cannot use the stars=TRUE argument in modelsummary. However, it is possible to extract this information by using the lmtest::coeftest function. To include such custom information, we will define new glance_custom and tidy_custom methods.\nWe begin by estimating a model with the MASS::polr:\n\nlibrary(MASS)\n\nmod_ordinal &lt;- polr(as.ordered(gear) ~ mpg + drat, data = mtcars)\n\nget_estimates(mod_ordinal)\n\n  term     estimate  std.error conf.level   conf.low  conf.high   statistic df.error      p.value component s.value group\n1  3|4 13.962948761 4.04107300       0.95  5.6851860 22.2407116  3.45525774       28 0.0017706303     alpha     9.1      \n2  4|5 16.898937342 4.39497069       0.95  7.8962480 25.9016267  3.84506258       28 0.0006356348     alpha    10.6      \n3  mpg -0.008646682 0.09034201       0.95 -0.1916706  0.1708667 -0.09571053       28 0.9244322098      beta     0.1      \n4 drat  3.949431923 1.30665144       0.95  1.6191505  6.8457246  3.02255965       28 0.0053120619      beta     7.6      \n\n\nThe get_estimates function shows that our default extractor does not produce a p.value column. As a result, setting stars=TRUE in modelsummary will produce an error.\nWe know that the MASS::polr produces an object of class polr:\n\nclass(mod_ordinal)\n\n[1] \"polr\"\n\n\nTo extract more (custom) information from a model of this class, we thus define a method called tidy_custom.polr which returns a data.frame with two columns: term and p.value:\n\ntidy_custom.polr &lt;- function(x, ...) {\n  s &lt;- lmtest::coeftest(x)\n  out &lt;- data.frame(\n    term = row.names(s),\n    p.value = s[, \"Pr(&gt;|t|)\"])\n  out\n}\n\nWhen this method is defined, modelsummary can automatically extract p values from all models of this class, and will now work properly with stars=TRUE:\n\nmodelsummary(mod_ordinal, stars = TRUE)\n\n \n\n  \n    \n\ntinytable_f0vx584sr6fschoqglq0\n\n\n      \n\n \n                (1)\n              \n+ p \n\n\n3|4     \n                  13.963** \n                \n\n        \n                  (4.041)  \n                \n\n4|5     \n                  16.899***\n                \n\n        \n                  (4.395)  \n                \n\nmpg     \n                  -0.009   \n                \n\n        \n                  (0.090)  \n                \n\ndrat    \n                  3.949**  \n                \n\n        \n                  (1.307)  \n                \n\nNum.Obs.\n                  32       \n                \n\nAIC     \n                  51.1     \n                \n\nBIC     \n                  57.0     \n                \n\nRMSE    \n                  3.44     \n                \n\n\n\n\n    \n\n\nNew information: tidy_custom and glance_custom\n\nSometimes users will want to include information that is not supplied by those functions. A pretty easy way to include extra information is to define new glance_custom and tidy_custom methods. To illustrate, we estimate two linear regression models using the lm function:\n\nlibrary(modelsummary)\n\nmod &lt;- list()\nmod[[1]] &lt;- lm(hp ~ mpg + drat, mtcars)\nmod[[2]] &lt;- lm(wt ~ mpg + drat + am, mtcars)\n\nIn R, the lm function produces models of class “lm”:\n\nclass(mod[[1]])\n\n[1] \"lm\"\n\n\nLet’s say you would like to print the dependent variable for each model of this particular class. All you need to do is define a new method called glance_custom.lm. This method should return a data.frame (or tibble) with 1 row, and 1 column per piece of information you want to display. For example:\n\nglance_custom.lm &lt;- function(x, ...) {\n    dv &lt;- as.character(formula(x)[2])\n    out &lt;- data.frame(\"DV\" = dv)\n    return(out)\n}\n\nNow, let’s customize the body of the table. The vcov argument already allows users to customize uncertainty estimates. But imagine you want to override the coefficient estimates of your “lm” models. Easy! All you need to do is define a tidy_custom.lm method which returns a data.frame (or tibble) with one column called “term” and one column called “estimate”.\nHere, we’ll substitute estimates by an up/down-pointing triangles which represents their signs:\n\ntidy_custom.lm &lt;- function(x, ...) {\n    s &lt;- summary(x)$coefficients\n    out &lt;- data.frame(\n      term = row.names(s),\n      estimate = ifelse(s[,1] &gt; 0, '▲', '▼'))\n    return(out)\n}\n\nAfter you define the glance_custom and tidy_custom methods, modelsummary will automatically display your customized model information:\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_hwtou6jdyzx35b3mgv7n\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  ▲       \n                  ▲      \n                \n\n           \n                  (55.415)\n                  (0.728)\n                \n\nmpg        \n                  ▼       \n                  ▼      \n                \n\n           \n                  (1.792) \n                  (0.019)\n                \n\ndrat       \n                  ▲       \n                  ▼      \n                \n\n           \n                  (20.198)\n                  (0.245)\n                \n\nam         \n                          \n                  ▼      \n                \n\n           \n                          \n                  (0.240)\n                \n\nNum.Obs.   \n                  32      \n                  32     \n                \n\nR2         \n                  0.614   \n                  0.803  \n                \n\nR2 Adj.    \n                  0.588   \n                  0.782  \n                \n\nAIC        \n                  337.9   \n                  46.4   \n                \n\nBIC        \n                  343.7   \n                  53.7   \n                \n\nLog.Lik.   \n                  -164.940\n                  -18.201\n                \n\nF          \n                  23.100  \n                  38.066 \n                \n\nRMSE       \n                  41.91   \n                  0.43   \n                \n\nDV         \n                  hp      \n                  wt     \n                \n\n\n\n\n    \n\n\nNote that you can define a std.error column in tidy_custom.lm to replace the uncertainty estimates instead of the coefficients.\nCustomization: New model class\nAn even more fundamental way to customize the output would be to completely bypass modelsummary’s extractor functions by assigning a new class name to your model. For example,\n\n# estimate a linear model\nmod_custom &lt;- lm(hp ~ mpg + drat, mtcars)\n\n# assign it a new class\nclass(mod_custom) &lt;- \"custom\"\n\n# define tidy and glance methods\ntidy.custom &lt;- function(x, ...) {\n  data.frame(\n    term = names(coef(x)),\n    estimate = letters[1:length(coef(x))],\n    std.error = seq_along(coef(x))\n  )\n}\n\nglance.custom &lt;- function(x, ...) {\n  data.frame(\n    \"Model\" = \"Custom\",\n    \"nobs\" = stats:::nobs.lm(x)\n  )\n}\n\n# summarize\nmodelsummary(mod_custom)\n\n \n\n  \n    \n\ntinytable_rhkg49z730j7lvo6r1y4\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  a      \n                \n\n           \n                  (1.000)\n                \n\nmpg        \n                  b      \n                \n\n           \n                  (2.000)\n                \n\ndrat       \n                  c      \n                \n\n           \n                  (3.000)\n                \n\nNum.Obs.   \n                  32     \n                \n\nModel      \n                  Custom \n                \n\n\n\n\n    \n\n\nWarning: When defining new tidy and glance methods, it is important to include an ellipsis argument (...).\nNote that in the glance.custom() method, we called stats:::nobs.lm() instead of the default stats::nobs() method, because the latter know does not know where to dispatch models of our new “custom” class. Being more explicit solves the problem.\nAn alternative would be to set a new class that inherits from the previous one, and to use a global option to set broom as the default extractor function (otherwise modelsummary will use its standard lm extractors by inheritance):\n\noptions(modelsummary_get = \"broom\")\nclass(mod_custom) &lt;- c(\"custom\", \"lm\")\n\nCustomization: modelsummary_list\n\nAnother flexible way to customize model output is to use output = \"modelsummary_list\". With this output option, modelsummary() returns a list with two elements: tidy contains parameter estimates, standard errors, etc., and glance contains model statistics such as the AIC. For example,\n\nmod &lt;- lm(hp ~ mpg + drat, mtcars)\nmod_list &lt;- modelsummary(mod, output = \"modelsummary_list\")\nmod_list$tidy\n\n         term   estimate std.error  statistic df.error      p.value s.value group conf.low conf.high\n1 (Intercept) 278.515455 55.414866  5.0260061       29 2.359726e-05    15.4             NA        NA\n2         mpg  -9.985499  1.791837 -5.5727709       29 5.172030e-06    17.6             NA        NA\n3        drat  19.125752 20.197756  0.9469246       29 3.515013e-01     1.5             NA        NA\n\nmod_list$glance\n\n       aic      bic r.squared adj.r.squared     rmse nobs        F    logLik\n1 337.8809 343.7438 0.6143611     0.5877653 41.90687   32 23.09994 -164.9404\n\n\nBoth tidy and glance can now be customized, and the updated model can be passed back to modelsummary using modelsummary(mod_list). All information that is displayed in the table is contained in mod_list, so this pattern allows for very flexible adjustments of output tables.\nA useful example for this pattern concerns mixed models using lme4. Assume we want to compare the effect of using different degrees-of-freedom adjustments on the significance of the coefficients. The models have identical parameter estimates, standard errors, and model fit statistics - we only want to change the p-values. We use the parameters package to compute the adjusted p-values.\n\nlibrary(\"lme4\")\nmod &lt;- lmer(mpg ~ drat + (1 | am), data = mtcars)\nmod_list &lt;- modelsummary(mod, output = \"modelsummary_list\", effects = \"fixed\")\n# create a copy, where we'll change the p-values\nmod_list_kenward &lt;- as.list(mod_list)\nmod_list_kenward$tidy$p.value &lt;- parameters::p_value_kenward(mod)$p\n\nmodelsummary(list(\"Wald\" = mod_list, \"Kenward\" = mod_list_kenward), \n            statistic = \"{std.error} ({p.value}) {stars}\")\n\n \n\n  \n    \n\ntinytable_4ybcmel83wjzwixxyrgk\n\n\n      \n\n \n                Wald\n                Kenward\n              \n\n\n(Intercept)\n                  -5.159            \n                  -5.159         \n                \n\n           \n                  6.409 (0.428)     \n                  6.409 (0.680)  \n                \n\ndrat       \n                  7.045             \n                  7.045          \n                \n\n           \n                  1.736 (\n                  \n1.736 (0.086) +\n                \n\nNum.Obs.   \n                  32                \n                  32             \n                \n\nR2 Marg.   \n                  0.402             \n                  0.402          \n                \n\nR2 Cond.   \n                  0.440             \n                  0.440          \n                \n\nAIC        \n                  188.7             \n                  188.7          \n                \n\nBIC        \n                  194.6             \n                  194.6          \n                \n\nICC        \n                  0.1               \n                  0.1            \n                \n\nRMSE       \n                  4.28              \n                  4.28",
+    "text": "A convenience function for use with a regression model or list of regression models. Returns a named list of models, where the names are the models’ respective dependent variables. Pass your list of models to dvnames before sending to modelsummary to automatically get dependent variable-titled columns.\n\ndvnames(models, number = FALSE, strip = FALSE, fill = \"Model\")\n\n\n\n\n\nmodels\n\n\nA regression model or list of regression models\n\n\n\n\nnumber\n\n\nShould the models be numbered (1), (2), etc., in addition to their dependent variable names?\n\n\n\n\nstrip\n\n\nboolean FALSE returns the dependent variable names as they appear in the model. TRUE returns the dependent variable names as they appear in the data, without transformations.\n\n\n\n\nfill\n\n\nIf insight::find_response() cannot find a response, the column title to use in its place. Set to ’ ’ to leave blank.\n\n\n\n\nlibrary(modelsummary)\n\n\nm1 &lt;- lm(mpg ~ hp, data = mtcars)\nm2 &lt;- lm(mpg ~ hp + wt, data = mtcars)\n\n# Without dvnames, column names are (1) and (2)\nmodelsummary(list(m1, m2))\n\n \n\n  \n    \n\ntinytable_u2hh31udbdeemx98iz83\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47   \n                \n\n\n\n\n    \n\n# With dvnames, they are \"mpg\" and \"mpg\"\nmodelsummary(dvnames(list(m1,m2)))\n\n \n\n  \n    \n\ntinytable_f852o222qpbpp0nismmp\n\n\n      \n\n \n                mpg\n                mpg \n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47",
     "crumbs": [
       "Get started",
-      "Extension and Customization"
+      "Functions",
+      "Utilities",
+      "`dvnames`"
     ]
   },
   {
-    "objectID": "vignettes/modelsummary.html",
-    "href": "vignettes/modelsummary.html",
-    "title": "Model Summaries",
+    "objectID": "man/modelsummary.html",
+    "href": "man/modelsummary.html",
+    "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "text": "modelsummary includes a powerful set of utilities to customize the information displayed in your model summary tables. You can easily rename, reorder, subset or omit parameter estimates; choose the set of goodness-of-fit statistics to display; display various “robust” standard errors or confidence intervals; add titles, footnotes, or source notes; insert stars or custom characters to indicate levels of statistical significance; or add rows with supplemental information about your models.\nBefore starting, we set modelsummary to use the tinytable package to draw table, and we set a few CSS properties to make tables more display in a more compact format on this website. This step is optional.\nNow, we download data, fit some models, and summarize them in a table using the modelsummary() function:\nlibrary(modelsummary)\n\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'\ndat &lt;- read.csv(url)\n\nmodels &lt;- list(\n  \"OLS 1\"     = lm(Donations ~ Literacy + Clergy, data = dat),\n  \"Poisson\" = glm(Donations ~ Literacy + Commerce, family = poisson, data = dat),\n  \"OLS 2\"     = lm(Crime_pers ~ Literacy + Clergy, data = dat)\n)\n\nmodelsummary(models)\n\n \n\n  \n    \n\ntinytable_6at2ztg025oh1ikjxsul\n\n\n      \n\n \n                OLS 1\n                Poisson\n                OLS 2\n              \n\n\n(Intercept)\n                  7948.667  \n                  8.241      \n                  16259.384 \n                \n\n           \n                  (2078.276)\n                  (0.006)    \n                  (2611.140)\n                \n\nLiteracy   \n                  -39.121   \n                  0.003      \n                  3.680     \n                \n\n           \n                  (37.052)  \n                  (0.000)    \n                  (46.552)  \n                \n\nClergy     \n                  15.257    \n                             \n                  77.148    \n                \n\n           \n                  (25.735)  \n                             \n                  (32.334)  \n                \n\nCommerce   \n                            \n                  0.011      \n                            \n                \n\n           \n                            \n                  (0.000)    \n                            \n                \n\nNum.Obs.   \n                  86        \n                  86         \n                  86        \n                \n\nR2         \n                  0.020     \n                             \n                  0.065     \n                \n\nR2 Adj.    \n                  -0.003    \n                             \n                  0.043     \n                \n\nAIC        \n                  1740.8    \n                  274160.8   \n                  1780.0    \n                \n\nBIC        \n                  1750.6    \n                  274168.2   \n                  1789.9    \n                \n\nLog.Lik.   \n                  -866.392  \n                  -137077.401\n                  -886.021  \n                \n\nF          \n                  0.866     \n                  18294.559  \n                  2.903     \n                \n\nRMSE       \n                  5740.99   \n                  5491.61    \n                  7212.97",
+    "text": "Create beautiful and customizable tables to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, Typst, PDF, PowerPoint, Excel, RTF, JPG, or PNG. The appearance of the tables can be customized extensively by specifying the output argument, and by using functions from one of the supported table customization packages: tinytable, kableExtra, gt, flextable, huxtable, DT. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nThe modelsummary Vignette includes dozens of examples of tables with extensive customizations.\n\n\nThe Appearance Vignette shows how to modify the look of tables.\n\n\nmodelsummary(\n  models,\n  output = \"default\",\n  fmt = 3,\n  estimate = \"estimate\",\n  statistic = \"std.error\",\n  vcov = NULL,\n  conf_level = 0.95,\n  exponentiate = FALSE,\n  stars = FALSE,\n  shape = term + statistic ~ model,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = FALSE,\n  gof_map = NULL,\n  gof_omit = NULL,\n  gof_function = NULL,\n  group_map = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  align = NULL,\n  notes = NULL,\n  title = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_decimal(digits = 2, pdigits = 3): Decimal digits for estimate and p values\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_term(“(Intercept)” = 1, “X” = 2): Format terms differently\n\n\nfmt = fmt_statistic(“estimate” = 1, “r.squared” = 6): Format statistics differently.\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\nstring:\n\n\nNote on LaTeX output: To ensure proper typography, all numeric entries are enclosed in the  command, which requires the siunitx package to be loaded in the LaTeX preamble. This behavior can be altered with global options. See the ‘Details’ section.\n\n\n\n\n\n\nestimate\n\n\na single string or a character vector of length equal to the number of models. Valid entries include any column name of the data.frame produced by get_estimates(model), and strings with curly braces compatible with the glue package format. Examples:\n\n\n“estimate”\n\n\n“{estimate} ({std.error}){stars}”\n\n\n“{estimate} [{conf.low}, {conf.high}]”\n\n\n\n\n\n\nstatistic\n\n\nvector of strings or glue strings which select uncertainty statistics to report vertically below the estimate. NULL omits all uncertainty statistics.\n\n\n\"conf.int\", \"std.error\", \"statistic\", \"p.value\", \"conf.low\", \"conf.high\", or any column name produced by get_estimates(model)\n\n\nglue package strings with braces, with or without R functions, such as:\n\n\n“{p.value} [{conf.low}, {conf.high}]”\n\n\n“Std.Error: {std.error}”\n\n\n“{exp(estimate) * std.error}”\n\n\nNumbers are automatically rounded and converted to strings. To apply functions to their numeric values, as in the last glue example, users must set fmt=NULL.\n\n\nParentheses are added automatically unless the string includes glue curly braces {}.\n\n\n\n\nNotes:\n\n\nThe names of the statistic are used a column names when using the shape argument to display statistics as columns:\n\n\nstatistic=c(“p”=“p.value”, “[”=”conf.low”, ”]”=“conf.high”)\n\n\n\n\nSome statistics are not supported for all models. See column names in get_estimates(model), and visit the website to learn how to add custom statistics.\n\n\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nstars\n\n\nto indicate statistical significance\n\n\nFALSE (default): no significance stars.\n\n\nTRUE: +=.1, =.05, =.01, =0.001\n\n\nNamed numeric vector for custom stars such as c(’*’ = .1, ‘+’ = .05)\n\n\nNote: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use \"glue strings\" with stars.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\ngof_map\n\n\nrename, reorder, and omit goodness-of-fit statistics and other model information. This argument accepts 4 types of values:\n\n\nNULL (default): the modelsummary::gof_map dictionary is used for formatting, and all unknown statistic are included.\n\n\ncharacter vector: \"all\", \"none\", or a vector of statistics such as c(“rmse”, “nobs”, “r.squared”). Elements correspond to colnames in the data.frame produced by get_gof(model). The modelsummary::gof_map default dictionary is used to format and rename statistics.\n\n\nNA: excludes all statistics from the bottom part of the table.\n\n\ndata.frame with 3 columns named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples’ section below. The fmt column in this data frame only accepts integers. For more flexibility, use a list of lists, as described in the next bullet.\n\n\nlist of lists, each of which includes 3 elements named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples section below’.\n\n\n\n\n\n\ngof_omit\n\n\nstring regular expression (perl-compatible) used to determine which statistics to omit from the bottom section of the table. A \"negative lookahead\" can be used to specify which statistics to keep in the table. Examples:\n\n\n“IC”: omit statistics matching the \"IC\" substring.\n\n\n“BIC|AIC”: omit statistics matching the \"AIC\" or \"BIC\" substrings.\n\n\n“^(?!.*IC)“: keep statistics matching the”IC\" substring.\n\n\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\ngroup_map\n\n\nnamed or unnamed character vector. Subset, rename, and reorder coefficient groups specified a grouping variable specified in the shape argument formula. This argument behaves like coef_map.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as #’ your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the columns positions. See Examples section below.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nThe modelsummary_list output is a lightweight format which can be used to save model results, so they can be fed back to modelsummary later to avoid extracting results again.\nWhen a file name with a valid extension is supplied to the output argument, the table is written immediately to file. If you want to customize your table by post-processing it with an external package, you need to choose a different output format and saving mechanism. Unfortunately, the approach differs from package to package:\n\n\ntinytable: set output=“tinytable”, post-process your table, and use the tinytable::save_tt function.\n\n\ngt: set output=“gt”, post-process your table, and use the gt::gtsave function.\n\n\nkableExtra: set output to your destination format (e.g., \"latex\", \"html\", \"markdown\"), post-process your table, and use kableExtra::save_kable function.\n\n\n\nTo use a string such as \"robust\" or \"HC0\", your model must be supported by the sandwich package. This includes objects such as: lm, glm, survreg, coxph, mlogit, polr, hurdle, zeroinfl, and more.\nNULL, \"classical\", \"iid\", and \"constant\" are aliases which do not modify uncertainty estimates and simply report the default standard errors stored in the model object.\nOne-sided formulas such as ~clusterid are passed to the sandwich::vcovCL function.\nMatrices and functions producing variance-covariance matrices are first passed to lmtest. If this does not work, modelsummary attempts to take the square root of the diagonal to adjust \"std.error\", but the other uncertainty estimates are not be adjusted.\nNumeric vectors are formatted according to fmt and placed in brackets. Character vectors printed as given, without parentheses.\nIf your model type is supported by the lmtest package, the vcov argument will try to use that package to adjust all the uncertainty estimates, including \"std.error\", \"statistic\", \"p.value\", and \"conf.int\". If your model is not supported by lmtest, only the \"std.error\" will be adjusted by, for example, taking the square root of the matrix’s diagonal.\n\na regression table in a format determined by the output argument.\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nIt can take a long time to compute and extract summary statistics from certain models (e.g., Bayesian). In those cases, users can parallelize the process. Since parallelization occurs at the model level, no speedup is available for tables with a single model. Users on mac or linux can launch parallel computation using the built-in parallel package. All they need to do is supply a mc.cores argument which will be pushed forward to the parallel::mclapply function:\n\nmodelsummary(model_list, mc.cores = 5)\n\n\nAll users can also use the future.apply package to parallelize model summaries. For example, to use 4 cores to extract results:\n\nlibrary(future.apply)\nplan(multicore, workers = 4)\noptions(\"modelsummary_future\" = TRUE)\nmodelsummary(model_list)\n\n\nNote that the \"multicore\" plan only parallelizes under mac or linux. Windows users can use plan(multisession) instead. However, note that the first time modelsummary() is called under multisession can be a fair bit longer, because of extra costs in passing data to and loading required packages on to workers. Subsequent calls to modelsummary() will often be much faster.\nSome users have reported difficult to reproduce errors when using the future package with some packages. The future parallelization in modelsummary can be disabled by calling:\noptions(“modelsummary_future” = FALSE)\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\n# The `modelsummary` website includes \\emph{many} examples and tutorials:\n# https://modelsummary.com\n\nlibrary(modelsummary)\n\n# load data and estimate models\nutils::data(trees)\nmodels &lt;- list()\nmodels[['Bivariate']] &lt;- lm(Girth ~ Height, data = trees)\nmodels[['Multivariate']] &lt;- lm(Girth ~ Height + Volume, data = trees)\n\n# simple table\nmodelsummary(models)\n\n# statistic\nmodelsummary(models, statistic = NULL)\n\nmodelsummary(models, statistic = 'p.value')\n\nmodelsummary(models, statistic = 'statistic')\n\nmodelsummary(models, statistic = 'conf.int', conf_level = 0.99)\n\nmodelsummary(models, statistic = c(\"t = {statistic}\",\n                                   \"se = {std.error}\",\n                                   \"conf.int\"))\n\n# estimate\nmodelsummary(models,\n  statistic = NULL,\n  estimate = \"{estimate} [{conf.low}, {conf.high}]\")\n\nmodelsummary(models,\n  estimate = c(\"{estimate}{stars}\",\n               \"{estimate} ({std.error})\"))\n\n# vcov\nmodelsummary(models, vcov = \"robust\")\n\nmodelsummary(models, vcov = list(\"classical\", \"stata\"))\n\nmodelsummary(models, vcov = sandwich::vcovHC)\n\nmodelsummary(models,\n  vcov = list(stats::vcov, sandwich::vcovHC))\n\nmodelsummary(models,\n  vcov = list(c(\"(Intercept)\"=\"\", \"Height\"=\"!\"),\n              c(\"(Intercept)\"=\"\", \"Height\"=\"!\", \"Volume\"=\"!!\")))\n\n# vcov with custom names\nmodelsummary(\n  models,\n  vcov = list(\"Stata Corp\" = \"stata\",\n              \"Newey Lewis & the News\" = \"NeweyWest\"))\n\n# fmt\nmod &lt;- lm(mpg ~ hp + drat + qsec, data = mtcars)\n\nmodelsummary(mod, fmt = 3)\n\nmodelsummary(mod, fmt = fmt_significant(3))\n\nmodelsummary(mod, fmt = NULL)\n\nmodelsummary(mod, fmt = fmt_decimal(4))\n\nmodelsummary(mod, fmt = fmt_sprintf(\"%.5f\"))\n\nmodelsummary(mod, fmt = fmt_statistic(estimate = 4, conf.int = 1), statistic = \"conf.int\")\n\nmodelsummary(mod, fmt = fmt_term(hp = 4, drat = 1, default = 2))\n\nm &lt;- lm(mpg ~ I(hp * 1000) + drat, data = mtcars)\nf &lt;- function(x) format(x, digits = 3, nsmall = 2, scientific = FALSE, trim = TRUE)\nmodelsummary(m, fmt = f, gof_map = NA)\n\n# coef_rename\nmodelsummary(models, coef_rename = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_rename = toupper)\n\nmodelsummary(models, coef_rename = coef_rename)\n\n# coef_rename = TRUE for variable labels\ndatlab &lt;- mtcars\ndatlab$cyl &lt;- factor(datlab$cyl)\nattr(datlab$hp, \"label\") &lt;- \"Horsepower\"\nattr(datlab$cyl, \"label\") &lt;- \"Cylinders\"\nmodlab &lt;- lm(mpg ~ hp * drat + cyl, data = datlab)\nmodelsummary(modlab, coef_rename = TRUE)\n\n# coef_rename: unnamed vector of length equal to the number of terms in the final table\nm &lt;- lm(hp ~ mpg + factor(cyl), data = mtcars)\nmodelsummary(m, coef_omit = -(3:4), coef_rename = c(\"Cyl 6\", \"Cyl 8\"))\n\n# coef_map\nmodelsummary(models, coef_map = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_map = c('Volume', 'Height'))\n\n# coef_omit: omit the first and second coefficients\nmodelsummary(models, coef_omit = 1:2)\n\n# coef_omit: omit coefficients matching one substring\nmodelsummary(models, coef_omit = \"ei\", gof_omit = \".*\")\n\n# coef_omit: omit a specific coefficient\nmodelsummary(models, coef_omit = \"^Volume$\", gof_omit = \".*\")\n\n# coef_omit: omit coefficients matching either one of two substring\n#modelsummary(models, coef_omit = \"ei|rc\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients starting with a substring (using a negative lookahead)\n#modelsummary(models, coef_omit = \"^(?!Vol)\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients matching a substring\nmodelsummary(models, coef_omit = \"^(?!.*ei|.*pt)\", gof_omit = \".*\")\n\n# shape: multinomial model\nlibrary(nnet)\nmulti &lt;- multinom(factor(cyl) ~ mpg + hp, data = mtcars, trace = FALSE) \n\n# shape: term names and group ids in rows, models in columns\nmodelsummary(multi, shape = response ~ model)\n\n# shape: term names and group ids in rows in a single column\nmodelsummary(multi, shape = term : response ~ model)\n\n# shape: term names in rows and group ids in columns\nmodelsummary(multi, shape = term ~ response:model)\n\n# shape = \"rcollapse\"\npanels &lt;- list(\n    \"Panel A: MPG\" = list(\n        \"A\" = lm(mpg ~ hp, data = mtcars),\n        \"B\" = lm(mpg ~ hp + factor(gear), data = mtcars)),\n    \"Panel B: Displacement\" = list(\n        \"A\" = lm(disp ~ hp, data = mtcars),\n        \"C\" = lm(disp ~ hp + factor(gear), data = mtcars))\n)\n\n# shape = \"cbind\"\nmodelsummary(panels, shape = \"cbind\")\n\nmodelsummary(\n    panels,\n    shape = \"rbind\",\n    gof_map = c(\"nobs\", \"r.squared\"))\n\n# title\nmodelsummary(models, title = 'This is the title')\n\n# title with LaTeX label (for numbering and referencing)\nmodelsummary(models, title = 'This is the title \\\\label{tab:description}')\n\n# add_rows\nrows &lt;- tibble::tribble(~term, ~Bivariate, ~Multivariate,\n  'Empty row', '-', '-',\n  'Another empty row', '?', '?')\nattr(rows, 'position') &lt;- c(1, 3)\nmodelsummary(models, add_rows = rows)\n\n# notes\nmodelsummary(models, notes = list('A first note', 'A second note'))\n\n# gof_map: tribble\nlibrary(tibble)\ngm &lt;- tribble(\n  ~raw,        ~clean,      ~fmt,\n  \"r.squared\", \"R Squared\", 5)\nmodelsummary(models, gof_map = gm)",
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-    "text": "Formula\nThe left side of the formula represents the rows and the right side represents the columns. The default formula is term + statistic ~ model:\n\nm &lt;- list(\n    lm(mpg ~ hp, data = mtcars),\n    lm(mpg ~ hp + drat, data = mtcars))\n\nmodelsummary(m, shape = term + statistic ~ model, gof_map = NA)\n\n \n\n  \n    \n\ntinytable_l4izyk5a1fd5tkp0rtks\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  30.099 \n                  10.790 \n                \n\n           \n                  (1.634)\n                  (5.078)\n                \n\nhp         \n                  -0.068 \n                  -0.052 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\ndrat       \n                         \n                  4.698  \n                \n\n           \n                         \n                  (1.192)\n                \n\n\n\n\n    \n\n\nWe can display statistics horizontally with:\n\nmodelsummary(m,\n             shape = term ~ model + statistic,\n             statistic = \"conf.int\",\n             gof_map = NA)\n\n \n\n  \n    \n\ntinytable_c6tuxvc6ncnmmettxgiu\n\n\n      \n\n\n \n(1)\n(2)\n\n\n \n                Est.\n                2.5 %\n                97.5 %\n                Est.\n                2.5 %\n                97.5 %\n              \n\n\n\n(Intercept)\n                  30.099\n                  26.762\n                  33.436\n                  10.790\n                  0.405 \n                  21.175\n                \n\nhp         \n                  -0.068\n                  -0.089\n                  -0.048\n                  -0.052\n                  -0.071\n                  -0.033\n                \n\ndrat       \n                        \n                        \n                        \n                  4.698 \n                  2.261 \n                  7.135 \n                \n\n\n\n\n    \n\n\nThe order of terms in the formula determines the order of headers in the table.\n\nmodelsummary(m,\n             shape = term ~ statistic + model,\n             statistic = \"conf.int\",\n             gof_map = NA)\n\n \n\n  \n    \n\ntinytable_e2ehntwti8u4juuwm28v\n\n\n      \n\n\n \nEst.\n2.5 %\n97.5 %\n\n\n \n                (1)\n                (2)\n                (1)\n                (2)\n                (1)\n                (2)\n              \n\n\n\n(Intercept)\n                  30.099\n                  10.790\n                  26.762\n                  0.405 \n                  33.436\n                  21.175\n                \n\nhp         \n                  -0.068\n                  -0.052\n                  -0.089\n                  -0.071\n                  -0.048\n                  -0.033\n                \n\ndrat       \n                        \n                  4.698 \n                        \n                  2.261 \n                        \n                  7.135 \n                \n\n\n\n\n    \n\n\nshape does partial matching and will try to fill-in incomplete formulas:\n\nmodelsummary(m, shape = ~ statistic)\n\nSome models like multinomial logit or GAMLSS produce “grouped” parameter estimates. To display these groups, we can include a group identifier in the shape formula. This group identifier must be one of the column names produced by get_estimates(model). For example, in models produced by nnet::multinom, the group identifier is called “response”:\n\nlibrary(nnet)\n\ndat_multinom &lt;- mtcars\ndat_multinom$cyl &lt;- sprintf(\"Cyl: %s\", dat_multinom$cyl)\n\nmod &lt;- list(\n    nnet::multinom(cyl ~ mpg, data = dat_multinom, trace = FALSE),\n    nnet::multinom(cyl ~ mpg + drat, data = dat_multinom, trace = FALSE))\n\nget_estimates(mod[[1]])\n\n         term  estimate std.error conf.level    conf.low   conf.high statistic df.error    p.value response s.value group\n1 (Intercept) 47.252432 34.975171       0.95 -21.2976435 115.8025065  1.351028      Inf 0.17668650   Cyl: 6     2.5      \n2         mpg -2.205418  1.637963       0.95  -5.4157653   1.0049299 -1.346440      Inf 0.17816078   Cyl: 6     2.5      \n3 (Intercept) 72.440246 37.175162       0.95  -0.4217332 145.3022247  1.948619      Inf 0.05134088   Cyl: 8     4.3      \n4         mpg -3.579991  1.774693       0.95  -7.0583242  -0.1016573 -2.017246      Inf 0.04366989   Cyl: 8     4.5      \n\n\nTo summarize the results, we can type:\n\n# modelsummary(mod, shape = term + response ~ statistic)\n\nThe terms of the shape formula above can of course be rearranged to reshape the table. For example:\n\nmodelsummary(mod, shape = model + term ~ response)\n\n \n\n  \n    \n\ntinytable_mfvwaob94a1z1gzjddig\n\n\n      \n\n \n                  \n                Cyl: 6\n                Cyl: 8\n              \n\n\n(1)\n                  (Intercept)\n                  47.252  \n                  72.440  \n                \n\n   \n                             \n                  (34.975)\n                  (37.175)\n                \n\n   \n                  mpg        \n                  -2.205  \n                  -3.580  \n                \n\n   \n                             \n                  (1.638) \n                  (1.775) \n                \n\n(2)\n                  (Intercept)\n                  89.573  \n                  117.971 \n                \n\n   \n                             \n                  (86.884)\n                  (87.998)\n                \n\n   \n                  mpg        \n                  -3.627  \n                  -4.838  \n                \n\n   \n                             \n                  (3.869) \n                  (3.915) \n                \n\n   \n                  drat       \n                  -3.210  \n                  -5.028  \n                \n\n   \n                             \n                  (3.810) \n                  (4.199) \n                \n\n\n\n\n    \n\n\nWe can combine the term and group identifier columns by inserting an interaction colon : instead of the + in the formula:\n\nlibrary(marginaleffects)\nmod &lt;- glm(am ~ mpg + factor(cyl), family = binomial, data = mtcars)\nmfx &lt;- avg_slopes(mod)\n\nmodelsummary(mfx, shape = term + contrast ~ model)\n\n \n\n  \n    \n\ntinytable_glywgw6ov80uyl6a7ck3\n\n\n      \n\n \n                  \n                (1)\n              \n\n\ncyl     \n                  mean(6) - mean(4)\n                  0.097  \n                \n\n        \n                                   \n                  (0.166)\n                \n\n        \n                  mean(8) - mean(4)\n                  0.093  \n                \n\n        \n                                   \n                  (0.234)\n                \n\nmpg     \n                  mean(dY/dX)      \n                  0.056  \n                \n\n        \n                                   \n                  (0.027)\n                \n\nNum.Obs.\n                                   \n                  32     \n                \n\nAIC     \n                                   \n                  37.4   \n                \n\nBIC     \n                                   \n                  43.3   \n                \n\nLog.Lik.\n                                   \n                  -14.702\n                \n\nF       \n                                   \n                  2.236  \n                \n\nRMSE    \n                                   \n                  0.39   \n                \n\n\n\n\n    \n\n\n\nmodelsummary(mfx, shape = term : contrast ~ model)\n\n \n\n  \n    \n\ntinytable_pdokaa3n2yqlof1ipwks\n\n\n      \n\n \n                (1)\n              \n\n\ncyl mean(6) - mean(4)\n                  0.097  \n                \n\n                     \n                  (0.166)\n                \n\ncyl mean(8) - mean(4)\n                  0.093  \n                \n\n                     \n                  (0.234)\n                \n\nmpg mean(dY/dX)      \n                  0.056  \n                \n\n                     \n                  (0.027)\n                \n\nNum.Obs.             \n                  32     \n                \n\nAIC                  \n                  37.4   \n                \n\nBIC                  \n                  43.3   \n                \n\nLog.Lik.             \n                  -14.702\n                \n\nF                    \n                  2.236  \n                \n\nRMSE                 \n                  0.39",
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+    "text": "Create beautiful and customizable tables to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, Typst, PDF, PowerPoint, Excel, RTF, JPG, or PNG. The appearance of the tables can be customized extensively by specifying the output argument, and by using functions from one of the supported table customization packages: tinytable, kableExtra, gt, flextable, huxtable, DT. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nThe modelsummary Vignette includes dozens of examples of tables with extensive customizations.\n\n\nThe Appearance Vignette shows how to modify the look of tables.\n\n\nmodelsummary(\n  models,\n  output = \"default\",\n  fmt = 3,\n  estimate = \"estimate\",\n  statistic = \"std.error\",\n  vcov = NULL,\n  conf_level = 0.95,\n  exponentiate = FALSE,\n  stars = FALSE,\n  shape = term + statistic ~ model,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = FALSE,\n  gof_map = NULL,\n  gof_omit = NULL,\n  gof_function = NULL,\n  group_map = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  align = NULL,\n  notes = NULL,\n  title = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_decimal(digits = 2, pdigits = 3): Decimal digits for estimate and p values\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_term(“(Intercept)” = 1, “X” = 2): Format terms differently\n\n\nfmt = fmt_statistic(“estimate” = 1, “r.squared” = 6): Format statistics differently.\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\nstring:\n\n\nNote on LaTeX output: To ensure proper typography, all numeric entries are enclosed in the  command, which requires the siunitx package to be loaded in the LaTeX preamble. This behavior can be altered with global options. See the ‘Details’ section.\n\n\n\n\n\n\nestimate\n\n\na single string or a character vector of length equal to the number of models. Valid entries include any column name of the data.frame produced by get_estimates(model), and strings with curly braces compatible with the glue package format. Examples:\n\n\n“estimate”\n\n\n“{estimate} ({std.error}){stars}”\n\n\n“{estimate} [{conf.low}, {conf.high}]”\n\n\n\n\n\n\nstatistic\n\n\nvector of strings or glue strings which select uncertainty statistics to report vertically below the estimate. NULL omits all uncertainty statistics.\n\n\n\"conf.int\", \"std.error\", \"statistic\", \"p.value\", \"conf.low\", \"conf.high\", or any column name produced by get_estimates(model)\n\n\nglue package strings with braces, with or without R functions, such as:\n\n\n“{p.value} [{conf.low}, {conf.high}]”\n\n\n“Std.Error: {std.error}”\n\n\n“{exp(estimate) * std.error}”\n\n\nNumbers are automatically rounded and converted to strings. To apply functions to their numeric values, as in the last glue example, users must set fmt=NULL.\n\n\nParentheses are added automatically unless the string includes glue curly braces {}.\n\n\n\n\nNotes:\n\n\nThe names of the statistic are used a column names when using the shape argument to display statistics as columns:\n\n\nstatistic=c(“p”=“p.value”, “[”=”conf.low”, ”]”=“conf.high”)\n\n\n\n\nSome statistics are not supported for all models. See column names in get_estimates(model), and visit the website to learn how to add custom statistics.\n\n\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nstars\n\n\nto indicate statistical significance\n\n\nFALSE (default): no significance stars.\n\n\nTRUE: +=.1, =.05, =.01, =0.001\n\n\nNamed numeric vector for custom stars such as c(’*’ = .1, ‘+’ = .05)\n\n\nNote: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use \"glue strings\" with stars.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\ngof_map\n\n\nrename, reorder, and omit goodness-of-fit statistics and other model information. This argument accepts 4 types of values:\n\n\nNULL (default): the modelsummary::gof_map dictionary is used for formatting, and all unknown statistic are included.\n\n\ncharacter vector: \"all\", \"none\", or a vector of statistics such as c(“rmse”, “nobs”, “r.squared”). Elements correspond to colnames in the data.frame produced by get_gof(model). The modelsummary::gof_map default dictionary is used to format and rename statistics.\n\n\nNA: excludes all statistics from the bottom part of the table.\n\n\ndata.frame with 3 columns named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples’ section below. The fmt column in this data frame only accepts integers. For more flexibility, use a list of lists, as described in the next bullet.\n\n\nlist of lists, each of which includes 3 elements named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples section below’.\n\n\n\n\n\n\ngof_omit\n\n\nstring regular expression (perl-compatible) used to determine which statistics to omit from the bottom section of the table. A \"negative lookahead\" can be used to specify which statistics to keep in the table. Examples:\n\n\n“IC”: omit statistics matching the \"IC\" substring.\n\n\n“BIC|AIC”: omit statistics matching the \"AIC\" or \"BIC\" substrings.\n\n\n“^(?!.*IC)“: keep statistics matching the”IC\" substring.\n\n\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\ngroup_map\n\n\nnamed or unnamed character vector. Subset, rename, and reorder coefficient groups specified a grouping variable specified in the shape argument formula. This argument behaves like coef_map.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as #’ your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the columns positions. See Examples section below.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nThe modelsummary_list output is a lightweight format which can be used to save model results, so they can be fed back to modelsummary later to avoid extracting results again.\nWhen a file name with a valid extension is supplied to the output argument, the table is written immediately to file. If you want to customize your table by post-processing it with an external package, you need to choose a different output format and saving mechanism. Unfortunately, the approach differs from package to package:\n\n\ntinytable: set output=“tinytable”, post-process your table, and use the tinytable::save_tt function.\n\n\ngt: set output=“gt”, post-process your table, and use the gt::gtsave function.\n\n\nkableExtra: set output to your destination format (e.g., \"latex\", \"html\", \"markdown\"), post-process your table, and use kableExtra::save_kable function.\n\n\n\nTo use a string such as \"robust\" or \"HC0\", your model must be supported by the sandwich package. This includes objects such as: lm, glm, survreg, coxph, mlogit, polr, hurdle, zeroinfl, and more.\nNULL, \"classical\", \"iid\", and \"constant\" are aliases which do not modify uncertainty estimates and simply report the default standard errors stored in the model object.\nOne-sided formulas such as ~clusterid are passed to the sandwich::vcovCL function.\nMatrices and functions producing variance-covariance matrices are first passed to lmtest. If this does not work, modelsummary attempts to take the square root of the diagonal to adjust \"std.error\", but the other uncertainty estimates are not be adjusted.\nNumeric vectors are formatted according to fmt and placed in brackets. Character vectors printed as given, without parentheses.\nIf your model type is supported by the lmtest package, the vcov argument will try to use that package to adjust all the uncertainty estimates, including \"std.error\", \"statistic\", \"p.value\", and \"conf.int\". If your model is not supported by lmtest, only the \"std.error\" will be adjusted by, for example, taking the square root of the matrix’s diagonal.\n\na regression table in a format determined by the output argument.\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nIt can take a long time to compute and extract summary statistics from certain models (e.g., Bayesian). In those cases, users can parallelize the process. Since parallelization occurs at the model level, no speedup is available for tables with a single model. Users on mac or linux can launch parallel computation using the built-in parallel package. All they need to do is supply a mc.cores argument which will be pushed forward to the parallel::mclapply function:\n\nmodelsummary(model_list, mc.cores = 5)\n\n\nAll users can also use the future.apply package to parallelize model summaries. For example, to use 4 cores to extract results:\n\nlibrary(future.apply)\nplan(multicore, workers = 4)\noptions(\"modelsummary_future\" = TRUE)\nmodelsummary(model_list)\n\n\nNote that the \"multicore\" plan only parallelizes under mac or linux. Windows users can use plan(multisession) instead. However, note that the first time modelsummary() is called under multisession can be a fair bit longer, because of extra costs in passing data to and loading required packages on to workers. Subsequent calls to modelsummary() will often be much faster.\nSome users have reported difficult to reproduce errors when using the future package with some packages. The future parallelization in modelsummary can be disabled by calling:\noptions(“modelsummary_future” = FALSE)\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\n# The `modelsummary` website includes \\emph{many} examples and tutorials:\n# https://modelsummary.com\n\nlibrary(modelsummary)\n\n# load data and estimate models\nutils::data(trees)\nmodels &lt;- list()\nmodels[['Bivariate']] &lt;- lm(Girth ~ Height, data = trees)\nmodels[['Multivariate']] &lt;- lm(Girth ~ Height + Volume, data = trees)\n\n# simple table\nmodelsummary(models)\n\n# statistic\nmodelsummary(models, statistic = NULL)\n\nmodelsummary(models, statistic = 'p.value')\n\nmodelsummary(models, statistic = 'statistic')\n\nmodelsummary(models, statistic = 'conf.int', conf_level = 0.99)\n\nmodelsummary(models, statistic = c(\"t = {statistic}\",\n                                   \"se = {std.error}\",\n                                   \"conf.int\"))\n\n# estimate\nmodelsummary(models,\n  statistic = NULL,\n  estimate = \"{estimate} [{conf.low}, {conf.high}]\")\n\nmodelsummary(models,\n  estimate = c(\"{estimate}{stars}\",\n               \"{estimate} ({std.error})\"))\n\n# vcov\nmodelsummary(models, vcov = \"robust\")\n\nmodelsummary(models, vcov = list(\"classical\", \"stata\"))\n\nmodelsummary(models, vcov = sandwich::vcovHC)\n\nmodelsummary(models,\n  vcov = list(stats::vcov, sandwich::vcovHC))\n\nmodelsummary(models,\n  vcov = list(c(\"(Intercept)\"=\"\", \"Height\"=\"!\"),\n              c(\"(Intercept)\"=\"\", \"Height\"=\"!\", \"Volume\"=\"!!\")))\n\n# vcov with custom names\nmodelsummary(\n  models,\n  vcov = list(\"Stata Corp\" = \"stata\",\n              \"Newey Lewis & the News\" = \"NeweyWest\"))\n\n# fmt\nmod &lt;- lm(mpg ~ hp + drat + qsec, data = mtcars)\n\nmodelsummary(mod, fmt = 3)\n\nmodelsummary(mod, fmt = fmt_significant(3))\n\nmodelsummary(mod, fmt = NULL)\n\nmodelsummary(mod, fmt = fmt_decimal(4))\n\nmodelsummary(mod, fmt = fmt_sprintf(\"%.5f\"))\n\nmodelsummary(mod, fmt = fmt_statistic(estimate = 4, conf.int = 1), statistic = \"conf.int\")\n\nmodelsummary(mod, fmt = fmt_term(hp = 4, drat = 1, default = 2))\n\nm &lt;- lm(mpg ~ I(hp * 1000) + drat, data = mtcars)\nf &lt;- function(x) format(x, digits = 3, nsmall = 2, scientific = FALSE, trim = TRUE)\nmodelsummary(m, fmt = f, gof_map = NA)\n\n# coef_rename\nmodelsummary(models, coef_rename = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_rename = toupper)\n\nmodelsummary(models, coef_rename = coef_rename)\n\n# coef_rename = TRUE for variable labels\ndatlab &lt;- mtcars\ndatlab$cyl &lt;- factor(datlab$cyl)\nattr(datlab$hp, \"label\") &lt;- \"Horsepower\"\nattr(datlab$cyl, \"label\") &lt;- \"Cylinders\"\nmodlab &lt;- lm(mpg ~ hp * drat + cyl, data = datlab)\nmodelsummary(modlab, coef_rename = TRUE)\n\n# coef_rename: unnamed vector of length equal to the number of terms in the final table\nm &lt;- lm(hp ~ mpg + factor(cyl), data = mtcars)\nmodelsummary(m, coef_omit = -(3:4), coef_rename = c(\"Cyl 6\", \"Cyl 8\"))\n\n# coef_map\nmodelsummary(models, coef_map = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_map = c('Volume', 'Height'))\n\n# coef_omit: omit the first and second coefficients\nmodelsummary(models, coef_omit = 1:2)\n\n# coef_omit: omit coefficients matching one substring\nmodelsummary(models, coef_omit = \"ei\", gof_omit = \".*\")\n\n# coef_omit: omit a specific coefficient\nmodelsummary(models, coef_omit = \"^Volume$\", gof_omit = \".*\")\n\n# coef_omit: omit coefficients matching either one of two substring\n#modelsummary(models, coef_omit = \"ei|rc\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients starting with a substring (using a negative lookahead)\n#modelsummary(models, coef_omit = \"^(?!Vol)\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients matching a substring\nmodelsummary(models, coef_omit = \"^(?!.*ei|.*pt)\", gof_omit = \".*\")\n\n# shape: multinomial model\nlibrary(nnet)\nmulti &lt;- multinom(factor(cyl) ~ mpg + hp, data = mtcars, trace = FALSE) \n\n# shape: term names and group ids in rows, models in columns\nmodelsummary(multi, shape = response ~ model)\n\n# shape: term names and group ids in rows in a single column\nmodelsummary(multi, shape = term : response ~ model)\n\n# shape: term names in rows and group ids in columns\nmodelsummary(multi, shape = term ~ response:model)\n\n# shape = \"rcollapse\"\npanels &lt;- list(\n    \"Panel A: MPG\" = list(\n        \"A\" = lm(mpg ~ hp, data = mtcars),\n        \"B\" = lm(mpg ~ hp + factor(gear), data = mtcars)),\n    \"Panel B: Displacement\" = list(\n        \"A\" = lm(disp ~ hp, data = mtcars),\n        \"C\" = lm(disp ~ hp + factor(gear), data = mtcars))\n)\n\n# shape = \"cbind\"\nmodelsummary(panels, shape = \"cbind\")\n\nmodelsummary(\n    panels,\n    shape = \"rbind\",\n    gof_map = c(\"nobs\", \"r.squared\"))\n\n# title\nmodelsummary(models, title = 'This is the title')\n\n# title with LaTeX label (for numbering and referencing)\nmodelsummary(models, title = 'This is the title \\\\label{tab:description}')\n\n# add_rows\nrows &lt;- tibble::tribble(~term, ~Bivariate, ~Multivariate,\n  'Empty row', '-', '-',\n  'Another empty row', '?', '?')\nattr(rows, 'position') &lt;- c(1, 3)\nmodelsummary(models, add_rows = rows)\n\n# notes\nmodelsummary(models, notes = list('A first note', 'A second note'))\n\n# gof_map: tribble\nlibrary(tibble)\ngm &lt;- tribble(\n  ~raw,        ~clean,      ~fmt,\n  \"r.squared\", \"R Squared\", 5)\nmodelsummary(models, gof_map = gm)",
     "crumbs": [
       "Get started",
-      "Model Summaries"
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-    "text": "String (“rbind” or “rcollapse”): Panels of models in stacked regression tables\nNote: The code in this section requires version 1.3.0 or the development version of modelsummary. See the website for installation instructions.\nThis section shows how to “stack/bind” multiple regression tables on top of one another, to display the results several models side-by-side and top-to-bottom. For example, imagine that we want to present 4 different models, half of which are estimated using a different outcome variable. When using modelsummary, we store models in a list. When using modelsummary with shape=\"rbind\" or shape=\"rbind\", we store models in a list of lists:\n\ngm &lt;- c(\"r.squared\", \"nobs\", \"rmse\")\n\npanels &lt;- list(\n  list(\n    lm(mpg ~ 1, data = mtcars),\n    lm(mpg ~ qsec, data = mtcars)\n  ),\n  list(\n    lm(hp ~ 1, data = mtcars),\n    lm(hp ~ qsec, data = mtcars)\n  )\n)\n\nmodelsummary(\n  panels,\n  shape = \"rbind\",\n  gof_map = gm)\n\n \n\n  \n    \n\ntinytable_jk1lpt7aiun7oxk7angd\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  20.091  \n                  -5.114  \n                \n\n           \n                  (1.065) \n                  (10.030)\n                \n\nqsec       \n                          \n                  1.412   \n                \n\n           \n                          \n                  (0.559) \n                \n\nR2         \n                  0.000   \n                  0.175   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  5.93    \n                  5.39    \n                \n\n(Intercept)\n                  146.688 \n                  631.704 \n                \n\n           \n                  (12.120)\n                  (88.700)\n                \n\nqsec       \n                          \n                  -27.174 \n                \n\n           \n                          \n                  (4.946) \n                \n\nR2         \n                  0.000   \n                  0.502   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  67.48   \n                  47.64   \n                \n\n\n\n\n    \n\n\nLike with modelsummary(), we can can name models and panels by naming elements of our nested list:\n\npanels &lt;- list(\n  \"Outcome: mpg\" = list(\n    \"(I)\" = lm(mpg ~ 1, data = mtcars),\n    \"(II)\" = lm(mpg ~ qsec, data = mtcars)\n  ),\n  \"Outcome: hp\" = list(\n    \"(I)\" = lm(hp ~ 1, data = mtcars),\n    \"(II)\" = lm(hp ~ qsec, data = mtcars)\n  )\n)\n\nmodelsummary(\n  panels,\n  shape = \"rbind\",\n  gof_map = gm)\n\n \n\n  \n    \n\ntinytable_rmzveenlqp1so6t23e8g\n\n\n      \n\n \n                (I)\n                (II)\n              \n\n\n(Intercept)\n                  20.091  \n                  -5.114  \n                \n\n           \n                  (1.065) \n                  (10.030)\n                \n\nqsec       \n                          \n                  1.412   \n                \n\n           \n                          \n                  (0.559) \n                \n\nR2         \n                  0.000   \n                  0.175   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  5.93    \n                  5.39    \n                \n\n(Intercept)\n                  146.688 \n                  631.704 \n                \n\n           \n                  (12.120)\n                  (88.700)\n                \n\nqsec       \n                          \n                  -27.174 \n                \n\n           \n                          \n                  (4.946) \n                \n\nR2         \n                  0.000   \n                  0.502   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  67.48   \n                  47.64",
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+    "text": "A convenience function which can be passed to the coef_rename argument of the modelsummary function.\n\ncoef_rename(\n  x,\n  factor = TRUE,\n  factor_name = TRUE,\n  backticks = TRUE,\n  titlecase = TRUE,\n  underscore = TRUE,\n  asis = TRUE\n)\n\n\n\n\n\nx\n\n\ncharacter vector of term names to transform\n\n\n\n\nfactor\n\n\nboolean remove the \"factor()\" label\n\n\n\n\nfactor_name\n\n\nboolean remove the \"factor()\" label and the name of the variable\n\n\n\n\nbackticks\n\n\nboolean remove backticks\n\n\n\n\ntitlecase\n\n\nboolean convert to title case\n\n\n\n\nunderscore\n\n\nboolean replace underscores by spaces\n\n\n\n\nasis\n\n\nboolean remove the I from as-is formula calls\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\ndat &lt;- mtcars\ndat$horse_power &lt;- dat$hp\nmod &lt;- lm(mpg ~ horse_power + factor(cyl), dat)\nmodelsummary(mod, coef_rename = coef_rename)\n\n \n\n  \n    \n\ntinytable_t1yml4r8s3vt1wgfunii\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  28.650 \n                \n\n           \n                  (1.588)\n                \n\nHorse Power\n                  -0.024 \n                \n\n           \n                  (0.015)\n                \n\n6          \n                  -5.968 \n                \n\n           \n                  (1.639)\n                \n\n8          \n                  -8.521 \n                \n\n           \n                  (2.326)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.754  \n                \n\nR2 Adj.    \n                  0.727  \n                \n\nAIC        \n                  169.9  \n                \n\nBIC        \n                  177.2  \n                \n\nLog.Lik.   \n                  -79.948\n                \n\nF          \n                  28.585 \n                \n\nRMSE       \n                  2.94",
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-    "text": "String (“cbind”): Automatic spanning column labels\nWhen using the default tinytable backend, we can use a named nested list to automatically add spanning column labels to a table:\n\nmodelsummary(panels, shape = \"cbind\")\n\n \n\n  \n    \n\ntinytable_o936pec9nm6jwcsvccal\n\n\n      \n\n\n \nOutcome: mpg\nOutcome: hp\n\n\n \n                (I)\n                (II)\n                (I) \n                (II) \n              \n\n\n\n(Intercept)\n                  20.091  \n                  -5.114  \n                  146.688 \n                  631.704 \n                \n\n           \n                  (1.065) \n                  (10.030)\n                  (12.120)\n                  (88.700)\n                \n\nqsec       \n                          \n                  1.412   \n                          \n                  -27.174 \n                \n\n           \n                          \n                  (0.559) \n                          \n                  (4.946) \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                  32      \n                  32      \n                \n\nR2         \n                  0.000   \n                  0.175   \n                  0.000   \n                  0.502   \n                \n\nR2 Adj.    \n                  0.000   \n                  0.148   \n                  0.000   \n                  0.485   \n                \n\nAIC        \n                  208.8   \n                  204.6   \n                  364.4   \n                  344.1   \n                \n\nBIC        \n                  211.7   \n                  209.0   \n                  367.3   \n                  348.5   \n                \n\nLog.Lik.   \n                  -102.378\n                  -99.294 \n                  -180.186\n                  -169.045\n                \n\nF          \n                          \n                  6.377   \n                          \n                  30.190  \n                \n\nRMSE       \n                  5.93    \n                  5.39    \n                  67.48   \n                  47.64",
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+    "text": "A convenience function which can be passed to the coef_rename argument of the modelsummary function.\n\ncoef_rename(\n  x,\n  factor = TRUE,\n  factor_name = TRUE,\n  backticks = TRUE,\n  titlecase = TRUE,\n  underscore = TRUE,\n  asis = TRUE\n)\n\n\n\n\n\nx\n\n\ncharacter vector of term names to transform\n\n\n\n\nfactor\n\n\nboolean remove the \"factor()\" label\n\n\n\n\nfactor_name\n\n\nboolean remove the \"factor()\" label and the name of the variable\n\n\n\n\nbackticks\n\n\nboolean remove backticks\n\n\n\n\ntitlecase\n\n\nboolean convert to title case\n\n\n\n\nunderscore\n\n\nboolean replace underscores by spaces\n\n\n\n\nasis\n\n\nboolean remove the I from as-is formula calls\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\ndat &lt;- mtcars\ndat$horse_power &lt;- dat$hp\nmod &lt;- lm(mpg ~ horse_power + factor(cyl), dat)\nmodelsummary(mod, coef_rename = coef_rename)\n\n \n\n  \n    \n\ntinytable_t1yml4r8s3vt1wgfunii\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  28.650 \n                \n\n           \n                  (1.588)\n                \n\nHorse Power\n                  -0.024 \n                \n\n           \n                  (0.015)\n                \n\n6          \n                  -5.968 \n                \n\n           \n                  (1.639)\n                \n\n8          \n                  -8.521 \n                \n\n           \n                  (2.326)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.754  \n                \n\nR2 Adj.    \n                  0.727  \n                \n\nAIC        \n                  169.9  \n                \n\nBIC        \n                  177.2  \n                \n\nLog.Lik.   \n                  -79.948\n                \n\nF          \n                  28.585 \n                \n\nRMSE       \n                  2.94",
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-    "text": "Renaming estimates and statistics\nWhen statistics are displayed in different columns, users may want control over the statistic names. To do so, we specify the estimate and statistic as named vectors:\n\nmod &lt;- lm(mpg ~ factor(cyl), mtcars)\n\nmodelsummary(\n    mod, \n    estimate = c(\"$\\\\hat{\\\\beta}$\" = \"estimate\"),\n    statistic = c(\"$\\\\sigma_{\\\\hat{\\\\beta}}$\" = \"std.error\"),\n    shape = term ~ model + statistic)\n\n \n\n  \n    \n\ntinytable_pd7bbcw5lf60sd2yj61u\n\n\n      \n\n\n \n(1)\n\n\n \n                $\\hat{\\beta}$\n                $\\sigma_{\\hat{\\beta}}$\n              \n\n\n\n(Intercept) \n                  26.664 \n                  0.972\n                \n\nfactor(cyl)6\n                  -6.921 \n                  1.558\n                \n\nfactor(cyl)8\n                  -11.564\n                  1.299\n                \n\nNum.Obs.    \n                  32     \n                       \n                \n\nR2          \n                  0.732  \n                       \n                \n\nR2 Adj.     \n                  0.714  \n                       \n                \n\nAIC         \n                  170.6  \n                       \n                \n\nBIC         \n                  176.4  \n                       \n                \n\nLog.Lik.    \n                  -81.282\n                       \n                \n\nF           \n                  39.698 \n                       \n                \n\nRMSE        \n                  3.07   \n                       \n                \n\n\n\n\n    \n\n\nfixest\nThe fixest package offers powerful tools to estimate multiple models using a concise syntax. fixest functions are also convenient because they return named lists of models which are easy to subset and manipulate using standard R functions like grepl.\nFor example, to introduce regressors in stepwise fashion, and to estimate models on different subsets of the data, we can do:\n\n# estimate 4 models\nlibrary(fixest)\nmod &lt;- feols(\n  c(hp, mpg) ~ csw(qsec, drat) | gear,\n  data = mtcars)\n\n# select models with different outcome variables\npanels &lt;- list(\n  \"Miles per gallon\" = mod[grepl(\"mpg\", names(mod))],\n  \"Horsepower\" = mod[grepl(\"hp\", names(mod))]\n)\n\nmodelsummary(\n  panels,\n  shape = \"rcollapse\",\n  gof_omit = \"IC|R2\")\n\n \n\n  \n    \n\ntinytable_pmlwp438dzqjq74rpai3\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\nqsec      \n                  1.436   \n                  1.519   \n                \n\n          \n                  (0.594) \n                  (0.529) \n                \n\ndrat      \n                          \n                  5.765   \n                \n\n          \n                          \n                  (2.381) \n                \n\nRMSE      \n                  4.03    \n                  3.67    \n                \n\nqsec      \n                  -22.175 \n                  -22.676 \n                \n\n          \n                  (12.762)\n                  (13.004)\n                \n\ndrat      \n                          \n                  -35.106 \n                \n\n          \n                          \n                  (28.509)\n                \n\nRMSE      \n                  40.45   \n                  39.14   \n                \n\nNum.Obs.  \n                  32      \n                  32      \n                \n\nStd.Errors\n                  by: gear\n                  by: gear\n                \n\nFE: gear  \n                  X       \n                  X       \n                \n\n\n\n\n    \n\n\nWe can use all the typical extension systems to add information, such as the mean of the dependent variable:\n\nglance_custom.fixest &lt;- function(x, ...) {\n  dv &lt;- insight::get_response(x)\n  dv &lt;- sprintf(\"%.2f\", mean(dv, na.rm = TRUE))\n  data.table::data.table(`Mean of DV` = dv)\n}\n\nmodelsummary(\n  panels,\n  shape = \"rcollapse\",\n  gof_omit = \"IC|R2\")\n\n \n\n  \n    \n\ntinytable_mcu1t5ajxmgpz9bkt32t\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\nqsec      \n                  1.436   \n                  1.519   \n                \n\n          \n                  (0.594) \n                  (0.529) \n                \n\ndrat      \n                          \n                  5.765   \n                \n\n          \n                          \n                  (2.381) \n                \n\nRMSE      \n                  4.03    \n                  3.67    \n                \n\nMean of DV\n                  20.09   \n                  20.09   \n                \n\nqsec      \n                  -22.175 \n                  -22.676 \n                \n\n          \n                  (12.762)\n                  (13.004)\n                \n\ndrat      \n                          \n                  -35.106 \n                \n\n          \n                          \n                  (28.509)\n                \n\nRMSE      \n                  40.45   \n                  39.14   \n                \n\nMean of DV\n                  146.69  \n                  146.69  \n                \n\nNum.Obs.  \n                  32      \n                  32      \n                \n\nStd.Errors\n                  by: gear\n                  by: gear\n                \n\nFE: gear  \n                  X       \n                  X       \n                \n\n\n\n\n    \n\nrm(\"glance_custom.fixest\")",
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-    "text": "Reference categories\nWhen estimating models with factor regressors, R will typically omit the reference category. We can include an empty row for the reference level automatically by using the include_reference argument. This argument is supplied by the parameters package, which modelsummary uses behind the scenes to extract model estimates. We can supply it directly to modelsummary(), which will then pass the argument forward automatically:\n\ntmp &lt;- transform(mtcars, cyl = factor(cyl))\nmod &lt;- lm(mpg ~ cyl, tmp)\nmodelsummary(mod, include_reference = TRUE)\n\n \n\n  \n    \n\ntinytable_x005147564gngte9a1nj\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  26.664 \n                \n\n           \n                  (0.972)\n                \n\ncyl4       \n                  -      \n                \n\ncyl6       \n                  -6.921 \n                \n\n           \n                  (1.558)\n                \n\ncyl8       \n                  -11.564\n                \n\n           \n                  (1.299)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.732  \n                \n\nR2 Adj.    \n                  0.714  \n                \n\nAIC        \n                  170.6  \n                \n\nBIC        \n                  176.4  \n                \n\nLog.Lik.   \n                  -81.282\n                \n\nF          \n                  39.698 \n                \n\nRMSE       \n                  3.07",
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+    "title": "modelsummary: Data and Model Summaries in R",
+    "section": "What?",
+    "text": "What?\nmodelsummary includes two families of functions:\n\nModel Summary\n\nmodelsummary: Regression tables with side-by-side models.\nmodelplot: Coefficient plots.\n\nData Summary\n\ndatasummary: Powerful tool to create (multi-level) cross-tabs and data summaries.\ndatasummary_crosstab: Cross-tabulations.\ndatasummary_balance: Balance tables with subgroup statistics and difference in means (aka “Table 1”).\ndatasummary_correlation: Correlation tables.\ndatasummary_skim: Quick overview (“skim”) of a dataset.\ndatasummary_df: Turn dataframes into nice tables with titles, notes, etc."
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+    "title": "modelsummary: Data and Model Summaries in R",
+    "section": "Why?",
+    "text": "Why?\nHere are a few benefits of modelsummary over some alternative packages:\n\nEasy\nmodelsummary is very easy to use. This simple call often suffices:\nlibrary(modelsummary)\n\nmod &lt;- lm(y ~ x, dat)\nThe command above will automatically display a summary table in the Rstudio Viewer or in a web browser. All you need is one word to change the output format. For example, a text-only version of the table can be printed to the Console by typing:\nmodelsummary(mod, output = \"markdown\")\nTables in Microsoft Word and LaTeX formats can be saved to file by typing:\nmodelsummary(mod, output = \"table.docx\")\nmodelsummary(mod, output = \"table.tex\")\n\n\nFlexible\nInformation: The package offers many intuitive and powerful utilities to customize the information reported in a summary table. You can rename, reorder, subset or omit parameter estimates; choose the set of goodness-of-fit statistics to include; display various “robust” standard errors or confidence intervals; add titles, footnotes, or source notes; insert stars or custom characters to indicate levels of statistical significance; or add rows with supplemental information about your models.\nAppearance: Thanks to the tinytable, gt, kableExtra, huxtable, flextable, and DT packages, the appearance of modelsummary tables is endlessly customizable. The appearance customization page shows tables with colored cells, weird text, spanning column labels, row groups, titles, source notes, footnotes, significance stars, and more. This only scratches the surface of possibilities.\nSupported models: Thanks to the broom and parameters, modelsummary supports hundreds of statistical models out-of-the-box. Installing other packages can extend the capabilities further (e.g., broom.mixed). It is also very easy to add or customize your own models.\nOutput formats: modelsummary tables can be saved to HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, RTF, JPG, or PNG formats. They can also be inserted seamlessly in Rmarkdown documents to produce automated documents and reports in PDF, HTML, RTF, or Microsoft Word formats.\n\n\nDangerous\nmodelsummary is dangerous! It allows users to do stupid stuff like inserting squirrels in all your tables.\n\n\n\nReliable\nmodelsummary is reliably dangerous! The package is developed using a suite of unit tests with about 95% coverage, so it (probably) won’t break.\n\n\nCommunity\nmodelsummary does not try to do everything. Instead, it leverages the incredible work of the R community. By building on top of the broom and parameters packages, modelsummary already supports hundreds of model types out-of-the-box. modelsummary also supports five of the most popular table-building and customization packages: gt, kableExtra, huxtable, flextable, and DT packages. By using those packages, modelsummary allows users to produce beautiful, endlessly customizable tables in a wide variety of formats, including HTML, PDF, LaTeX, Markdown, and MS Word.\nOne benefit of this community-focused approach is that when external packages improve, modelsummary improves as well. Another benefit is that leveraging external packages allows modelsummary to have a massively simplified codebase (relative to other similar packages). This should improve long term code maintainability, and allow contributors to participate through GitHub."
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+    "title": "modelsummary: Data and Model Summaries in R",
+    "section": "How?",
+    "text": "How?\nYou can install modelsummary from CRAN:\ninstall.packages('modelsummary')\nYou can install the development version of modelsummary and its dependencies from R-Universe:\ninstall.packages(\n    c(\"modelsummary\", \"tinytable\", \"insight\", \"performance\", \"parameters\"),\n    repos = c(\n        \"https://vincentarelbundock.r-universe.dev\",\n        \"https://easystats.r-universe.dev\"))\nRestart R completely before moving on."
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+    "title": "modelsummary: Data and Model Summaries in R",
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+    "text": "Get started\nThe GET STARTED vignette will walk you through the basics of modelsummary.\nYou will find that there are a million ways to customize the tables and plots produced by modelsummary, but the Get Started page only scratches the surface. For details, see the vignettes:\n\nData Summaries\nModel Summaries\nModel Plots\nmodelsummary extensions and customization\nThemes and Styles"
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+    "text": "New features:\n\nmodelsummary() gets a gof_function argument which accepts functions to extract custom information from models.\nflextable: Support spanning column headers\n\ndatasummary_balance():\n\nformula can now include variables on the left-hand side to indicate the subset of columns to summarize: datasummary_balance(mpg + hp ~ am, data = mtcars) Thanks to @etiennebacher for feature request #751.\n\nMinor:\n\nUnnecessary text printed to screen on some F stat computations is now suppressed.\n\nBugs:\n\nescape argument not respected in datasummary_df(). Thanks to @adamaltmejd for report #740\ndatasummary_correlation() supports data.table. Thanks to volatilehead on Twitter for report #737.\nAccepts named estimate argument when using shape and statistics are horizontal. Thanks to @iago-pssjd for report #745.\nLabelled values but no label for variable broke datasummary(). Thanks to @marklhc for report #752.\ncoef_map does not work when there is a group. Thanks to @mccarthy-m-g for report #757.\nkableExtra: fix spanning column headers when using the shape argument.\nMultiple footnotes and line breaks in footnotes are now allowed in tinytable output. Thanks to\n\n\n\n\nMAJOR BREAKING CHANGE: The default output format is now tinytable instead of kableExtra. Learn more about tinytable here:\nhttps://vincentarelbundock.github.io/tinytable/\nTo revert to the previous behavior persistently, users can call:\nlibrary(modelsummary) config_modelsummary(factory_default = “kableExtra”)\nOther breaking changes:\n\nThe statistic_override argument was replaced by vcov over 1 year ago, with appropriate deprecation warnings. It is now fully removed.\nThe group argument was replaced by shape several releases ago. It is now fully removed.\ndatasummary_skim()\n\nhistograms are only available with the tinytable backend. This allows a lot of code simplification and more customization.\nThe order of arguments type and output is switched for consistency with other functions.\nhistogram argument is deprecated.\n\n\nNew features:\n\ndatasummary_skim():\n\nNew type=\"all\" by default to display both numeric and categorical variables in a single table with distinct panels. This feature is only available with the tinytable backend (default).\nby argument allows summarizing numeric variables by group.\nfun_numeric argument accepts a list of functions to control the summary columns.\n\nmodelsummary():\n\nstatistic and estimate can be specified as named vectors to control the names of statistics when displayed in different columns using the shape argument. (Thanks to @mps9506 for bug report #722)\nmodelsummary(panels, shape = \"cbind\") automatically adds column-spanning labels when panels is a named nested list of models.\n\nconfig_modelsummary() gets a startup_message argument to silence the startup message persistently.\nImproved documentation and vignettes, providing clearer instructions and examples.\nUpdated tests and snapshots to ensure reliability and consistency across changes.\n\nBug fixes:\n\nFixed Issue #399: datasummary_balance() siunitx formatting.\nFixed Issue #782: Useless warning in some modelplot() calls. Thanks to @iago-pssjd for the report and @florence-laflamme for the fix.\nAddressed various bugs and made optimizations for better performance and user experience.\n\n\n\n\n\ntinytable supports histograms in datasummary_skim()\nconfig_modelsummary() supports tinytable factory.\n\n\n\n\n\nSupport the tinytable package as an output format (“factory”): https://vincentarelbundock.github.io/tinytable/\nQuarto: md output format is recognized.\noptions(modelsummary_factory_default) is respected, even in qmd-&gt;md documents.\n\nBugs:\n\nSome omitted coefficients with I() operator in formulas. Issue #693.\n\n\n\n\nMisc:\n\nDuplicate values in shape groups are removed automatically for cleaner labels.\n“Title” line no longer indented in markdown tables. Thanks to Ryan Briggs for report #671.\n\nBugs:\n\nSmall p values were not displayed properly in HTML output using kableExtra. Issue #669.\n\n\n\n\nNew:\n\nMinimal support for Typst output, with auto-detection in Quarto documents.\nstrip argument in dvnames.\ns.value statistic is now available whenever p.value is available. See Greenland (2019).\ndatasummary_skim() now includes histograms in gt tables.\n\nBugs:\n\nGOF term names get escaped in LaTeX. Thanks to @shreyasgm for reviving Issue #546.\nConflict with furrr generated errors for some models. Thanks to @sammo3182 for Issue #647.\n\n\n\n\nNew:\n\nfmt_sci() can now be used in the fmt argument for rounding with scientific notation.\n\nBugs:\n\nGroup separators respect add_rows with shape=\"rbind\". Thanks to @lrose1 for Report #626.\nBad column with horizontal models in shape and grouped estimates. Thanks to @iago-pssjd for Report #631.\ncoef_rename=TRUE bug with grouped estimates. Thanks to @iago-pssjd for Report #631.\nUpstream issue #881 in parameters meant that vcov was no longer used for confidence intervals.\n\n\n\n\n\nBuilt-in support for markdown tables.\nPackage no longer depends on kableExtra. Recommends an additional install for other formats.\nPersistent configuration of default output format: config_modelsummary(factory_default = \"gt\")\nshape = \"rcollapse\" and shape = \"rbind\"\nglance_custom() can drop GOF by assigning NA: https://stackoverflow.com/questions/75215355/assigning-different-coefficient-names-and-goodness-of-fit-statistics-to-differen\nWhen a statistic is not available, modelsummary prints an empty cell instead of returning an error.\n“\\label{tab:something}” works in title even when escape=TRUE\nMultiple fixest_multi objects supported.\noptions(modelsummary_future = FALSE) disables future parallelism.\n\nBug fixes:\n\nstatistic=NULL is now respected when shape=\"rbind\". Thanks to Panos Mavros for report #620.\nget_estimates() supports vcov string shortcuts and formulas. Thanks to @ethans-carl for report #605.\nQuarto and Rmarkdown documents include situnix in header automatically for decimal alignement with align=\"ddd\"\nescape is now respected by modelsummary with shape=\"rbind\". Thanks to @chickymonkeys for report #622.\n\n\n\n\nBreaking change:\n\nThe default column label style in modelsummary() has changed from “Model 1” to “(1)”. The benefits are: labels are no longer in English by default; use less horizontal space; eliminate the “Model” redundancy. Unfortunately, this could break code in some edge cases where users rely on column names to manipulate tables. The old behavior can be restored by calling: options(modelsummary_model_labels=\"model\")\n\nNew features:\n\nshape=\"rbind\" to stack multiple regression tables and create “panels” with labelled groups of models.\nfmt: new helper functions for different formatting styles\n\nfmt = fmt_decimal(2): decimal digits\nfmt = fmt_decimal(digits = 2, pdigits = 4): decimal digits with p value-specific setting\nfmt = fmt_sprintf(\"%.3f\"): sprintf() decimal\nfmt = fmt_sprintf(\"%.3e\"): sprintf() scientific\nfmt = fmt_significant(3): significant digits\nfmt = fmt_statistic(\"estimate\" = 2, \"std.error\" = 3): statistic-specific formatting\nfmt = fmt_term(\"(Intercept)\" = 2, \"hp\" = 3): term-specific formatting\nfmt = fmt_identity(): raw values\n\nNew styles for default column labels in modelsummary, such as Roman Numerals or letters in parentheses.\n\nSet the style with a global option: options(modelsummary_model_labels = \"roman\")\nSupported styles: “model”, “arabic”, “letters”, “roman”, “(arabic)”, “(letters)”, “(roman)””\n\nmodelplot(draw = FALSE) now returns a p.value column. This allows conditional aesthetics (see the modelplot vignette).\nBetter integration with the marginaleffects package.\n\nBugs:\n\nSome fixest models returns useless “group.x” and “group.y” columns. Isse #591. Thanks to Adam Altmejd for the report.\n\n\n\n\nBreaking change:\n\nWith the shape and output=\"dataframe\" arguments, there always used to be a group column. Now, this column has the same name as the variable in the shape formula (“response”, “component”, etc.).\n\nNew features:\n\nshape can include multiple groups.\ncoef_rename can be an unnamed vector of length equal to the number of terms in the final table, obtained after coef_map and coef_omit are applied and models are merged.\ncoef_omit accepts numeric indices. Positive values: coefficients to omit. Negative values: coefficients to keep.\ndatasummary_skim: Increased maximum number of variables to 250.\nQuarto notebooks compile to Word and Markdown automatically.\n\nBug fixes:\n\nOrder of notes preserved in some output format (Issue #577)\n\n\n\n\nBreaking change:\n\nRequires siunitx version 3.0.25 LaTeX package.\nThe title argument now respects the escape argument for all kableExtra output formats. This can break tables in which users manually escaped titles.\n\nNew features:\n\n“d” is accepted for decimal-alignment in the align argument for all output formats. modelsummary(mod, align = \"ld\")\nNew update_modelsummary() function makes it easy to install the dev versions of modelsummary and its dependencies (mostly useful for Vincent and people who report bugs).\nRounding: display at least one significant digit by default.\nAutomatic renaming of haven labels in modelsummary(), datasummary(), datasummary_skim()\nAllow output = \"filename.csv\"\nAllow output = \"filename.xlsx\"\nadd_columns argument supported in modelsummary()\ndatasummary_balance supports the stars argument.\nAllow stars and confidence intervals with align = \"d\" column.\n\nBug fixes:\n\nIn some locales, the HTML minus sign created problems in the output. We only use it in “known” locales.\nMany minor bug fixes\n\n\n\n\n\nMinor release to fix CRAN failure\n\n\n\n\n\nshape argument accepts interactions with the colon “:” character. This combines two columns into one, which can be useful to display terms and group names in a single column.\nParallelization using parallel::mclapply. See ?modelsummary\nmodelsummary no longer computes confidence intervals when not necessary, which can save some time. Also see: conf_level=NULL\nAdded log likelihood to GOF for lm and glm models.\nRemoved extraneous warnings\nBug fixes\n\n\n\n\nThis first major release accompanies the publication of an article in the Journal of Statistical Software:\nArel-Bundock, Vincent (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. doi:10.18637/jss.v103.i01 https://doi.org/10.18637/jss.v103.i01.’\nIf you like modelsummary, please cite the JSS article and tell your friends about it.\nMinor changes:\n\ngof_map=\"all\" includes all available statistics. gof_map=\"none\" excludes all statistics.\nBug fixes\n\n\n\n\n\nBetter printout for term names in mixed-effects models\n{brms} and {stanreg} models now extracted with diagnostic=NULL and test=NULL by default for speed.\n\n\n\n\nBreaking changes:\n\nmodelsummary_wide is no longer available. Use the shape argument of modelsummary instead.\nmodelsummary now uses the easystats packages (performance and parameters) to extract estimates and goodness-of-fit statistics instead of broom. This can be reverted by setting a global option: options(modelsummary_get=\"broom\"). This change aims to (1) increase consistency across models, (2) improve the developers’ ability to push bug fixes upstream when necessary, and (3) improve support for mixed effects, bayesian, and GAM models. The two main drawbacks are: (a) The set of printed statistics may be slightly different from previous versions of modelsummary (b) The group identifiers used in the shape formula will also be different for certain models (e.g., in nnet::multinom, y.level becomes response).\n\nNew features:\n\nThe shape argument accepts a formula and can reshape information in myriad ways. Deprecates the group argument. Examples:\n\n~ statistic: statistics are shown horizontally in distinct columns.\nmodel ~ term: models in rows and terms in columns.\nterm + y.level + statistic ~ model: grouped coefficients for multivariate outcome in nnet::multinom\ny.level ~ model: partial match is the same as the previous formula\n\nFormat distinct statistics differently by passing a named list to fmt:\n\nmodelsummary(mod, fmt = list(estimate = 2, std.error = 1, rmse = 4))\n\nUse glue to apply functions to numeric values by setting fmt = NULL. Example:\n\nmodelsummary(model, fmt = NULL, estimate = \"{log(estimate)}\")\n\nUpdate for breaking changes after fixest 0.10.4\n\nBug fixes:\n\ngroup_map rename issue\nResidual standard error mistakenly labelled “RMSE” in lm models.\ndatasummary_skim output to jpg should now works\nescape fixes\n\n\n\n\n\nNew exponentiate argument for modelsummary() and modelplot()\ngof_map accepts a vector such as c(\"rmse\", \"nobs\", \"r.squared\")\nDrop rlang dependency\nBug fixes\n\n\n\n\ndatasummary_balance:\n\nAccepts ~ 1 as a formula to summarize all data.\n\nMisc:\n\ndocumentation improvements\nRMSE included by default in models of class lm\n\n\n\n\nmodelsummary:\n\nvcov strings like HC1 and Robust are now case-insensitive\ngof_map now accepts a data.frame or tibble with a fmt list-column which includes functions (see Examples in docs)\nR2 is no longer computed by default for bayesian and mixed effects models. An informative one-time warning is printed about the metrics argument.\n\ndatasummary_skim:\n\nHistograms now work in Jupyter\nBugfix: harmless error message is no longer printed\n\nkableExtra factory:\n\nThe col.names argument can now be passed to kableExtra::kbl through the … ellipsis.\n\nMisc:\n\nMany small improvements to the vignettes and docs\noutput = \"github_document\" is now supported\n\n\n\n\n\nBug fix: siunitx and rounding NA\n\n\n\n\nmodelsummary:\n\nF statistic takes into account vcov argument\nSupport group = group ~ model + term\n\ndatasummary_balance:\n\nWeighted means and standard deviations are now supported. Counts and percentages are not, but raise a warning.\n\nMisc:\n\nBugfix: rounding in LaTeX w/ siunitx and NaN entries.\noutput=‘jupyter’ no longer prints an extraneous TRUE to the notebook\n\n\n\n\nmodelsummary:\n\nImproved vcov argument handling for fixest models (#357 by @grantmcdermott)\nFix display of fixest::i() variables and interactions (#361 by @grantmcdermott)\nConsistent display of clustered SEs (#356, #363 and #366 by @grantmcdermott)\n\ndatasummary_correlation:\n\nadd_rows and add_columns arguments are now available here.\n\nMisc:\n\nGlobal options for output factories are renamed: modelsummary_factory_default, modelsummary_factory_html, etc.\nHot fix for change in R-devel behavior or intersect\n\nBug fixes:\n\ndatasummary_balance: escape variable names when escape=TRUE\nBlogdown LaTeX dependency bug when output is HTML\n\n\n\n\nBreaking change:\n\nSupport for dcolumn for dot-aligned columns is deprecated. Use “d” in the align argument instead.\n\nOther changes:\n\nLaTeX output: Numeric entries are wrapped in the \\num{} function from the siunitx package by default. This produces much nicer formatting. This can be disabled with a global option. See ?modelsummary\nThe align argument accepts a “d” column for dot-alignment using the siunitx LaTeX package: align=\"ldd\".\nHTML tables display proper minus signs.\nNew escape argument in most table-building functions.\nLaTeX output accepts the threeparttable=TRUE argument through ...\nNo more dependency on tidyr\n\nmodelsummary:\n\ngroup: The order of terms in the formula determines the order of rows/columns\n\nmodelsummary_wide:\n\nNote: This function will eventually be deprecated\nBugfix with statistic=NULL.\n\nmodelplot:\n\nPreserves order of models in the user-supplied list\n\ndatasummary_crosstab:\n\nstatistic=NULL produces a very basic crosstab\n\ndatasummary_crosstab:\n\nDefault alignment “lrrrrr” consistent with other datasummary_* functions\n\n\n\n\nmodelsummary:\n\nDisable stars footnote with options(\"modelsummary_stars_note\" = FALSE)\nlongtable=TRUE works for LaTeX output\nInteractions with “:” are no longer converted to “x” when coef_map or coef_rename are used.\ngroup = model ~ term + group is now supported.\n\ndatasummary_skim:\n\ndatasummary_skim(\"categorical\") keeps NA by default. Users can convert variables to factors before calling datasummary_skim to exclude NA.\n\nOther:\n\nImproved warnings for bad calls: modelsummary(model1, model2)\ngt titles use the new caption argument in the gt 0.3.0 function\nBug fix: Overaggressive tests for glue strings prevented functions inside {}\n\n\n\n\nBreaking change:\n\nThe default significance markers stars=TRUE have been updated to be consistent with the default output from base R (e.g., in summary.lm). The new significance thresholds are: “+” p &lt; 0.1, “” p &lt; 0.05, ”” p &lt; 0.01, ”” p &lt; 0.001\n\ndatasummary_crosstab:\n\nNew function to produce cross-tabulations\n\ndatasummary:\n\nN is smart enough to return either the number of elements in a subset or the number of non-missing observations in a variable\n\ndatasummary_balance:\n\nKeeps NAs in factor variables by default. Users can convert their variables with the factor() function to omit NAs automatically.\n\nmodelsummary:\n\nthemes can be set using global options (experimental)\nnew vcov options: “bootstrap”, “HAC”, “NeweyWest”, “Andrews”, “panel-corrected”, “weave”, “outer-product”\nA valid get_gof (glance) is now optional.\n… is pushed through to sandwich, which allows things like: modelsummary(model, vcov = \"bootstrap\", R = 1000, cluster = \"firm\")\n\nOther:\n\nJupyter notebook support via output=\"jupyter\"\nBug fixes\n\n\n\n\nmodelsummary:\n\nnew arguments for modelsummary: group and group_map for grouped parameters (e.g., outcome levels in multinomial logit or components of gamlss model).\ndvnames() makes it easy to get dependent variable column titles (thanks to @NickCH-K)\noutput=\"modelsummary_list\" to save a lightweight list-based representation of the table which can be saved and fed to modelsummary once more to get a full table.\nvcov adds a row to note the type of standard errors.\nmodelsummary accepts a single model with multiple vcovs.\nget_gof forwards … to model_performance\ncoef_map accepts unnamed vectors for easy subsetting\nfixest::fixest_multi support\noptions(modelsummary_get) to set the order of extraction functions to use under the hood (broom vs. easystats vs. all)\nmetrics argument of performance::model_performance is available via modelsummary’s … ellipsis to limit the GOF statistics in Bayesian models.\nusers can omit the stars legend note by using glue strings: estimate=\"{estimate}{stars}\"\noutput=“html” can use gt by setting options(modelsummary_factory_html=\"gt\")\n\ndatasummary_correlation:\n\npasses ... forward\nnew function: datasummary_correlation_format\ndatasummary_correlation’s method argument accepts functions and “pearspear” (thanks to @joachim-gassen)\n\ndatasummary:\n\ndatasummary functions and rounding accept …, big.mark, etc.\n\ndatasummary_skim:\n\nnow works with haven_labeled numeric\nfaster tables with bayesian models.\n\nBug fixes and lints\n\n\n\n\nnew output format: latex_tabular\ntidy_custom allows partial term matches\nmodelsummary(coef_rename) accepts functions\nnew function coef_rename for use in modelsummary(coef_rename=coef_rename)\nmodelplot accepts add_rows to add reference categories\ninformative error message when estimate or statistic is not available\nbug fixes\n\n\n\n\n\nstatistic_override becomes vcov\nvcov accepts shortcuts: “robust”, “stata”, “HC0”, etc.\nvcov accepts formulas for clustered SEs: ~group\nmodelsummary_wide has a new “stacking” argument\nhtml horizontal rule to separate estimates form gof\ngof_map accepts list of lists. only needs 3 columns.\nsupport officedown Rmd\nestimate accepts a vector for per model estimates\noptions(modelsummary_default) can be markdown, html, latex\nbug: passing arguments through …\nbug: stars and rounding\n\n\n\n\n\nglue format for estimate and statistic\neasystats support for model info extraction\ndeprecate statistic_vertical\ndeprecate extract_models. Use modelsummary(output=“dataframe”) instead.\nmodelplot pushes … through to modelsummary(output=“dataframe”)\ndatasummary_skim(type=“dataset”)\ngof_map omits by default\ndatasummary_balance uses row percentages\nstatistic_override does not require a list\nstatistic_override accepts a single model\nN function for well formatted N in datasummary\nBug fixes\n\n\n\n\n\nnew function: modelsummary_wide\ncoef_omit and gof_omit use grepl(perl=TRUE)\nfmt accepts integer, string or function and respects options(OutDec=“,”)\nalign argument for modelsummary\nalign is more liberal to accept dcolumn alignment\nglance_custom methods for lfe and fixest\nbug fixes\n\n\n\n\n\nnew argument: coef_rename\nnew function: datasummary_df\npreserve term order in modelsummary\nrefactor datasummary_balance\ndatasummary_skim uses svg histograms instead of unicode\nremoved 5 dependencies\npass … to kableExtra::kbl for more customization\ntest improvements\ninternal code style\nbug fixes\n\n\n\n\n\nbug fixes\n\n\n\n\n\ndefault HTML output factory is now kableExtra\ninteraction “:” gsubbed by “0d7”\ndependencies: removed 1 depends, 3 imports, and 3 suggests\nword_document knitr works out-of-the-box\nbug fixes\n\n\n\n\n\nglance_custom.fixest ships with modelsummary\n\n\n\n\n\ndatasummary\ndatasummary_skim\ndatasummary_balance\ndatasummary_correlation\nmodelplot\nallow duplicate model names\nbug: can’t use coef_map with multiple statistics (thanks @sbw78)\nbug: wrong number of stars w/ statistic=‘p.value’ (thanks @torfason)\noutput=‘data.frame’. extract is no longer documented.\n\n\n\n\n\nadd_rows now accepts a data.frame with “position” and “section” columns\nadd_rows_location is deprecated\nbug in sanity_output prevented overwriting files\n\n\n\n\n\nhuxtable support\nflextable support\nestimate argument\nfixest tidiers\nwebsite and vignette improvements\ngof_map additions\nglance_custom\ntidy_custom\n\n\n\n\n\nOut-of-the-box Rmarkdown compilation to HTML, PDF, RTF\nkableExtra output format for LaTeX and Markdown\nSupport for threeparttable, colors, and many other LaTeX options\nDeprecated arguments: filename, subtitle\nDeprecated functions: clean_latex, knit_latex\npkgdown website and doc improvements\nmitools tidiers\nNew tests\n\n\n\n\n\nConvenience function to render markdown in row/column labels\nbug: breakage when all GOF were omitted\nClean up manual with @keywords internal\nbug: tidyr import\n\n\n\n\n\ngt is now available on CRAN\nnew latex_env argument for knit_latex and clean_latex\nbug when all gof omitted\nbug in statistic_override with functions\nbug caused by upstream changes in tab_style\nbug caused by upstream changes in filename=‘rtf’\nAllow multiple rows of uncertainty estimates per coefficient\nPreserve add_rows order\nDisplay uncertainty estimates next to the coefficient with statistic_vertical = FALSE\nBetter clean_latex function\nCan display R2 and confidence intervals for mice-imputed lm-models\nInternal functions have @keywords internal to avoid inclusion in docs\nStatistic override accepts pre-formatted character vectors\n\n\n\n\n\nInitial release (gt still needs to be installed from github)",
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-    "text": "Standardization\nIn some cases, it is useful to standardize coefficients before reporting them. modelsummary extracts coefficients from model objects using the parameters package, and that package offers several options for standardization: https://easystats.github.io/parameters/reference/model_parameters.default.html\nWe can pass the standardize argument directly to modelsummary or modelplot, and that argument will be forwarded to parameters. For example to refit the model on standardized data and plot the results, we can do:\n\nmod &lt;- lm(mpg ~ hp + am, data = mtcars)\n\nmodelplot(mod, standardize = \"refit\")\n\n\n\n\n\n\n\nCompare to the unstandardized plot:\n\nmodelplot(mod)",
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+    "text": "New features:\n\nmodelsummary() gets a gof_function argument which accepts functions to extract custom information from models.\nflextable: Support spanning column headers\n\ndatasummary_balance():\n\nformula can now include variables on the left-hand side to indicate the subset of columns to summarize: datasummary_balance(mpg + hp ~ am, data = mtcars) Thanks to @etiennebacher for feature request #751.\n\nMinor:\n\nUnnecessary text printed to screen on some F stat computations is now suppressed.\n\nBugs:\n\nescape argument not respected in datasummary_df(). Thanks to @adamaltmejd for report #740\ndatasummary_correlation() supports data.table. Thanks to volatilehead on Twitter for report #737.\nAccepts named estimate argument when using shape and statistics are horizontal. Thanks to @iago-pssjd for report #745.\nLabelled values but no label for variable broke datasummary(). Thanks to @marklhc for report #752.\ncoef_map does not work when there is a group. Thanks to @mccarthy-m-g for report #757.\nkableExtra: fix spanning column headers when using the shape argument.\nMultiple footnotes and line breaks in footnotes are now allowed in tinytable output. Thanks to",
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+    "text": "MAJOR BREAKING CHANGE: The default output format is now tinytable instead of kableExtra. Learn more about tinytable here:\nhttps://vincentarelbundock.github.io/tinytable/\nTo revert to the previous behavior persistently, users can call:\nlibrary(modelsummary) config_modelsummary(factory_default = “kableExtra”)\nOther breaking changes:\n\nThe statistic_override argument was replaced by vcov over 1 year ago, with appropriate deprecation warnings. It is now fully removed.\nThe group argument was replaced by shape several releases ago. It is now fully removed.\ndatasummary_skim()\n\nhistograms are only available with the tinytable backend. This allows a lot of code simplification and more customization.\nThe order of arguments type and output is switched for consistency with other functions.\nhistogram argument is deprecated.\n\n\nNew features:\n\ndatasummary_skim():\n\nNew type=\"all\" by default to display both numeric and categorical variables in a single table with distinct panels. This feature is only available with the tinytable backend (default).\nby argument allows summarizing numeric variables by group.\nfun_numeric argument accepts a list of functions to control the summary columns.\n\nmodelsummary():\n\nstatistic and estimate can be specified as named vectors to control the names of statistics when displayed in different columns using the shape argument. (Thanks to @mps9506 for bug report #722)\nmodelsummary(panels, shape = \"cbind\") automatically adds column-spanning labels when panels is a named nested list of models.\n\nconfig_modelsummary() gets a startup_message argument to silence the startup message persistently.\nImproved documentation and vignettes, providing clearer instructions and examples.\nUpdated tests and snapshots to ensure reliability and consistency across changes.\n\nBug fixes:\n\nFixed Issue #399: datasummary_balance() siunitx formatting.\nFixed Issue #782: Useless warning in some modelplot() calls. Thanks to @iago-pssjd for the report and @florence-laflamme for the fix.\nAddressed various bugs and made optimizations for better performance and user experience.",
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-    "text": "Quarto\nQuarto is an open source publishing system built on top of Pandoc. It was designed as a “successor” to Rmarkdown, and includes useful features for technical writing, such as built-in support for cross-references. modelsummary works automatically with Quarto. This is a minimal document with cross-references which should render automatically to PDF, HTML, and more:\n---\nformat: pdf\ntitle: Example\n---\n\n@tbl-mtcars shows that cars with high horse power get low miles per gallon.\n\n::: {#tbl-mtcars .cell tbl-cap='Horse Powers vs. Miles per Gallon'}\n\n```{.r .cell-code}\nlibrary(modelsummary)\nmod &lt;- lm(mpg ~ hp, mtcars)\nmodelsummary(mod)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n&lt;!DOCTYPE html&gt; \n&lt;html lang=\"en\"&gt;\n  &lt;head&gt;\n    &lt;meta charset=\"UTF-8\"&gt;\n    &lt;meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\"&gt;\n    &lt;title&gt;tinytable_j6sclijzbhq96g2yotqq&lt;/title&gt;\n    &lt;style&gt;\n.table td.tinytable_css_83z0321nhkxyd5fuo2o1, .table th.tinytable_css_83z0321nhkxyd5fuo2o1 {    border-bottom: solid 0.1em #d3d8dc; }\n.table td.tinytable_css_m8i9dirhztql7ryf1b4q, .table th.tinytable_css_m8i9dirhztql7ryf1b4q {    text-align: left; }\n.table 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&lt;tr&gt;\n                &lt;th scope=\"col\"&gt; &lt;/th&gt;\n                &lt;th scope=\"col\"&gt;(1)&lt;/th&gt;\n              &lt;/tr&gt;\n        &lt;/thead&gt;\n        \n        &lt;tbody&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;(Intercept)&lt;/td&gt;\n                  &lt;td&gt;30.099 &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;           &lt;/td&gt;\n                  &lt;td&gt;(1.634)&lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;hp         &lt;/td&gt;\n                  &lt;td&gt;-0.068 &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;           &lt;/td&gt;\n                  &lt;td&gt;(0.010)&lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;Num.Obs.   &lt;/td&gt;\n                  &lt;td&gt;32     &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;R2         &lt;/td&gt;\n                  &lt;td&gt;0.602  &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;R2 Adj.    &lt;/td&gt;\n                  &lt;td&gt;0.589  &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;AIC        &lt;/td&gt;\n                  &lt;td&gt;181.2  &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;BIC        &lt;/td&gt;\n                  &lt;td&gt;185.6  &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;Log.Lik.   &lt;/td&gt;\n                  &lt;td&gt;-87.619&lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;F          &lt;/td&gt;\n                  &lt;td&gt;45.460 &lt;/td&gt;\n                &lt;/tr&gt;\n                &lt;tr&gt;\n                  &lt;td&gt;RMSE       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styleCell_tinytable_jigzgtugv8ucjpqxbahd(4, 1, 'tinytable_css_qodvhqm3z9psme2ma93q') })\n    &lt;/script&gt;\n\n  &lt;/body&gt;\n\n&lt;/html&gt;\n```\n\n:::\n:::",
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+    "text": "tinytable supports histograms in datasummary_skim()\nconfig_modelsummary() supports tinytable factory.",
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-    "text": "Emacs Org-Mode\nYou can use modelsummary to insert tables into Emacs Org-Mode documents, which can be exported to a variety of formats, including HTML and PDF (via LaTeX). As with anything Emacs-related, there are many ways to achieve the outcomes you want. Here is one example of an Org-Mode document which can automatically export tables to HTML and PDF without manual tweaks:\n#+PROPERTY: header-args:R :var orgbackend=(prin1-to-string org-export-current-backend)\n#+MACRO: Rtable (eval (concat \"#+header: :results output \" (prin1-to-string org-export-current-backend)))\n\n{{{Rtable}}}\n#+BEGIN_SRC R :exports both\nlibrary(modelsummary)\noptions(modelsummary_factory_default = orgbackend)\n\nmod = lm(hp ~ mpg, data = mtcars)\n\nmodelsummary(mod)\n#+END_SRC\nThe first line tells Org-mode to assign a variable called orgbackend. This variable will be accessible by the R session, and will be equal to “html” or “latex”, depending on the export format.\nThe second line creates an Org macro which we will use to automatically add useful information to the header of source blocks. For instance, when we export to HTML, the macro will expand to :results output html. This tells Org-Mode to insert the last printed output from the R session, and to treat it as raw HTML.\nThe {{Rtable}} call expands the macro to add information to the header of the block that follows.\n#+BEGIN_SRC R :exports both says that we want to print both the original code and the output (:exports results would omit the code, for example).\nFinally, options(modelsummary_factory_default=orgbackend uses the variable we defined to set the default output format. That way, we don’t have to use the output argument every time.\nOne potentially issue to keep in mind is that the code above extracts the printout from the R console. However, when we customize tables with kableExtra or gt functions, those functions do not always return printed raw HTML or LaTeX code. Sometimes, it can be necessary to add a call to cat at the end of a table customization pipeline. For example:\n{{{Rtable}}}\n#+BEGIN_SRC R :exports both\nlibrary(modelsummary)\nlibrary(kableExtra)\n\nmod = lm(hp ~ mpg, data = mtcars)\n\nmodelsummary(mod, output = orgbackend) %&gt;%\n  row_spec(1, background = \"pink\") %&gt;%\n  cat()\n#+END_SRC",
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+    "text": "Support the tinytable package as an output format (“factory”): https://vincentarelbundock.github.io/tinytable/\nQuarto: md output format is recognized.\noptions(modelsummary_factory_default) is respected, even in qmd-&gt;md documents.\n\nBugs:\n\nSome omitted coefficients with I() operator in formulas. Issue #693.",
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-    "section": "Collapse control variables into an indicator",
-    "text": "Collapse control variables into an indicator\nIn some cases, analysts want to display a single checkmark in their table if a full set of control variables is available. This can easily be achieved with the glance_custom mechanism described in detail in the extension vignette. Here is a simple example:\n\nlibrary(modelsummary)\n\nmod &lt;- list(\n  glm(am ~ hp + factor(cyl), data = mtcars, family = binomial),\n  lm(mpg ~ hp + factor(cyl) + gear + qsec, data = mtcars)\n)\n\nglance_custom.lm &lt;- function(x, ...) {\n  controls &lt;- c(\"gear\", \"qsec\")\n  if (all(controls %in% names(coef(x)))) {\n    out &lt;- data.frame(Controls = \"✓\")\n  } else {\n    out &lt;- data.frame(Controls = \"✗\")\n  }\n  return(out)\n}\n\nglance_custom.glm &lt;- glance_custom.lm\n\nmodelsummary(mod, \"html\", coef_omit = \"qsec|gear\")\n\n \n\n  \n    \n\ntinytable_ms8a4iyhejss4c9y1vtu\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept) \n                  −2.029 \n                  33.178  \n                \n\n            \n                  (1.604)\n                  (13.713)\n                \n\nhp          \n                  0.038  \n                  −0.057  \n                \n\n            \n                  (0.019)\n                  (0.018) \n                \n\nfactor(cyl)6\n                  −2.874 \n                  −4.818  \n                \n\n            \n                  (1.360)\n                  (1.805) \n                \n\nfactor(cyl)8\n                  −8.644 \n                  −4.037  \n                \n\n            \n                  (3.423)\n                  (3.585) \n                \n\nNum.Obs.    \n                  32     \n                  32      \n                \n\nR2          \n                         \n                  0.812   \n                \n\nR2 Adj.     \n                         \n                  0.776   \n                \n\nAIC         \n                  35.2   \n                  165.2   \n                \n\nBIC         \n                  41.1   \n                  175.5   \n                \n\nLog.Lik.    \n                  −13.602\n                  −75.598 \n                \n\nF           \n                  2.514  \n                  22.526  \n                \n\nRMSE        \n                  0.38   \n                  2.57    \n                \n\nControls    \n                  ✗      \n                  ✓       \n                \n\n\n\n\n    \n\n\nReturn to nomal:\n\nrm(list = c(\"glance_custom.glm\", \"glance_custom.lm\"))",
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+    "text": "Misc:\n\nDuplicate values in shape groups are removed automatically for cleaner labels.\n“Title” line no longer indented in markdown tables. Thanks to Ryan Briggs for report #671.\n\nBugs:\n\nSmall p values were not displayed properly in HTML output using kableExtra. Issue #669.",
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-    "section": "Subgroup estimation with nest_by\n",
-    "text": "Subgroup estimation with nest_by\n\nSometimes, it is useful to estimate multiple regression models on subsets of the data. To do this efficiently, we can use the nest_by function from the dplyr package. Then, estimate the models with lm, extract them and name them with pull, and finally summarize them with modelsummary:\n\nlibrary(tidyverse)\n\nmtcars %&gt;%\n    nest_by(cyl) %&gt;%\n    mutate(models = list(lm(mpg ~ hp, data))) %&gt;%\n    pull(models, name = cyl) %&gt;%\n    modelsummary\n\n \n\n  \n    \n\ntinytable_qws112k7rokbcv6rxh0v\n\n\n      \n\n \n                4\n                6\n                8\n              \n\n\n(Intercept)\n                  35.983 \n                  20.674 \n                  18.080 \n                \n\n           \n                  (5.201)\n                  (3.304)\n                  (2.988)\n                \n\nhp         \n                  -0.113 \n                  -0.008 \n                  -0.014 \n                \n\n           \n                  (0.061)\n                  (0.027)\n                  (0.014)\n                \n\nNum.Obs.   \n                  11     \n                  7      \n                  14     \n                \n\nR2         \n                  0.274  \n                  0.016  \n                  0.080  \n                \n\nR2 Adj.    \n                  0.193  \n                  -0.181 \n                  0.004  \n                \n\nAIC        \n                  65.8   \n                  29.9   \n                  69.8   \n                \n\nBIC        \n                  67.0   \n                  29.7   \n                  71.8   \n                \n\nLog.Lik.   \n                  -29.891\n                  -11.954\n                  -31.920\n                \n\nRMSE       \n                  3.66   \n                  1.33   \n                  2.37",
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+    "text": "New:\n\nMinimal support for Typst output, with auto-detection in Quarto documents.\nstrip argument in dvnames.\ns.value statistic is now available whenever p.value is available. See Greenland (2019).\ndatasummary_skim() now includes histograms in gt tables.\n\nBugs:\n\nGOF term names get escaped in LaTeX. Thanks to @shreyasgm for reviving Issue #546.\nConflict with furrr generated errors for some models. Thanks to @sammo3182 for Issue #647.",
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-    "text": "Bootstrap\nUsers often want to use estimates or standard errors that have been obtained using a custom strategy. To achieve this in an automated and replicable way, it can be useful to use the tidy_custom strategy described above in the “Cutomizing Existing Models” section.\nFor example, we can use the modelr package to draw 500 resamples of a dataset, and compute bootstrap standard errors by taking the standard deviation of estimates computed in all of those resampled datasets. To do this, we defined tidy_custom.lm function that will automatically bootstrap any lm model supplied to modelsummary, and replace the values in the table automatically.\nNote that the tidy_custom_lm returns a data.frame with 3 columns: term, estimate, and std.error:\n\nlibrary(\"broom\")\nlibrary(\"tidyverse\")\nlibrary(\"modelr\")\n\ntidy_custom.lm &lt;- function(x, ...) {\n  # extract data from the model\n  model.frame(x) %&gt;%\n    # draw 500 bootstrap resamples\n    modelr::bootstrap(n = 500) %&gt;%\n    # estimate the model 500 times\n    mutate(results = map(strap, ~ update(x, data = .))) %&gt;%\n    # extract results using `broom::tidy`\n    mutate(results = map(results, tidy)) %&gt;%\n    # unnest and summarize\n    unnest(results) %&gt;%\n    group_by(term) %&gt;%\n    summarize(std.error = sd(estimate),\n              estimate = mean(estimate))\n}\n\nmod = list(\n  lm(hp ~ mpg, mtcars) ,\n  lm(hp ~ mpg + drat, mtcars))\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_kb3c0yseq28pzetod0b9\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  326.027 \n                  283.140 \n                \n\n           \n                  (31.202)\n                  (42.615)\n                \n\nmpg        \n                  -8.946  \n                  -10.079 \n                \n\n           \n                  (1.376) \n                  (2.341) \n                \n\ndrat       \n                          \n                  18.241  \n                \n\n           \n                          \n                  (20.931)\n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nR2         \n                  0.602   \n                  0.614   \n                \n\nR2 Adj.    \n                  0.589   \n                  0.588   \n                \n\nAIC        \n                  336.9   \n                  337.9   \n                \n\nBIC        \n                  341.3   \n                  343.7   \n                \n\nLog.Lik.   \n                  -165.428\n                  -164.940\n                \n\nF          \n                  45.460  \n                  23.100  \n                \n\nRMSE       \n                  42.55   \n                  41.91",
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+    "text": "New:\n\nfmt_sci() can now be used in the fmt argument for rounding with scientific notation.\n\nBugs:\n\nGroup separators respect add_rows with shape=\"rbind\". Thanks to @lrose1 for Report #626.\nBad column with horizontal models in shape and grouped estimates. Thanks to @iago-pssjd for Report #631.\ncoef_rename=TRUE bug with grouped estimates. Thanks to @iago-pssjd for Report #631.\nUpstream issue #881 in parameters meant that vcov was no longer used for confidence intervals.",
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-    "section": "\nfixest: Fixed effects and instrumental variable regression",
-    "text": "fixest: Fixed effects and instrumental variable regression\nOne common use-case for glance_custom is to include additional goodness-of-fit statistics. For example, in an instrumental variable estimation computed by the fixest package, we may want to include an IV-Wald statistic for the first-stage regression of each endogenous regressor:\n\nlibrary(fixest)\nlibrary(tidyverse)\n\n# create a toy dataset\nbase &lt;- iris\nnames(base) &lt;- c(\"y\", \"x1\", \"x_endo_1\", \"x_inst_1\", \"fe\")\nbase$x_inst_2 &lt;- 0.2 * base$y + 0.2 * base$x_endo_1 + rnorm(150, sd = 0.5)\nbase$x_endo_2 &lt;- 0.2 * base$y - 0.2 * base$x_inst_1 + rnorm(150, sd = 0.5)\n\n# estimate an instrumental variable model\nmod &lt;- feols(y ~ x1 | fe | x_endo_1 + x_endo_2 ~ x_inst_1 + x_inst_2, base)\n\n# custom extractor function returns a one-row data.frame (or tibble)\nglance_custom.fixest &lt;- function(x) {\n  tibble(\n    \"Wald (x_endo_1)\" = fitstat(x, \"ivwald\")[[1]]$stat,\n    \"Wald (x_endo_2)\" = fitstat(x, \"ivwald\")[[2]]$stat\n  )\n}\n\n# draw table\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_hvo9mq1dvgmmg6tr6xih\n\n\n      \n\n \n                (1)\n              \n\n\nfit_x_endo_1   \n                  0.646           \n                \n\n               \n                  (0.246)         \n                \n\nfit_x_endo_2   \n                  0.444           \n                \n\n               \n                  (0.566)         \n                \n\nx1             \n                  0.461           \n                \n\n               \n                  (0.269)         \n                \n\nNum.Obs.       \n                  150             \n                \n\nR2             \n                  0.813           \n                \n\nR2 Adj.        \n                  0.807           \n                \n\nR2 Within      \n                  0.510           \n                \n\nR2 Within Adj. \n                  0.500           \n                \n\nAIC            \n                  128.5           \n                \n\nBIC            \n                  146.6           \n                \n\nRMSE           \n                  0.36            \n                \n\nStd.Errors     \n                  by: fe          \n                \n\nFE: fe         \n                  X               \n                \n\nWald (x_endo_1)\n                  46.9104378163272\n                \n\nWald (x_endo_2)\n                  4.41706222570233\n                \n\n\n\n\n    \n\n\n\nrm(\"glance_custom.fixest\")",
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+    "text": "Built-in support for markdown tables.\nPackage no longer depends on kableExtra. Recommends an additional install for other formats.\nPersistent configuration of default output format: config_modelsummary(factory_default = \"gt\")\nshape = \"rcollapse\" and shape = \"rbind\"\nglance_custom() can drop GOF by assigning NA: https://stackoverflow.com/questions/75215355/assigning-different-coefficient-names-and-goodness-of-fit-statistics-to-differen\nWhen a statistic is not available, modelsummary prints an empty cell instead of returning an error.\n“\\label{tab:something}” works in title even when escape=TRUE\nMultiple fixest_multi objects supported.\noptions(modelsummary_future = FALSE) disables future parallelism.\n\nBug fixes:\n\nstatistic=NULL is now respected when shape=\"rbind\". Thanks to Panos Mavros for report #620.\nget_estimates() supports vcov string shortcuts and formulas. Thanks to @ethans-carl for report #605.\nQuarto and Rmarkdown documents include situnix in header automatically for decimal alignement with align=\"ddd\"\nescape is now respected by modelsummary with shape=\"rbind\". Thanks to @chickymonkeys for report #622.",
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-    "text": "Multiple imputation\nmodelsummary can pool and display analyses on several datasets imputed using the mice or Amelia packages. This code illustrates how:\n\nlibrary(mice)\n\nWarning in check_dep_version(): ABI version mismatch: \nlme4 was built with Matrix ABI version 2\nCurrent Matrix ABI version is 1\nPlease re-install lme4 from source or restore original 'Matrix' package\n\nlibrary(Amelia)\n\n# Download data from `Rdatasets`\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'\ndat &lt;- read.csv(url)[, c('Clergy', 'Commerce', 'Literacy')]\n\n# Insert missing values\ndat$Clergy[sample(1:nrow(dat), 10)] &lt;- NA\ndat$Commerce[sample(1:nrow(dat), 10)] &lt;- NA\ndat$Literacy[sample(1:nrow(dat), 10)] &lt;- NA\n\n# Impute with `mice` and `Amelia`\ndat_mice &lt;- mice(dat, m = 5, printFlag = FALSE)\ndat_amelia &lt;- amelia(dat, m = 5, p2s = 0)$imputations\n\n# Estimate models\nmod &lt;- list()\nmod[['Listwise deletion']] &lt;- lm(Clergy ~ Literacy + Commerce, dat)\nmod[['Mice']] &lt;- with(dat_mice, lm(Clergy ~ Literacy + Commerce)) \nmod[['Amelia']] &lt;- lapply(dat_amelia, function(x) lm(Clergy ~ Literacy + Commerce, x))\n\n# Pool results\nmod[['Mice']] &lt;- mice::pool(mod[['Mice']])\nmod[['Amelia']] &lt;- mice::pool(mod[['Amelia']])\n\n# Summarize\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_bzjss5rake3s1sfgl1k5\n\n\n      \n\n \n                Listwise deletion\n                Mice\n                Amelia\n              \n\n\n(Intercept)\n                  77.468  \n                  68.041  \n                  79.707  \n                \n\n           \n                  (13.883)\n                  (17.248)\n                  (12.214)\n                \n\nLiteracy   \n                  -0.513  \n                  -0.379  \n                  -0.526  \n                \n\n           \n                  (0.210) \n                  (0.257) \n                  (0.183) \n                \n\nCommerce   \n                  -0.320  \n                  -0.229  \n                  -0.338  \n                \n\n           \n                  (0.154) \n                  (0.186) \n                  (0.134) \n                \n\nNum.Obs.   \n                  61      \n                  86      \n                  86      \n                \n\nNum.Imp.   \n                          \n                  5       \n                  5       \n                \n\nR2         \n                  0.100   \n                  0.054   \n                  0.105   \n                \n\nR2 Adj.    \n                  0.069   \n                          \n                  0.083   \n                \n\nAIC        \n                  563.7   \n                          \n                          \n                \n\nBIC        \n                  572.2   \n                          \n                          \n                \n\nLog.Lik.   \n                  -277.862\n                          \n                          \n                \n\nRMSE       \n                  23.02",
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+    "text": "Breaking change:\n\nThe default column label style in modelsummary() has changed from “Model 1” to “(1)”. The benefits are: labels are no longer in English by default; use less horizontal space; eliminate the “Model” redundancy. Unfortunately, this could break code in some edge cases where users rely on column names to manipulate tables. The old behavior can be restored by calling: options(modelsummary_model_labels=\"model\")\n\nNew features:\n\nshape=\"rbind\" to stack multiple regression tables and create “panels” with labelled groups of models.\nfmt: new helper functions for different formatting styles\n\nfmt = fmt_decimal(2): decimal digits\nfmt = fmt_decimal(digits = 2, pdigits = 4): decimal digits with p value-specific setting\nfmt = fmt_sprintf(\"%.3f\"): sprintf() decimal\nfmt = fmt_sprintf(\"%.3e\"): sprintf() scientific\nfmt = fmt_significant(3): significant digits\nfmt = fmt_statistic(\"estimate\" = 2, \"std.error\" = 3): statistic-specific formatting\nfmt = fmt_term(\"(Intercept)\" = 2, \"hp\" = 3): term-specific formatting\nfmt = fmt_identity(): raw values\n\nNew styles for default column labels in modelsummary, such as Roman Numerals or letters in parentheses.\n\nSet the style with a global option: options(modelsummary_model_labels = \"roman\")\nSupported styles: “model”, “arabic”, “letters”, “roman”, “(arabic)”, “(letters)”, “(roman)””\n\nmodelplot(draw = FALSE) now returns a p.value column. This allows conditional aesthetics (see the modelplot vignette).\nBetter integration with the marginaleffects package.\n\nBugs:\n\nSome fixest models returns useless “group.x” and “group.y” columns. Isse #591. Thanks to Adam Altmejd for the report.",
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-    "text": "Stack Overflow is useful\n\nStandardized coefficients\nRow group labels\nCustomizing Word tables\nHow to add p values to datasummary_correlation",
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+    "text": "Breaking change:\n\nWith the shape and output=\"dataframe\" arguments, there always used to be a group column. Now, this column has the same name as the variable in the shape formula (“response”, “component”, etc.).\n\nNew features:\n\nshape can include multiple groups.\ncoef_rename can be an unnamed vector of length equal to the number of terms in the final table, obtained after coef_map and coef_omit are applied and models are merged.\ncoef_omit accepts numeric indices. Positive values: coefficients to omit. Negative values: coefficients to keep.\ndatasummary_skim: Increased maximum number of variables to 250.\nQuarto notebooks compile to Word and Markdown automatically.\n\nBug fixes:\n\nOrder of notes preserved in some output format (Issue #577)",
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-    "section": "Where can I get help?",
-    "text": "Where can I get help?\nFirst, please read the documentation in ?modelsummary and on the modelsummary website. The website includes dozens of worked examples and a lot of detailed explanation.\nSecond, try to use the [modelsummary] tag on StackOverflow.\nThird, if you think you found a bug or have a feature request, please file it on the Github issue tracker:",
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+    "text": "Breaking change:\n\nRequires siunitx version 3.0.25 LaTeX package.\nThe title argument now respects the escape argument for all kableExtra output formats. This can break tables in which users manually escaped titles.\n\nNew features:\n\n“d” is accepted for decimal-alignment in the align argument for all output formats. modelsummary(mod, align = \"ld\")\nNew update_modelsummary() function makes it easy to install the dev versions of modelsummary and its dependencies (mostly useful for Vincent and people who report bugs).\nRounding: display at least one significant digit by default.\nAutomatic renaming of haven labels in modelsummary(), datasummary(), datasummary_skim()\nAllow output = \"filename.csv\"\nAllow output = \"filename.xlsx\"\nadd_columns argument supported in modelsummary()\ndatasummary_balance supports the stars argument.\nAllow stars and confidence intervals with align = \"d\" column.\n\nBug fixes:\n\nIn some locales, the HTML minus sign created problems in the output. We only use it in “known” locales.\nMany minor bug fixes",
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-    "section": "How can I add or modify statistics in a table?",
-    "text": "How can I add or modify statistics in a table?\nSee the detailed documentation in the “Adding and Customizing Models” section of the modelsummary website.",
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+    "text": "Minor release to fix CRAN failure",
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-    "section": "How does modelsummary extract estimates and goodness-of-fit statistics?",
-    "text": "How does modelsummary extract estimates and goodness-of-fit statistics?\nA modelsummary table is divided in two parts: “Estimates” (top of the table) and “Goodness-of-fit” (bottom of the table). To populate those two parts, modelsummary tries using the broom, parameters and performance packages in sequence.\nEstimates:\n\nTry the broom::tidy function to see if that package supports this model type, or if the user defined a custom tidy function in their global environment. If this fails…\nTry the parameters::model_parameters function to see if the parameters package supports this model type.\n\nGoodness-of-fit:\n\nTry the performance::model_performance function to see if the performance package supports this model type.\nTry the broom::glance function to see if that package supports this model type, or if the user defined a custom glance function in their global environment. If this fails…\n\nYou can change the order in which those steps are executed by setting a global option:\n\n# tidymodels: broom \noptions(modelsummary_get = \"broom\")\n\n# easystats: performance + parameters\noptions(modelsummary_get = \"easystats\")\n\nIf all of this fails, modelsummary will return an error message.\nIf you have problems with a model object, you can often diagnose the problem by running the following commands from a clean R session:\n# see if parameters and performance support your model type\nlibrary(parameters)\nlibrary(performance)\nmodel_parameters(model)\nmodel_performance(model)\n\n# see if broom supports your model type\nlibrary(broom)\ntidy(model)\nglance(model)\n\n# see if broom.mixed supports your model type\nlibrary(broom.mixed)\ntidy(model)\nglance(model)\nIf none of these options work, you can create your own tidy and glance methods, as described in the Adding new models section.\nIf one of the extractor functions does not work well or takes too long to process, you can define a new “custom” model class and choose your own extractors, as described in the Adding new models section.",
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+    "text": "shape argument accepts interactions with the colon “:” character. This combines two columns into one, which can be useful to display terms and group names in a single column.\nParallelization using parallel::mclapply. See ?modelsummary\nmodelsummary no longer computes confidence intervals when not necessary, which can save some time. Also see: conf_level=NULL\nAdded log likelihood to GOF for lm and glm models.\nRemoved extraneous warnings\nBug fixes",
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-    "text": "How can I speed up modelsummary?\nThe modelsummary function, by itself, is not slow: it should only take a couple seconds to produce a table in any output format. However, sometimes it can be computationally expensive (and long) to extract estimates and to compute goodness-of-fit statistics for your model.\nThe main options to speed up modelsummary are:\n\nSet gof_map=NA to avoid computing expensive goodness-of-fit statistics.\nUse the easystats extractor functions and the metrics argument to avoid computing expensive statistics (see below for an example).\nUse parallel computation if you are summarizing multiple models. See the “Parallel computation” section in the ?modelsummary documentation.\n\nTo diagnose the slowdown and find the bottleneck, you can try to benchmark the various extractor functions:\n\nlibrary(tictoc)\n\ndata(trade)\nmod &lt;- lm(mpg ~ hp + drat, mtcars)\n\ntic(\"tidy\")\nx &lt;- broom::tidy(mod)\ntoc()\n\ntidy: 0.003 sec elapsed\n\ntic(\"glance\")\nx &lt;- broom::glance(mod)\ntoc()\n\nglance: 0.003 sec elapsed\n\ntic(\"parameters\")\nx &lt;- parameters::parameters(mod)\ntoc()\n\nparameters: 0.021 sec elapsed\n\ntic(\"performance\")\nx &lt;- performance::performance(mod)\ntoc()\n\nperformance: 0.011 sec elapsed\n\n\nIn my experience, the main bottleneck tends to be computing goodness-of-fit statistics. The performance extractor allows users to specify a metrics argument to select a subset of GOF to include. Using this can speedup things considerably.\nWe call modelsummary with the metrics argument:\n\nmodelsummary(mod, metrics = \"rmse\")\n\n \n\n  \n    \n\ntinytable_ief0ezi5199jwpnqdy2u\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  10.790 \n                \n\n           \n                  (5.078)\n                \n\nhp         \n                  -0.052 \n                \n\n           \n                  (0.009)\n                \n\ndrat       \n                  4.698  \n                \n\n           \n                  (1.192)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.741  \n                \n\nR2 Adj.    \n                  0.723  \n                \n\nAIC        \n                  169.5  \n                \n\nBIC        \n                  175.4  \n                \n\nLog.Lik.   \n                  -80.752\n                \n\nF          \n                  41.522",
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+    "text": "This first major release accompanies the publication of an article in the Journal of Statistical Software:\nArel-Bundock, Vincent (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. doi:10.18637/jss.v103.i01 https://doi.org/10.18637/jss.v103.i01.’\nIf you like modelsummary, please cite the JSS article and tell your friends about it.\nMinor changes:\n\ngof_map=\"all\" includes all available statistics. gof_map=\"none\" excludes all statistics.\nBug fixes",
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-    "text": "Escaped LaTeX characters\nSometimes, users want to include raw LaTeX commands in their tables, such as coefficient names including math mode: Apple $\\times$ Orange. The result of these attempts is often a weird string such as: \\$\\textbackslash{}times\\$ instead of proper LaTeX-rendered characters.\nThe source of the problem is that kableExtra, default table-making package in modelsummary, automatically escapes weird characters to make sure that your tables compile properly in LaTeX. To avoid this, we need to pass the escape=FALSE to modelsummary:\n\nmodelsummary(mod, escape = FALSE)",
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+    "text": "Better printout for term names in mixed-effects models\n{brms} and {stanreg} models now extracted with diagnostic=NULL and test=NULL by default for speed.",
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-    "text": "Bayesian models\nMany bayesian models are supported out-of-the-box, including those produced by the rstanarm and brms packages. The statistics available for bayesian models are slightly different than those available for most frequentist models. Users can call get_estimates to see what is available:\n\nlibrary(rstanarm)\n\nThis is rstanarm version 2.32.1\n\n\n- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!\n\n\n- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.\n\n\n- For execution on a local, multicore CPU with excess RAM we recommend calling\n\n\n  options(mc.cores = parallel::detectCores())\n\n\n\nAttaching package: 'rstanarm'\n\n\nThe following object is masked from 'package:fixest':\n\n    se\n\nmod &lt;- stan_glm(am ~ hp + drat, data = mtcars)\n\n\nget_estimates(mod)\n\n         term      estimate         mad conf.level     conf.low    conf.high prior.distribution prior.location prior.scale group std.error statistic p.value\n1 (Intercept) -2.2318051666 0.571312754       0.95 -3.398159779 -1.036557136             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006961822 0.001081759       0.95 -0.001400122  0.002867875             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.7056622409 0.131337768       0.95  0.425382271  0.970069039             normal        0.00000  2.33313429              NA        NA      NA\n\n\nThis shows that there is no std.error column, but that there is a mad statistic (mean absolute deviation). So we can do:\n\nmodelsummary(mod, statistic = \"mad\")\n\nWarning: \n`modelsummary` uses the `performance` package to extract goodness-of-fit\nstatistics from models of this class. You can specify the statistics you wish\nto compute by supplying a `metrics` argument to `modelsummary`, which will then\npush it forward to `performance`. Acceptable values are: \"all\", \"common\",\n\"none\", or a character vector of metrics names. For example: `modelsummary(mod,\nmetrics = c(\"RMSE\", \"R2\")` Note that some metrics are computationally\nexpensive. See `?performance::performance` for details.\n This warning appears once per session.\n\n\n \n\n  \n    \n\ntinytable_n2vjo8urtofxbxq65slf\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  -2.232 \n                \n\n           \n                  (0.571)\n                \n\nhp         \n                  0.001  \n                \n\n           \n                  (0.001)\n                \n\ndrat       \n                  0.706  \n                \n\n           \n                  (0.131)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.499  \n                \n\nR2 Adj.    \n                  0.433  \n                \n\nLog.Lik.   \n                  -12.036\n                \n\nELPD       \n                  -15.1  \n                \n\nELPD s.e.  \n                  3.1    \n                \n\nLOOIC      \n                  30.1   \n                \n\nLOOIC s.e. \n                  6.2    \n                \n\nWAIC       \n                  29.8   \n                \n\nRMSE       \n                  0.34   \n                \n\n\n\n\n    \n\n\nAs noted in the modelsummary() documentation, model results are extracted using the parameters package. Users can pass additional arguments to modelsummary(), which will then push forward those arguments to the parameters::parameters function to change the results. For example, the parameters documentation for bayesian models shows that there is a centrality argument, which allows users to report the mean and standard deviation of the posterior distribution, instead of the median and MAD:\n\nget_estimates(mod, centrality = \"mean\")\n\n         term      estimate     std.dev conf.level     conf.low    conf.high prior.distribution prior.location prior.scale group std.error statistic p.value\n1 (Intercept) -2.2253997084 0.593524210       0.95 -3.398159779 -1.036557136             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006951465 0.001091155       0.95 -0.001400122  0.002867875             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.7034512498 0.138457886       0.95  0.425382271  0.970069039             normal        0.00000  2.33313429              NA        NA      NA\n\nmodelsummary(mod, statistic = \"std.dev\", centrality = \"mean\")\n\n \n\n  \n    \n\ntinytable_yqy9c0mns7hanl7frc7u\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  -2.225 \n                \n\n           \n                  (0.594)\n                \n\nhp         \n                  0.001  \n                \n\n           \n                  (0.001)\n                \n\ndrat       \n                  0.703  \n                \n\n           \n                  (0.138)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.499  \n                \n\nR2 Adj.    \n                  0.433  \n                \n\nLog.Lik.   \n                  -12.036\n                \n\nELPD       \n                  -15.1  \n                \n\nELPD s.e.  \n                  3.1    \n                \n\nLOOIC      \n                  30.1   \n                \n\nLOOIC s.e. \n                  6.2    \n                \n\nWAIC       \n                  29.8   \n                \n\nRMSE       \n                  0.34   \n                \n\n\n\n\n    \n\n\nWe can also get additional test statistics using the test argument:\n\nget_estimates(mod, test = c(\"pd\", \"rope\"))\n\n         term      estimate         mad conf.level     conf.low    conf.high      pd rope.percentage prior.distribution prior.location prior.scale group std.error statistic p.value\n1 (Intercept) -2.2318051666 0.571312754       0.95 -3.398159779 -1.036557136 0.99925               0             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006961822 0.001081759       0.95 -0.001400122  0.002867875 0.74000               1             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.7056622409 0.131337768       0.95  0.425382271  0.970069039 1.00000               0             normal        0.00000  2.33313429              NA        NA      NA",
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+    "text": "Breaking changes:\n\nmodelsummary_wide is no longer available. Use the shape argument of modelsummary instead.\nmodelsummary now uses the easystats packages (performance and parameters) to extract estimates and goodness-of-fit statistics instead of broom. This can be reverted by setting a global option: options(modelsummary_get=\"broom\"). This change aims to (1) increase consistency across models, (2) improve the developers’ ability to push bug fixes upstream when necessary, and (3) improve support for mixed effects, bayesian, and GAM models. The two main drawbacks are: (a) The set of printed statistics may be slightly different from previous versions of modelsummary (b) The group identifiers used in the shape formula will also be different for certain models (e.g., in nnet::multinom, y.level becomes response).\n\nNew features:\n\nThe shape argument accepts a formula and can reshape information in myriad ways. Deprecates the group argument. Examples:\n\n~ statistic: statistics are shown horizontally in distinct columns.\nmodel ~ term: models in rows and terms in columns.\nterm + y.level + statistic ~ model: grouped coefficients for multivariate outcome in nnet::multinom\ny.level ~ model: partial match is the same as the previous formula\n\nFormat distinct statistics differently by passing a named list to fmt:\n\nmodelsummary(mod, fmt = list(estimate = 2, std.error = 1, rmse = 4))\n\nUse glue to apply functions to numeric values by setting fmt = NULL. Example:\n\nmodelsummary(model, fmt = NULL, estimate = \"{log(estimate)}\")\n\nUpdate for breaking changes after fixest 0.10.4\n\nBug fixes:\n\ngroup_map rename issue\nResidual standard error mistakenly labelled “RMSE” in lm models.\ndatasummary_skim output to jpg should now works\nescape fixes",
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-    "text": "datasummary is a function from the modelsummary package. It allows us to create data summaries, frequency tables, crosstabs, correlation tables, balance tables (aka “Table 1”), and more. It has many benefits:\nThis tutorial will show how to draw tables like these (and more!):\ndatasummary is built around the fantastic tables package for R. It is a thin “wrapper” which adds convenience functions and arguments; a user-interface consistent with modelsummary; cleaner html output; and the ability to export tables to more formats, including gt tables, flextable objects, and Microsoft Word documents.\ndatasummary is a general-purpose table-making tool. It allows us to build (nearly) any summary table we want by using simple 2-sided formulae. For example, in the expression x + y ~ mean + sd, the left-hand side of the formula identifies the variables or statistics to display as rows, and the right-hand side defines the columns. Below, we will see how variables and statistics can be “nested” with the * operator to produce tables like the ones above.\nIn addition to datasummary, the modelsummary package includes a “family” of companion functions named datasummary_*. These functions facilitate the production of standard, commonly used tables. This family currently includes:\nIn the next three sections, we illustrate how to use datasummary_balance, datasummary_correlation, datasummary_skim, and datasummary_crosstab. Then, we dive into datasummary itself to highlight its ease and flexibility.",
+    "text": "New exponentiate argument for modelsummary() and modelplot()\ngof_map accepts a vector such as c(\"rmse\", \"nobs\", \"r.squared\")\nDrop rlang dependency\nBug fixes",
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-    "text": "Custom summary functions\nThe Mean function is a shortcut supplied by modelsummary, and it is equivalent to mean(x,na.rm=TRUE). Since the flipper_length_mm variable includes missing observation, using the mean formula (with default na.rm=FALSE) would produce a missing/empty cell:\n\ndatasummary(flipper_length_mm ~ mean,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_rwla8m86hmh3wmociyd9\n\n\n      \n\n \n                mean\n              \n\nflipper_length_mm\n                  \n                \n\n\n\n    \n\n\nmodelsummary supplies these functions: Mean, SD, Min, Max, Median, P0, P25, P50, P75, P100, Histogram, and a few more (see the package documentation).\nUsers are also free to create and use their own custom summaries. Any R function which takes a vector and produces a single value is acceptable. For example, the Range functions return a numerical value, and the MinMax returns a string:\n\nRange &lt;- function(x) max(x, na.rm = TRUE) - min(x, na.rm = TRUE)\n\ndatasummary(flipper_length_mm ~ Range,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_jyvtvaehr9vgol4jhh7s\n\n\n      \n\n \n                Range\n              \n\nflipper_length_mm\n                  59.00\n                \n\n\n\n    \n\nMinMax &lt;- function(x) paste0('[', min(x, na.rm = TRUE), ', ', max(x, na.rm = TRUE), ']')\n\ndatasummary(flipper_length_mm ~ MinMax,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_u3p4jhx097tdtetdzvmi\n\n\n      \n\n \n                MinMax\n              \n\nflipper_length_mm\n                  [172, 231]",
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+    "text": "datasummary_balance:\n\nAccepts ~ 1 as a formula to summarize all data.\n\nMisc:\n\ndocumentation improvements\nRMSE included by default in models of class lm",
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-    "text": "Concatenating with +\n\nTo include more rows and columns, we use the + sign:\n\ndatasummary(flipper_length_mm + body_mass_g ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_a7tgtk5vm1vbmj83atj4\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nflipper_length_mm\n                  200.92 \n                  14.06 \n                \n\nbody_mass_g      \n                  4201.75\n                  801.95\n                \n\n\n\n\n    \n\n\nSometimes, it can be cumbersome to list all variables separated by + signs. The All() function is a useful shortcut:\n\ndatasummary(All(penguins) ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_qhimj363vv9biddepqfy\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nrownames         \n                  172.50 \n                  99.45 \n                \n\nbill_length_mm   \n                  43.92  \n                  5.46  \n                \n\nbill_depth_mm    \n                  17.15  \n                  1.97  \n                \n\nflipper_length_mm\n                  200.92 \n                  14.06 \n                \n\nbody_mass_g      \n                  4201.75\n                  801.95\n                \n\nyear             \n                  2008.03\n                  0.82  \n                \n\n\n\n\n    \n\n\nBy default, All selects all numeric variables. This behavior can be changed by modifying the function’s arguments. See ?All for details.",
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+    "text": "modelsummary:\n\nvcov strings like HC1 and Robust are now case-insensitive\ngof_map now accepts a data.frame or tibble with a fmt list-column which includes functions (see Examples in docs)\nR2 is no longer computed by default for bayesian and mixed effects models. An informative one-time warning is printed about the metrics argument.\n\ndatasummary_skim:\n\nHistograms now work in Jupyter\nBugfix: harmless error message is no longer printed\n\nkableExtra factory:\n\nThe col.names argument can now be passed to kableExtra::kbl through the … ellipsis.\n\nMisc:\n\nMany small improvements to the vignettes and docs\noutput = \"github_document\" is now supported",
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-    "text": "Nesting with *\n\ndatasummary can nest variables and statistics inside categorical variables using the * symbol. When applying the the * operator to factor, character, or logical variables, columns or rows will automatically be nested. For instance, if we want to display separate means for each value of the variable sex, we use mean * sex:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * sex,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_1r9ymtuhq8t5wqhjkihm\n\n\n      \n\n \n                female\n                male\n              \n\n\nflipper_length_mm\n                  197.36 \n                  204.51 \n                \n\nbody_mass_g      \n                  3862.27\n                  4545.68\n                \n\n\n\n\n    \n\n\nWe can use parentheses to nest several terms inside one another, using a call of this form: x * (y + z). Here is an example with nested columns:\n\ndatasummary(body_mass_g ~ sex * (mean + sd),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_obmklq0itbezch9njcgh\n\n\n      \n\n\n \nfemale\nmale\n\n\n \n                mean\n                sd\n                mean\n                sd\n              \n\n\nbody_mass_g\n                  3862.27\n                  666.17\n                  4545.68\n                  787.63\n                \n\n\n\n    \n\n\nHere is an example with nested rows:\n\ndatasummary(sex * (body_mass_g + flipper_length_mm) ~ mean + sd,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_sor9t2xlfjxtsj45gmat\n\n\n      \n\nsex\n                 \n                mean\n                sd\n              \n\n\nfemale\n                  body_mass_g      \n                  3862.27\n                  666.17\n                \n\n      \n                  flipper_length_mm\n                  197.36 \n                  12.50 \n                \n\nmale  \n                  body_mass_g      \n                  4545.68\n                  787.63\n                \n\n      \n                  flipper_length_mm\n                  204.51 \n                  14.55 \n                \n\n\n\n\n    \n\n\nThe order in which terms enter the formula determines the order in which labels are displayed. For example, this shows island above sex:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * island * sex,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_5m9enarf2j6ftcj2hvwr\n\n\n      \n\n\n \nBiscoe\nDream\nTorgersen\n\n\n \n                female\n                male\n                female\n                male\n                female\n                male\n              \n\n\n\nflipper_length_mm\n                  205.69 \n                  213.29 \n                  190.02 \n                  196.31 \n                  188.29 \n                  194.91 \n                \n\nbody_mass_g      \n                  4319.38\n                  5104.52\n                  3446.31\n                  3987.10\n                  3395.83\n                  4034.78\n                \n\n\n\n\n    \n\n\nThis shows sex above island values:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * sex * island,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_rjyraq8dor88hhfe9ax2\n\n\n      \n\n\n \nfemale\nmale\n\n\n \n                Biscoe\n                Dream\n                Torgersen\n                Biscoe\n                Dream\n                Torgersen\n              \n\n\n\nflipper_length_mm\n                  205.69 \n                  190.02 \n                  188.29 \n                  213.29 \n                  196.31 \n                  194.91 \n                \n\nbody_mass_g      \n                  4319.38\n                  3446.31\n                  3395.83\n                  5104.52\n                  3987.10\n                  4034.78\n                \n\n\n\n\n    \n\n\nBy default, datasummary omits column headers with a single value/label across all columns, except for the header that sits just above the data. If the header we want to see is not displayed, we may want to reorder the terms of the formula. To show all headers, set sparse_header=FALSE:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * sex * island,\n            data = penguins,\n            sparse_header = FALSE)\n\n \n\n  \n    \n\ntinytable_hxgj4ka0hhxdbc1i7jnt\n\n\n      \n\n\n \nfemale\nmale\n\n\n \n                Biscoe\n                Dream\n                Torgersen\n                Biscoe\n                Dream\n                Torgersen\n              \n\n\n\nflipper_length_mm\n                  205.69 \n                  190.02 \n                  188.29 \n                  213.29 \n                  196.31 \n                  194.91 \n                \n\nbody_mass_g      \n                  4319.38\n                  3446.31\n                  3395.83\n                  5104.52\n                  3987.10\n                  4034.78\n                \n\n\n\n\n    \n\n\nWhen using sparse_header=FALSE, it is often useful to insert Heading() * in the table formula, in order to rename or omit some of the labels manually. Type ?tables::Heading for details and examples.",
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-    "text": "Renaming with =\n\nPersonally, I prefer to rename variables and values before drawing my tables, using backticks when variable names include whitespace. For example,\n\ntmp &lt;- penguins %&gt;%\n       select(`Flipper length (mm)` = flipper_length_mm,\n              `Body mass (g)` = body_mass_g)\n\ndatasummary(`Flipper length (mm)` + `Body mass (g)` ~ Mean + SD,\n            data = tmp)\n\n \n\n  \n    \n\ntinytable_zkc51txei4500qtusgg3\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nFlipper length (mm)\n                  200.92 \n                  14.06 \n                \n\nBody mass (g)      \n                  4201.75\n                  801.95\n                \n\n\n\n\n    \n\n\nHowever, thanks to the tables package, datasummary offers two additional mechanisms to rename. First, we can wrap a term in parentheses and use the equal = sign: (NewName=OldName):\n\ndatasummary((`Flipper length (mm)` = flipper_length_mm) + (`Body mass (g)` = body_mass_g) ~\n            island * ((Avg. = Mean) + (Std.Dev. = SD)),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_4h1bhvxkrwhahymhzla9\n\n\n      \n\n\n \nBiscoe\nDream\nTorgersen\n\n\n \n                Avg.\n                Std.Dev.\n                Avg.\n                Std.Dev.\n                Avg.\n                Std.Dev.\n              \n\n\n\nFlipper length (mm)\n                  209.71 \n                  14.14 \n                  193.07 \n                  7.51  \n                  191.20 \n                  6.23  \n                \n\nBody mass (g)      \n                  4716.02\n                  782.86\n                  3712.90\n                  416.64\n                  3706.37\n                  445.11\n                \n\n\n\n\n    \n\n\nSecond, we can use the Heading() function:\n\ndatasummary(Heading(\"Flipper length (mm)\") * flipper_length_mm + Heading(\"Body mass (g)\") * body_mass_g ~ island * (Mean + SD),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_rxown474cw0yxpryifej\n\n\n      \n\n\n \nBiscoe\nDream\nTorgersen\n\n\n \n                Mean\n                SD\n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nFlipper length (mm)\n                  209.71 \n                  14.14 \n                  193.07 \n                  7.51  \n                  191.20 \n                  6.23  \n                \n\nBody mass (g)      \n                  4716.02\n                  782.86\n                  3712.90\n                  416.64\n                  3706.37\n                  445.11\n                \n\n\n\n\n    \n\n\nThe Heading function also has a nearData argument which can be useful in cases where some rows are nested but others are not. Compare the last row of these two tables:\n\ndatasummary(sex * (flipper_length_mm + bill_length_mm) + Heading(\"Body mass (g)\") * body_mass_g ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_ei003aavc5e7vur8lzz1\n\n\n      \n\nsex\n                 \n                Mean\n                SD\n              \n\n\nfemale\n                  flipper_length_mm\n                  197.36 \n                  12.50 \n                \n\n      \n                  bill_length_mm   \n                  42.10  \n                  4.90  \n                \n\nmale  \n                  flipper_length_mm\n                  204.51 \n                  14.55 \n                \n\n      \n                  bill_length_mm   \n                  45.85  \n                  5.37  \n                \n\n      \n                  Body mass (g)    \n                  4201.75\n                  801.95\n                \n\n\n\n\n    \n\ndatasummary(sex * (flipper_length_mm + bill_length_mm) + Heading(\"Body mass (g)\", nearData=FALSE) * body_mass_g ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_7g4uyfmhkfin69idswko\n\n\n      \n\nsex\n                 \n                Mean\n                SD\n              \n\n\nfemale       \n                  flipper_length_mm\n                  197.36 \n                  12.50 \n                \n\n             \n                  bill_length_mm   \n                  42.10  \n                  4.90  \n                \n\nmale         \n                  flipper_length_mm\n                  204.51 \n                  14.55 \n                \n\n             \n                  bill_length_mm   \n                  45.85  \n                  5.37  \n                \n\nBody mass (g)\n                                   \n                  4201.75\n                  801.95",
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+    "text": "modelsummary:\n\nF statistic takes into account vcov argument\nSupport group = group ~ model + term\n\ndatasummary_balance:\n\nWeighted means and standard deviations are now supported. Counts and percentages are not, but raise a warning.\n\nMisc:\n\nBugfix: rounding in LaTeX w/ siunitx and NaN entries.\noutput=‘jupyter’ no longer prints an extraneous TRUE to the notebook",
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-    "text": "Counts and Percentages\nThe tables package allows datasummary to use neat tricks to produce frequency tables:\n\nAdd a N to the right-hand side of the equation.\nAdd Percent() to the right-hand side to calculate the percentage of observations in each cell.\nAdd 1 to the left-hand side to include a row with the total number of observations:\n\n\ndatasummary(species * sex + 1 ~ N + Percent(),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_755u18xqvxqbag3mv27b\n\n\n      \n\nspecies\n                sex\n                N\n                Percent\n              \n\n\nAdelie   \n                  female\n                  73 \n                  21.22 \n                \n\n         \n                  male  \n                  73 \n                  21.22 \n                \n\nChinstrap\n                  female\n                  34 \n                  9.88  \n                \n\n         \n                  male  \n                  34 \n                  9.88  \n                \n\nGentoo   \n                  female\n                  58 \n                  16.86 \n                \n\n         \n                  male  \n                  61 \n                  17.73 \n                \n\n         \n                  All   \n                  344\n                  100.00\n                \n\n\n\n\n    \n\n\nNote that the Percent() function accepts a denom argument to determine if percentages should be calculated row or column-wise, or if they should take into account all cells.",
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+    "text": "modelsummary:\n\nImproved vcov argument handling for fixest models (#357 by @grantmcdermott)\nFix display of fixest::i() variables and interactions (#361 by @grantmcdermott)\nConsistent display of clustered SEs (#356, #363 and #366 by @grantmcdermott)\n\ndatasummary_correlation:\n\nadd_rows and add_columns arguments are now available here.\n\nMisc:\n\nGlobal options for output factories are renamed: modelsummary_factory_default, modelsummary_factory_html, etc.\nHot fix for change in R-devel behavior or intersect\n\nBug fixes:\n\ndatasummary_balance: escape variable names when escape=TRUE\nBlogdown LaTeX dependency bug when output is HTML",
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-    "text": "Weighted percentages\nThe Percent() pseudo-function also accepts a fn argument, which must be a function which accepts two vectors: x is the values in the current cell, and y is all the values in the whole dataset. The default fn is:\n\ndatasummary(species * sex + 1 ~ N + Percent(fn = function(x, y) 100 * length(x) / length(y)),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_205a3bxbkahnluvbhizw\n\n\n      \n\nspecies\n                sex\n                N\n                Percent\n              \n\n\nAdelie   \n                  female\n                  73 \n                  21.22 \n                \n\n         \n                  male  \n                  73 \n                  21.22 \n                \n\nChinstrap\n                  female\n                  34 \n                  9.88  \n                \n\n         \n                  male  \n                  34 \n                  9.88  \n                \n\nGentoo   \n                  female\n                  58 \n                  16.86 \n                \n\n         \n                  male  \n                  61 \n                  17.73 \n                \n\n         \n                  All   \n                  344\n                  100.00\n                \n\n\n\n\n    \n\n\nThe code above takes the number of elements in the cell length(x) and divides it by the number of total elements length(y).\nNow, let’s say we want to display percentages weighted by one of the variables of the dataset. This can often be useful with survey weights, for example. Here, we use an arbitrary column of weights called flipper_length_mm:\n\nwtpct &lt;- function(x, y) sum(x, na.rm = TRUE) / sum(y, na.rm = TRUE) * 100\ndatasummary(species * sex + 1 ~ N + flipper_length_mm * Percent(fn = wtpct),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_t8lb898vqn7z0edqtq1e\n\n\n      \n\nspecies\n                sex\n                N\n                Percent\n              \n\n\nAdelie   \n                  female\n                  73 \n                  19.95 \n                \n\n         \n                  male  \n                  73 \n                  20.44 \n                \n\nChinstrap\n                  female\n                  34 \n                  9.49  \n                \n\n         \n                  male  \n                  34 \n                  9.89  \n                \n\nGentoo   \n                  female\n                  58 \n                  17.95 \n                \n\n         \n                  male  \n                  61 \n                  19.67 \n                \n\n         \n                  All   \n                  344\n                  100.00\n                \n\n\n\n\n    \n\n\nIn each cell we now have the sum of weights in that cell, divided by the total sum of weights in the column.",
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+    "text": "Breaking change:\n\nSupport for dcolumn for dot-aligned columns is deprecated. Use “d” in the align argument instead.\n\nOther changes:\n\nLaTeX output: Numeric entries are wrapped in the \\num{} function from the siunitx package by default. This produces much nicer formatting. This can be disabled with a global option. See ?modelsummary\nThe align argument accepts a “d” column for dot-alignment using the siunitx LaTeX package: align=\"ldd\".\nHTML tables display proper minus signs.\nNew escape argument in most table-building functions.\nLaTeX output accepts the threeparttable=TRUE argument through ...\nNo more dependency on tidyr\n\nmodelsummary:\n\ngroup: The order of terms in the formula determines the order of rows/columns\n\nmodelsummary_wide:\n\nNote: This function will eventually be deprecated\nBugfix with statistic=NULL.\n\nmodelplot:\n\nPreserves order of models in the user-supplied list\n\ndatasummary_crosstab:\n\nstatistic=NULL produces a very basic crosstab\n\ndatasummary_crosstab:\n\nDefault alignment “lrrrrr” consistent with other datasummary_* functions",
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-    "text": "Custom percentages\nHere is another simple illustration of Percent function mechanism in action, where we combine counts and percentages in a simple nice label:\n\ndat &lt;- mtcars\ndat$cyl &lt;- as.factor(dat$cyl)\n\nfn &lt;- function(x, y) {\n    out &lt;- sprintf(\n        \"%s (%.1f%%)\",\n        length(x),\n        length(x) / length(y) * 100)\n}\ndatasummary(\n    cyl ~ Percent(fn = fn),\n    data = dat)\n\n \n\n  \n    \n\ntinytable_k77q7ak59ws303mablu9\n\n\n      \n\ncyl\n                Percent\n              \n\n\n4\n                  11 (34.4%)\n                \n\n6\n                  7 (21.9%) \n                \n\n8\n                  14 (43.8%)",
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+    "text": "modelsummary:\n\nDisable stars footnote with options(\"modelsummary_stars_note\" = FALSE)\nlongtable=TRUE works for LaTeX output\nInteractions with “:” are no longer converted to “x” when coef_map or coef_rename are used.\ngroup = model ~ term + group is now supported.\n\ndatasummary_skim:\n\ndatasummary_skim(\"categorical\") keeps NA by default. Users can convert variables to factors before calling datasummary_skim to exclude NA.\n\nOther:\n\nImproved warnings for bad calls: modelsummary(model1, model2)\ngt titles use the new caption argument in the gt 0.3.0 function\nBug fix: Overaggressive tests for glue strings prevented functions inside {}",
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-    "text": "Factor\nThe * nesting operator that we used above works automatically for factor, character, and logical variables. Sometimes, it is convenient to use it with other types of variables, such as binary numeric variables. In that case, we can wrap the variable name inside a call to Factor(). This allows us to treat a variable as factor, without having to modify it in the original data. For example, in the mtcars data, there is a binary numeric variable call am. We nest statistics within categories of am by typing:\n\ndatasummary(mpg + hp ~ Factor(am) * (mean + sd),\n            data = mtcars)\n\n \n\n  \n    \n\ntinytable_adc2rle4l1ijhavmka2z\n\n\n      \n\n\n \n0\n1\n\n\n \n                mean\n                sd\n                mean\n                sd\n              \n\n\n\nmpg\n                  17.15 \n                  3.83 \n                  24.39 \n                  6.17 \n                \n\nhp \n                  160.26\n                  53.91\n                  126.85\n                  84.06",
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+    "text": "Breaking change:\n\nThe default significance markers stars=TRUE have been updated to be consistent with the default output from base R (e.g., in summary.lm). The new significance thresholds are: “+” p &lt; 0.1, “” p &lt; 0.05, ”” p &lt; 0.01, ”” p &lt; 0.001\n\ndatasummary_crosstab:\n\nNew function to produce cross-tabulations\n\ndatasummary:\n\nN is smart enough to return either the number of elements in a subset or the number of non-missing observations in a variable\n\ndatasummary_balance:\n\nKeeps NAs in factor variables by default. Users can convert their variables with the factor() function to omit NAs automatically.\n\nmodelsummary:\n\nthemes can be set using global options (experimental)\nnew vcov options: “bootstrap”, “HAC”, “NeweyWest”, “Andrews”, “panel-corrected”, “weave”, “outer-product”\nA valid get_gof (glance) is now optional.\n… is pushed through to sandwich, which allows things like: modelsummary(model, vcov = \"bootstrap\", R = 1000, cluster = \"firm\")\n\nOther:\n\nJupyter notebook support via output=\"jupyter\"\nBug fixes",
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-    "text": "Arguments: na.rm=TRUE\n\nWe can pass any argument to the summary function by including a call to Arguments(). For instance, there are missing values in the flipper_length_mm variable of the penguins dataset. Therefore, the standard mean function will produce no result, because its default argument is na.rm=FALSE. We can change that by calling:\n\ndatasummary(flipper_length_mm ~ mean + mean*Arguments(na.rm=TRUE),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_d4f7ey98nxsmi6a67is0\n\n\n      \n\n \n                mean\n                mean \n              \n\nflipper_length_mm\n                  \n                  200.92\n                \n\n\n\n    \n\n\nNotice that there is an empty cell (NA) under the normal mean function, but that the mean call with Arguments(na.rm=TRUE) produced a numeric result.\nWe can pass the same arguments to multiple functions using the parentheses:\n\ndatasummary(flipper_length_mm ~ (mean + sd) * Arguments(na.rm=TRUE),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_g7o1lwooojehnq7fkzg2\n\n\n      \n\n \n                mean\n                sd\n              \n\nflipper_length_mm\n                  200.92\n                  14.06\n                \n\n\n\n    \n\n\nNote that the shortcut functions that modelsummary supplies use na.rm=TRUE by default, so we can use them directly without arguments, even when there are missing values:\n\ndatasummary(flipper_length_mm ~ Mean + Var + P75 + Range,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_kz2ccpx6i2n68xge1gic\n\n\n      \n\n \n                Mean\n                Var\n                P75\n                Range\n              \n\nflipper_length_mm\n                  200.92\n                  197.73\n                  213.00\n                  59.00",
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+    "text": "modelsummary:\n\nnew arguments for modelsummary: group and group_map for grouped parameters (e.g., outcome levels in multinomial logit or components of gamlss model).\ndvnames() makes it easy to get dependent variable column titles (thanks to @NickCH-K)\noutput=\"modelsummary_list\" to save a lightweight list-based representation of the table which can be saved and fed to modelsummary once more to get a full table.\nvcov adds a row to note the type of standard errors.\nmodelsummary accepts a single model with multiple vcovs.\nget_gof forwards … to model_performance\ncoef_map accepts unnamed vectors for easy subsetting\nfixest::fixest_multi support\noptions(modelsummary_get) to set the order of extraction functions to use under the hood (broom vs. easystats vs. all)\nmetrics argument of performance::model_performance is available via modelsummary’s … ellipsis to limit the GOF statistics in Bayesian models.\nusers can omit the stars legend note by using glue strings: estimate=\"{estimate}{stars}\"\noutput=“html” can use gt by setting options(modelsummary_factory_html=\"gt\")\n\ndatasummary_correlation:\n\npasses ... forward\nnew function: datasummary_correlation_format\ndatasummary_correlation’s method argument accepts functions and “pearspear” (thanks to @joachim-gassen)\n\ndatasummary:\n\ndatasummary functions and rounding accept …, big.mark, etc.\n\ndatasummary_skim:\n\nnow works with haven_labeled numeric\nfaster tables with bayesian models.\n\nBug fixes and lints",
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-    "text": "Arguments: Weighted Mean\nYou can use the Arguments mechanism to do various things, such as calculating weighted means:\n\nnewdata &lt;- data.frame(\n  x = rnorm(20),\n  w = rnorm(20),\n  y = rnorm(20))\n\ndatasummary(x + y ~ weighted.mean * Arguments(w = w),\n            data = newdata)\n\n \n\n  \n    \n\ntinytable_o9udgga7ineti6dtcum6\n\n\n      \n\n \n                weighted.mean\n              \n\n\nx\n                  -15.63\n                \n\ny\n                  -11.14\n                \n\n\n\n\n    \n\n\nWhich produces the same results as:\n\nweighted.mean(newdata$x, newdata$w)\n\n[1] -15.62678\n\nweighted.mean(newdata$y, newdata$w)\n\n[1] -11.1353\n\n\nBut different results from:\n\nmean(newdata$x)\n\n[1] -0.2762772\n\nmean(newdata$y)\n\n[1] 0.1409996",
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+    "text": "new output format: latex_tabular\ntidy_custom allows partial term matches\nmodelsummary(coef_rename) accepts functions\nnew function coef_rename for use in modelsummary(coef_rename=coef_rename)\nmodelplot accepts add_rows to add reference categories\ninformative error message when estimate or statistic is not available\nbug fixes",
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-    "text": "Logical subsets\nCool stuff is possible with logical subsets:\n\ndatasummary((bill_length_mm &gt; 44.5) + (bill_length_mm &lt;= 44.5) ~ Mean * flipper_length_mm,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_ty9gtsz2l0qjrkw6tvwb\n\n\n      \n\n\n                \n              \n\n\nbill_length_mm &gt; 44.5 \n                  209.68\n                \n\nbill_length_mm \n                  \n192.45\n                \n\n\n\n\n    \n\n\nSee the tables package documentation for more details and examples.",
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+    "text": "statistic_override becomes vcov\nvcov accepts shortcuts: “robust”, “stata”, “HC0”, etc.\nvcov accepts formulas for clustered SEs: ~group\nmodelsummary_wide has a new “stacking” argument\nhtml horizontal rule to separate estimates form gof\ngof_map accepts list of lists. only needs 3 columns.\nsupport officedown Rmd\nestimate accepts a vector for per model estimates\noptions(modelsummary_default) can be markdown, html, latex\nbug: passing arguments through …\nbug: stars and rounding",
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-    "text": "Missing values\nAt least 3 distinct issues can arise related to missing values.\nFunctions and na.rm\n\nAn empty cell can appear in the table when a statistical function returns NA instead of a numeric value. In those cases, you should:\n\nMake sure that your statistical function (e.g., mean or sd) uses na.rm=TRUE by default\nUse the Arguments strategy to set na.rm=TRUE (see the Arguments section of this vignette).\nUse a convenience function supplied by modelsummary, where na.rm is TRUE by default: Mean, SD, P25, etc.\nEmpty crosstab cells\nAn empty cell can appear in the table when a crosstab is deeply nested, and there are no observations for a given combination of covariates. In those cases, you can use the * DropEmpty pseudo-function. See the “Empty cells” section of this vignette for examples.\n\ndatasummary(island * species * body_mass_g ~ sex * (Mean + SD),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_ghvvioffsipv5kzz07p0\n\n\n      \n\n\n \n \n \nfemale\nmale\n\n\nisland\n                species\n                 \n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nBiscoe   \n                  Adelie   \n                  body_mass_g\n                  3369.32\n                  343.47\n                  4050.00\n                  355.57\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                  4679.74\n                  281.58\n                  5484.84\n                  313.16\n                \n\nDream    \n                  Adelie   \n                  body_mass_g\n                  3344.44\n                  212.06\n                  4045.54\n                  330.55\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                  3527.21\n                  285.33\n                  3938.97\n                  362.14\n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\nTorgersen\n                  Adelie   \n                  body_mass_g\n                  3395.83\n                  259.14\n                  4034.78\n                  372.47\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\n\n\n\n    \n\ndatasummary(island * species * body_mass_g ~ sex * (Mean + SD) * DropEmpty(),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_hbebehgvncwr2fpm8zgj\n\n\n      \n\n\n \n \n \nfemale\nmale\n\n\nisland\n                species\n                 \n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nBiscoe   \n                  Adelie   \n                  body_mass_g\n                  3369.32\n                  343.47\n                  4050.00\n                  355.57\n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                  4679.74\n                  281.58\n                  5484.84\n                  313.16\n                \n\nDream    \n                  Adelie   \n                  body_mass_g\n                  3344.44\n                  212.06\n                  4045.54\n                  330.55\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                  3527.21\n                  285.33\n                  3938.97\n                  362.14\n                \n\nTorgersen\n                  Adelie   \n                  body_mass_g\n                  3395.83\n                  259.14\n                  4034.78\n                  372.47\n                \n\n\n\n\n    \n\n\nPercent()\nBy default, the Percent() function computes the share of each category with respect to the whole data, including missing observations, but we can work around this with some trickery:jig\n\ndat &lt;- data.frame(z = c(0,0,1,0,NA,0,1))\n\nprop.table(table(dat$z))\n\n\n        0         1 \n0.6666667 0.3333333 \n\ndatasummary(Factor(z) ~ N + Percent(), data = dat)\n\n \n\n  \n    \n\ntinytable_xf3oydm5ss7i11ppmy75\n\n\n      \n\nz\n                N\n                Percent\n              \n\n\n0\n                  4\n                  57.14\n                \n\n1\n                  2\n                  28.57\n                \n\n\n\n\n    \n\n\nTo count percentages without missing values, we can exploit the fact that Percent() accepts a denom argument which can be a logical vector:\n\ndatasummary(Factor(z) ~ N + Percent(denom = !is.na(dat$z)), data = dat)\n\n \n\n  \n    \n\ntinytable_xz1uq0nd4uarcyh7o2n2\n\n\n      \n\nz\n                N\n                Percent\n              \n\n\n0\n                  4\n                  66.67\n                \n\n1\n                  2\n                  33.33",
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+    "text": "glue format for estimate and statistic\neasystats support for model info extraction\ndeprecate statistic_vertical\ndeprecate extract_models. Use modelsummary(output=“dataframe”) instead.\nmodelplot pushes … through to modelsummary(output=“dataframe”)\ndatasummary_skim(type=“dataset”)\ngof_map omits by default\ndatasummary_balance uses row percentages\nstatistic_override does not require a list\nstatistic_override accepts a single model\nN function for well formatted N in datasummary\nBug fixes",
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-    "text": "Add rows\n\nnew_rows &lt;- data.frame('Does',\n                       2,\n                       'plus',\n                       2,\n                       'equals',\n                       5,\n                       '?')\ndatasummary(flipper_length_mm + body_mass_g ~ species * (Mean + SD),\n            data = penguins,\n            add_rows = new_rows)\n\n \n\n  \n    \n\ntinytable_o4d4rgd32ctt4yallwk1\n\n\n      \n\n\n \nAdelie\nChinstrap\nGentoo\n\n\n \n                Mean\n                SD\n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nflipper_length_mm\n                  189.95 \n                  6.54  \n                  195.82 \n                  7.13  \n                  217.19 \n                  6.48  \n                \n\nbody_mass_g      \n                  3700.66\n                  458.57\n                  3733.09\n                  384.34\n                  5076.02\n                  504.12\n                \n\nDoes             \n                  2.00   \n                  plus  \n                  2.00   \n                  equals\n                  5.00   \n                  ?",
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+    "text": "new function: modelsummary_wide\ncoef_omit and gof_omit use grepl(perl=TRUE)\nfmt accepts integer, string or function and respects options(OutDec=“,”)\nalign argument for modelsummary\nalign is more liberal to accept dcolumn alignment\nglance_custom methods for lfe and fixest\nbug fixes",
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-    "text": "Add columns\n\nnew_cols &lt;- data.frame('New Stat' = runif(2))\ndatasummary(flipper_length_mm + body_mass_g ~ species * (Mean + SD),\n            data = penguins,\n            add_columns = new_cols)\n\n \n\n  \n    \n\ntinytable_zvoinlsco7bnd6nzv1k8\n\n\n      \n\n\n \nAdelie\nChinstrap\nGentoo\n \n\n\n \n                Mean\n                SD\n                Mean\n                SD\n                Mean\n                SD\n                New.Stat\n              \n\n\n\nflipper_length_mm\n                  189.95 \n                  6.54  \n                  195.82 \n                  7.13  \n                  217.19 \n                  6.48  \n                  0.99\n                \n\nbody_mass_g      \n                  3700.66\n                  458.57\n                  3733.09\n                  384.34\n                  5076.02\n                  504.12\n                  0.94",
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+    "text": "new argument: coef_rename\nnew function: datasummary_df\npreserve term order in modelsummary\nrefactor datasummary_balance\ndatasummary_skim uses svg histograms instead of unicode\nremoved 5 dependencies\npass … to kableExtra::kbl for more customization\ntest improvements\ninternal code style\nbug fixes",
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-    "text": "modelsummary creates tables and plots to present descriptive statistics and to summarize statistical models in R."
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-    "text": "What?\nmodelsummary includes two families of functions:\n\nModel Summary\n\nmodelsummary: Regression tables with side-by-side models.\nmodelplot: Coefficient plots.\n\nData Summary\n\ndatasummary: Powerful tool to create (multi-level) cross-tabs and data summaries.\ndatasummary_crosstab: Cross-tabulations.\ndatasummary_balance: Balance tables with subgroup statistics and difference in means (aka “Table 1”).\ndatasummary_correlation: Correlation tables.\ndatasummary_skim: Quick overview (“skim”) of a dataset.\ndatasummary_df: Turn dataframes into nice tables with titles, notes, etc."
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-    "text": "Why?\nHere are a few benefits of modelsummary over some alternative packages:\n\nEasy\nmodelsummary is very easy to use. This simple call often suffices:\nlibrary(modelsummary)\n\nmod &lt;- lm(y ~ x, dat)\nThe command above will automatically display a summary table in the Rstudio Viewer or in a web browser. All you need is one word to change the output format. For example, a text-only version of the table can be printed to the Console by typing:\nmodelsummary(mod, output = \"markdown\")\nTables in Microsoft Word and LaTeX formats can be saved to file by typing:\nmodelsummary(mod, output = \"table.docx\")\nmodelsummary(mod, output = \"table.tex\")\n\n\nFlexible\nInformation: The package offers many intuitive and powerful utilities to customize the information reported in a summary table. You can rename, reorder, subset or omit parameter estimates; choose the set of goodness-of-fit statistics to include; display various “robust” standard errors or confidence intervals; add titles, footnotes, or source notes; insert stars or custom characters to indicate levels of statistical significance; or add rows with supplemental information about your models.\nAppearance: Thanks to the tinytable, gt, kableExtra, huxtable, flextable, and DT packages, the appearance of modelsummary tables is endlessly customizable. The appearance customization page shows tables with colored cells, weird text, spanning column labels, row groups, titles, source notes, footnotes, significance stars, and more. This only scratches the surface of possibilities.\nSupported models: Thanks to the broom and parameters, modelsummary supports hundreds of statistical models out-of-the-box. Installing other packages can extend the capabilities further (e.g., broom.mixed). It is also very easy to add or customize your own models.\nOutput formats: modelsummary tables can be saved to HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, RTF, JPG, or PNG formats. They can also be inserted seamlessly in Rmarkdown documents to produce automated documents and reports in PDF, HTML, RTF, or Microsoft Word formats.\n\n\nDangerous\nmodelsummary is dangerous! It allows users to do stupid stuff like inserting squirrels in all your tables.\n\n\n\nReliable\nmodelsummary is reliably dangerous! The package is developed using a suite of unit tests with about 95% coverage, so it (probably) won’t break.\n\n\nCommunity\nmodelsummary does not try to do everything. Instead, it leverages the incredible work of the R community. By building on top of the broom and parameters packages, modelsummary already supports hundreds of model types out-of-the-box. modelsummary also supports five of the most popular table-building and customization packages: gt, kableExtra, huxtable, flextable, and DT packages. By using those packages, modelsummary allows users to produce beautiful, endlessly customizable tables in a wide variety of formats, including HTML, PDF, LaTeX, Markdown, and MS Word.\nOne benefit of this community-focused approach is that when external packages improve, modelsummary improves as well. Another benefit is that leveraging external packages allows modelsummary to have a massively simplified codebase (relative to other similar packages). This should improve long term code maintainability, and allow contributors to participate through GitHub."
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-    "text": "How?\nYou can install modelsummary from CRAN:\ninstall.packages('modelsummary')\nYou can install the development version of modelsummary and its dependencies from R-Universe:\ninstall.packages(\n    c(\"modelsummary\", \"tinytable\", \"insight\", \"performance\", \"parameters\"),\n    repos = c(\n        \"https://vincentarelbundock.r-universe.dev\",\n        \"https://easystats.r-universe.dev\"))\nRestart R completely before moving on."
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-    "text": "Get started\nThe GET STARTED vignette will walk you through the basics of modelsummary.\nYou will find that there are a million ways to customize the tables and plots produced by modelsummary, but the Get Started page only scratches the surface. For details, see the vignettes:\n\nData Summaries\nModel Summaries\nModel Plots\nmodelsummary extensions and customization\nThemes and Styles"
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-    "text": "New features:\n\nmodelsummary() gets a gof_function argument which accepts functions to extract custom information from models.\nflextable: Support spanning column headers\n\ndatasummary_balance():\n\nformula can now include variables on the left-hand side to indicate the subset of columns to summarize: datasummary_balance(mpg + hp ~ am, data = mtcars) Thanks to @etiennebacher for feature request #751.\n\nBugs:\n\nescape argument not respected in datasummary_df(). Thanks to @adamaltmejd for report #740\ndatasummary_correlation() supports data.table. Thanks to volatilehead on Twitter for report #737.\nAccepts named estimate argument when using shape and statistics are horizontal. Thanks to @iago-pssjd for report #745.\nLabelled values but no label for variable broke datasummary(). Thanks to @marklhc for report #752.\ncoef_map does not work when there is a group. Thanks to @mccarthy-m-g for report #757.\nkableExtra: fix spanning column headers when using the shape argument.\nMultiple footnotes and line breaks in footnotes are now allowed in tinytable output. Thanks to\n\n\n\n\nMAJOR BREAKING CHANGE: The default output format is now tinytable instead of kableExtra. Learn more about tinytable here:\nhttps://vincentarelbundock.github.io/tinytable/\nTo revert to the previous behavior persistently, users can call:\nlibrary(modelsummary) config_modelsummary(factory_default = “kableExtra”)\nOther breaking changes:\n\nThe statistic_override argument was replaced by vcov over 1 year ago, with appropriate deprecation warnings. It is now fully removed.\nThe group argument was replaced by shape several releases ago. It is now fully removed.\ndatasummary_skim()\n\nhistograms are only available with the tinytable backend. This allows a lot of code simplification and more customization.\nThe order of arguments type and output is switched for consistency with other functions.\nhistogram argument is deprecated.\n\n\nNew features:\n\ndatasummary_skim():\n\nNew type=\"all\" by default to display both numeric and categorical variables in a single table with distinct panels. This feature is only available with the tinytable backend (default).\nby argument allows summarizing numeric variables by group.\nfun_numeric argument accepts a list of functions to control the summary columns.\n\nmodelsummary():\n\nstatistic and estimate can be specified as named vectors to control the names of statistics when displayed in different columns using the shape argument. (Thanks to @mps9506 for bug report #722)\nmodelsummary(panels, shape = \"cbind\") automatically adds column-spanning labels when panels is a named nested list of models.\n\nconfig_modelsummary() gets a startup_message argument to silence the startup message persistently.\nImproved documentation and vignettes, providing clearer instructions and examples.\nUpdated tests and snapshots to ensure reliability and consistency across changes.\n\nBug fixes:\n\nFixed Issue #399: datasummary_balance() siunitx formatting.\nFixed Issue #782: Useless warning in some modelplot() calls. Thanks to @iago-pssjd for the report and @florence-laflamme for the fix.\nAddressed various bugs and made optimizations for better performance and user experience.\n\n\n\n\n\ntinytable supports histograms in datasummary_skim()\nconfig_modelsummary() supports tinytable factory.\n\n\n\n\n\nSupport the tinytable package as an output format (“factory”): https://vincentarelbundock.github.io/tinytable/\nQuarto: md output format is recognized.\noptions(modelsummary_factory_default) is respected, even in qmd-&gt;md documents.\n\nBugs:\n\nSome omitted coefficients with I() operator in formulas. Issue #693.\n\n\n\n\nMisc:\n\nDuplicate values in shape groups are removed automatically for cleaner labels.\n“Title” line no longer indented in markdown tables. Thanks to Ryan Briggs for report #671.\n\nBugs:\n\nSmall p values were not displayed properly in HTML output using kableExtra. Issue #669.\n\n\n\n\nNew:\n\nMinimal support for Typst output, with auto-detection in Quarto documents.\nstrip argument in dvnames.\ns.value statistic is now available whenever p.value is available. See Greenland (2019).\ndatasummary_skim() now includes histograms in gt tables.\n\nBugs:\n\nGOF term names get escaped in LaTeX. Thanks to @shreyasgm for reviving Issue #546.\nConflict with furrr generated errors for some models. Thanks to @sammo3182 for Issue #647.\n\n\n\n\nNew:\n\nfmt_sci() can now be used in the fmt argument for rounding with scientific notation.\n\nBugs:\n\nGroup separators respect add_rows with shape=\"rbind\". Thanks to @lrose1 for Report #626.\nBad column with horizontal models in shape and grouped estimates. Thanks to @iago-pssjd for Report #631.\ncoef_rename=TRUE bug with grouped estimates. Thanks to @iago-pssjd for Report #631.\nUpstream issue #881 in parameters meant that vcov was no longer used for confidence intervals.\n\n\n\n\n\nBuilt-in support for markdown tables.\nPackage no longer depends on kableExtra. Recommends an additional install for other formats.\nPersistent configuration of default output format: config_modelsummary(factory_default = \"gt\")\nshape = \"rcollapse\" and shape = \"rbind\"\nglance_custom() can drop GOF by assigning NA: https://stackoverflow.com/questions/75215355/assigning-different-coefficient-names-and-goodness-of-fit-statistics-to-differen\nWhen a statistic is not available, modelsummary prints an empty cell instead of returning an error.\n“\\label{tab:something}” works in title even when escape=TRUE\nMultiple fixest_multi objects supported.\noptions(modelsummary_future = FALSE) disables future parallelism.\n\nBug fixes:\n\nstatistic=NULL is now respected when shape=\"rbind\". Thanks to Panos Mavros for report #620.\nget_estimates() supports vcov string shortcuts and formulas. Thanks to @ethans-carl for report #605.\nQuarto and Rmarkdown documents include situnix in header automatically for decimal alignement with align=\"ddd\"\nescape is now respected by modelsummary with shape=\"rbind\". Thanks to @chickymonkeys for report #622.\n\n\n\n\nBreaking change:\n\nThe default column label style in modelsummary() has changed from “Model 1” to “(1)”. The benefits are: labels are no longer in English by default; use less horizontal space; eliminate the “Model” redundancy. Unfortunately, this could break code in some edge cases where users rely on column names to manipulate tables. The old behavior can be restored by calling: options(modelsummary_model_labels=\"model\")\n\nNew features:\n\nshape=\"rbind\" to stack multiple regression tables and create “panels” with labelled groups of models.\nfmt: new helper functions for different formatting styles\n\nfmt = fmt_decimal(2): decimal digits\nfmt = fmt_decimal(digits = 2, pdigits = 4): decimal digits with p value-specific setting\nfmt = fmt_sprintf(\"%.3f\"): sprintf() decimal\nfmt = fmt_sprintf(\"%.3e\"): sprintf() scientific\nfmt = fmt_significant(3): significant digits\nfmt = fmt_statistic(\"estimate\" = 2, \"std.error\" = 3): statistic-specific formatting\nfmt = fmt_term(\"(Intercept)\" = 2, \"hp\" = 3): term-specific formatting\nfmt = fmt_identity(): raw values\n\nNew styles for default column labels in modelsummary, such as Roman Numerals or letters in parentheses.\n\nSet the style with a global option: options(modelsummary_model_labels = \"roman\")\nSupported styles: “model”, “arabic”, “letters”, “roman”, “(arabic)”, “(letters)”, “(roman)””\n\nmodelplot(draw = FALSE) now returns a p.value column. This allows conditional aesthetics (see the modelplot vignette).\nBetter integration with the marginaleffects package.\n\nBugs:\n\nSome fixest models returns useless “group.x” and “group.y” columns. Isse #591. Thanks to Adam Altmejd for the report.\n\n\n\n\nBreaking change:\n\nWith the shape and output=\"dataframe\" arguments, there always used to be a group column. Now, this column has the same name as the variable in the shape formula (“response”, “component”, etc.).\n\nNew features:\n\nshape can include multiple groups.\ncoef_rename can be an unnamed vector of length equal to the number of terms in the final table, obtained after coef_map and coef_omit are applied and models are merged.\ncoef_omit accepts numeric indices. Positive values: coefficients to omit. Negative values: coefficients to keep.\ndatasummary_skim: Increased maximum number of variables to 250.\nQuarto notebooks compile to Word and Markdown automatically.\n\nBug fixes:\n\nOrder of notes preserved in some output format (Issue #577)\n\n\n\n\nBreaking change:\n\nRequires siunitx version 3.0.25 LaTeX package.\nThe title argument now respects the escape argument for all kableExtra output formats. This can break tables in which users manually escaped titles.\n\nNew features:\n\n“d” is accepted for decimal-alignment in the align argument for all output formats. modelsummary(mod, align = \"ld\")\nNew update_modelsummary() function makes it easy to install the dev versions of modelsummary and its dependencies (mostly useful for Vincent and people who report bugs).\nRounding: display at least one significant digit by default.\nAutomatic renaming of haven labels in modelsummary(), datasummary(), datasummary_skim()\nAllow output = \"filename.csv\"\nAllow output = \"filename.xlsx\"\nadd_columns argument supported in modelsummary()\ndatasummary_balance supports the stars argument.\nAllow stars and confidence intervals with align = \"d\" column.\n\nBug fixes:\n\nIn some locales, the HTML minus sign created problems in the output. We only use it in “known” locales.\nMany minor bug fixes\n\n\n\n\n\nMinor release to fix CRAN failure\n\n\n\n\n\nshape argument accepts interactions with the colon “:” character. This combines two columns into one, which can be useful to display terms and group names in a single column.\nParallelization using parallel::mclapply. See ?modelsummary\nmodelsummary no longer computes confidence intervals when not necessary, which can save some time. Also see: conf_level=NULL\nAdded log likelihood to GOF for lm and glm models.\nRemoved extraneous warnings\nBug fixes\n\n\n\n\nThis first major release accompanies the publication of an article in the Journal of Statistical Software:\nArel-Bundock, Vincent (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. doi:10.18637/jss.v103.i01 https://doi.org/10.18637/jss.v103.i01.’\nIf you like modelsummary, please cite the JSS article and tell your friends about it.\nMinor changes:\n\ngof_map=\"all\" includes all available statistics. gof_map=\"none\" excludes all statistics.\nBug fixes\n\n\n\n\n\nBetter printout for term names in mixed-effects models\n{brms} and {stanreg} models now extracted with diagnostic=NULL and test=NULL by default for speed.\n\n\n\n\nBreaking changes:\n\nmodelsummary_wide is no longer available. Use the shape argument of modelsummary instead.\nmodelsummary now uses the easystats packages (performance and parameters) to extract estimates and goodness-of-fit statistics instead of broom. This can be reverted by setting a global option: options(modelsummary_get=\"broom\"). This change aims to (1) increase consistency across models, (2) improve the developers’ ability to push bug fixes upstream when necessary, and (3) improve support for mixed effects, bayesian, and GAM models. The two main drawbacks are: (a) The set of printed statistics may be slightly different from previous versions of modelsummary (b) The group identifiers used in the shape formula will also be different for certain models (e.g., in nnet::multinom, y.level becomes response).\n\nNew features:\n\nThe shape argument accepts a formula and can reshape information in myriad ways. Deprecates the group argument. Examples:\n\n~ statistic: statistics are shown horizontally in distinct columns.\nmodel ~ term: models in rows and terms in columns.\nterm + y.level + statistic ~ model: grouped coefficients for multivariate outcome in nnet::multinom\ny.level ~ model: partial match is the same as the previous formula\n\nFormat distinct statistics differently by passing a named list to fmt:\n\nmodelsummary(mod, fmt = list(estimate = 2, std.error = 1, rmse = 4))\n\nUse glue to apply functions to numeric values by setting fmt = NULL. Example:\n\nmodelsummary(model, fmt = NULL, estimate = \"{log(estimate)}\")\n\nUpdate for breaking changes after fixest 0.10.4\n\nBug fixes:\n\ngroup_map rename issue\nResidual standard error mistakenly labelled “RMSE” in lm models.\ndatasummary_skim output to jpg should now works\nescape fixes\n\n\n\n\n\nNew exponentiate argument for modelsummary() and modelplot()\ngof_map accepts a vector such as c(\"rmse\", \"nobs\", \"r.squared\")\nDrop rlang dependency\nBug fixes\n\n\n\n\ndatasummary_balance:\n\nAccepts ~ 1 as a formula to summarize all data.\n\nMisc:\n\ndocumentation improvements\nRMSE included by default in models of class lm\n\n\n\n\nmodelsummary:\n\nvcov strings like HC1 and Robust are now case-insensitive\ngof_map now accepts a data.frame or tibble with a fmt list-column which includes functions (see Examples in docs)\nR2 is no longer computed by default for bayesian and mixed effects models. An informative one-time warning is printed about the metrics argument.\n\ndatasummary_skim:\n\nHistograms now work in Jupyter\nBugfix: harmless error message is no longer printed\n\nkableExtra factory:\n\nThe col.names argument can now be passed to kableExtra::kbl through the … ellipsis.\n\nMisc:\n\nMany small improvements to the vignettes and docs\noutput = \"github_document\" is now supported\n\n\n\n\n\nBug fix: siunitx and rounding NA\n\n\n\n\nmodelsummary:\n\nF statistic takes into account vcov argument\nSupport group = group ~ model + term\n\ndatasummary_balance:\n\nWeighted means and standard deviations are now supported. Counts and percentages are not, but raise a warning.\n\nMisc:\n\nBugfix: rounding in LaTeX w/ siunitx and NaN entries.\noutput=‘jupyter’ no longer prints an extraneous TRUE to the notebook\n\n\n\n\nmodelsummary:\n\nImproved vcov argument handling for fixest models (#357 by @grantmcdermott)\nFix display of fixest::i() variables and interactions (#361 by @grantmcdermott)\nConsistent display of clustered SEs (#356, #363 and #366 by @grantmcdermott)\n\ndatasummary_correlation:\n\nadd_rows and add_columns arguments are now available here.\n\nMisc:\n\nGlobal options for output factories are renamed: modelsummary_factory_default, modelsummary_factory_html, etc.\nHot fix for change in R-devel behavior or intersect\n\nBug fixes:\n\ndatasummary_balance: escape variable names when escape=TRUE\nBlogdown LaTeX dependency bug when output is HTML\n\n\n\n\nBreaking change:\n\nSupport for dcolumn for dot-aligned columns is deprecated. Use “d” in the align argument instead.\n\nOther changes:\n\nLaTeX output: Numeric entries are wrapped in the \\num{} function from the siunitx package by default. This produces much nicer formatting. This can be disabled with a global option. See ?modelsummary\nThe align argument accepts a “d” column for dot-alignment using the siunitx LaTeX package: align=\"ldd\".\nHTML tables display proper minus signs.\nNew escape argument in most table-building functions.\nLaTeX output accepts the threeparttable=TRUE argument through ...\nNo more dependency on tidyr\n\nmodelsummary:\n\ngroup: The order of terms in the formula determines the order of rows/columns\n\nmodelsummary_wide:\n\nNote: This function will eventually be deprecated\nBugfix with statistic=NULL.\n\nmodelplot:\n\nPreserves order of models in the user-supplied list\n\ndatasummary_crosstab:\n\nstatistic=NULL produces a very basic crosstab\n\ndatasummary_crosstab:\n\nDefault alignment “lrrrrr” consistent with other datasummary_* functions\n\n\n\n\nmodelsummary:\n\nDisable stars footnote with options(\"modelsummary_stars_note\" = FALSE)\nlongtable=TRUE works for LaTeX output\nInteractions with “:” are no longer converted to “x” when coef_map or coef_rename are used.\ngroup = model ~ term + group is now supported.\n\ndatasummary_skim:\n\ndatasummary_skim(\"categorical\") keeps NA by default. Users can convert variables to factors before calling datasummary_skim to exclude NA.\n\nOther:\n\nImproved warnings for bad calls: modelsummary(model1, model2)\ngt titles use the new caption argument in the gt 0.3.0 function\nBug fix: Overaggressive tests for glue strings prevented functions inside {}\n\n\n\n\nBreaking change:\n\nThe default significance markers stars=TRUE have been updated to be consistent with the default output from base R (e.g., in summary.lm). The new significance thresholds are: “+” p &lt; 0.1, “” p &lt; 0.05, ”” p &lt; 0.01, ”” p &lt; 0.001\n\ndatasummary_crosstab:\n\nNew function to produce cross-tabulations\n\ndatasummary:\n\nN is smart enough to return either the number of elements in a subset or the number of non-missing observations in a variable\n\ndatasummary_balance:\n\nKeeps NAs in factor variables by default. Users can convert their variables with the factor() function to omit NAs automatically.\n\nmodelsummary:\n\nthemes can be set using global options (experimental)\nnew vcov options: “bootstrap”, “HAC”, “NeweyWest”, “Andrews”, “panel-corrected”, “weave”, “outer-product”\nA valid get_gof (glance) is now optional.\n… is pushed through to sandwich, which allows things like: modelsummary(model, vcov = \"bootstrap\", R = 1000, cluster = \"firm\")\n\nOther:\n\nJupyter notebook support via output=\"jupyter\"\nBug fixes\n\n\n\n\nmodelsummary:\n\nnew arguments for modelsummary: group and group_map for grouped parameters (e.g., outcome levels in multinomial logit or components of gamlss model).\ndvnames() makes it easy to get dependent variable column titles (thanks to @NickCH-K)\noutput=\"modelsummary_list\" to save a lightweight list-based representation of the table which can be saved and fed to modelsummary once more to get a full table.\nvcov adds a row to note the type of standard errors.\nmodelsummary accepts a single model with multiple vcovs.\nget_gof forwards … to model_performance\ncoef_map accepts unnamed vectors for easy subsetting\nfixest::fixest_multi support\noptions(modelsummary_get) to set the order of extraction functions to use under the hood (broom vs. easystats vs. all)\nmetrics argument of performance::model_performance is available via modelsummary’s … ellipsis to limit the GOF statistics in Bayesian models.\nusers can omit the stars legend note by using glue strings: estimate=\"{estimate}{stars}\"\noutput=“html” can use gt by setting options(modelsummary_factory_html=\"gt\")\n\ndatasummary_correlation:\n\npasses ... forward\nnew function: datasummary_correlation_format\ndatasummary_correlation’s method argument accepts functions and “pearspear” (thanks to @joachim-gassen)\n\ndatasummary:\n\ndatasummary functions and rounding accept …, big.mark, etc.\n\ndatasummary_skim:\n\nnow works with haven_labeled numeric\nfaster tables with bayesian models.\n\nBug fixes and lints\n\n\n\n\nnew output format: latex_tabular\ntidy_custom allows partial term matches\nmodelsummary(coef_rename) accepts functions\nnew function coef_rename for use in modelsummary(coef_rename=coef_rename)\nmodelplot accepts add_rows to add reference categories\ninformative error message when estimate or statistic is not available\nbug fixes\n\n\n\n\n\nstatistic_override becomes vcov\nvcov accepts shortcuts: “robust”, “stata”, “HC0”, etc.\nvcov accepts formulas for clustered SEs: ~group\nmodelsummary_wide has a new “stacking” argument\nhtml horizontal rule to separate estimates form gof\ngof_map accepts list of lists. only needs 3 columns.\nsupport officedown Rmd\nestimate accepts a vector for per model estimates\noptions(modelsummary_default) can be markdown, html, latex\nbug: passing arguments through …\nbug: stars and rounding\n\n\n\n\n\nglue format for estimate and statistic\neasystats support for model info extraction\ndeprecate statistic_vertical\ndeprecate extract_models. Use modelsummary(output=“dataframe”) instead.\nmodelplot pushes … through to modelsummary(output=“dataframe”)\ndatasummary_skim(type=“dataset”)\ngof_map omits by default\ndatasummary_balance uses row percentages\nstatistic_override does not require a list\nstatistic_override accepts a single model\nN function for well formatted N in datasummary\nBug fixes\n\n\n\n\n\nnew function: modelsummary_wide\ncoef_omit and gof_omit use grepl(perl=TRUE)\nfmt accepts integer, string or function and respects options(OutDec=“,”)\nalign argument for modelsummary\nalign is more liberal to accept dcolumn alignment\nglance_custom methods for lfe and fixest\nbug fixes\n\n\n\n\n\nnew argument: coef_rename\nnew function: datasummary_df\npreserve term order in modelsummary\nrefactor datasummary_balance\ndatasummary_skim uses svg histograms instead of unicode\nremoved 5 dependencies\npass … to kableExtra::kbl for more customization\ntest improvements\ninternal code style\nbug fixes\n\n\n\n\n\nbug fixes\n\n\n\n\n\ndefault HTML output factory is now kableExtra\ninteraction “:” gsubbed by “0d7”\ndependencies: removed 1 depends, 3 imports, and 3 suggests\nword_document knitr works out-of-the-box\nbug fixes\n\n\n\n\n\nglance_custom.fixest ships with modelsummary\n\n\n\n\n\ndatasummary\ndatasummary_skim\ndatasummary_balance\ndatasummary_correlation\nmodelplot\nallow duplicate model names\nbug: can’t use coef_map with multiple statistics (thanks @sbw78)\nbug: wrong number of stars w/ statistic=‘p.value’ (thanks @torfason)\noutput=‘data.frame’. extract is no longer documented.\n\n\n\n\n\nadd_rows now accepts a data.frame with “position” and “section” columns\nadd_rows_location is deprecated\nbug in sanity_output prevented overwriting files\n\n\n\n\n\nhuxtable support\nflextable support\nestimate argument\nfixest tidiers\nwebsite and vignette improvements\ngof_map additions\nglance_custom\ntidy_custom\n\n\n\n\n\nOut-of-the-box Rmarkdown compilation to HTML, PDF, RTF\nkableExtra output format for LaTeX and Markdown\nSupport for threeparttable, colors, and many other LaTeX options\nDeprecated arguments: filename, subtitle\nDeprecated functions: clean_latex, knit_latex\npkgdown website and doc improvements\nmitools tidiers\nNew tests\n\n\n\n\n\nConvenience function to render markdown in row/column labels\nbug: breakage when all GOF were omitted\nClean up manual with @keywords internal\nbug: tidyr import\n\n\n\n\n\ngt is now available on CRAN\nnew latex_env argument for knit_latex and clean_latex\nbug when all gof omitted\nbug in statistic_override with functions\nbug caused by upstream changes in tab_style\nbug caused by upstream changes in filename=‘rtf’\nAllow multiple rows of uncertainty estimates per coefficient\nPreserve add_rows order\nDisplay uncertainty estimates next to the coefficient with statistic_vertical = FALSE\nBetter clean_latex function\nCan display R2 and confidence intervals for mice-imputed lm-models\nInternal functions have @keywords internal to avoid inclusion in docs\nStatistic override accepts pre-formatted character vectors\n\n\n\n\n\nInitial release (gt still needs to be installed from github)",
+    "text": "default HTML output factory is now kableExtra\ninteraction “:” gsubbed by “0d7”\ndependencies: removed 1 depends, 3 imports, and 3 suggests\nword_document knitr works out-of-the-box\nbug fixes",
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-    "text": "New features:\n\nmodelsummary() gets a gof_function argument which accepts functions to extract custom information from models.\nflextable: Support spanning column headers\n\ndatasummary_balance():\n\nformula can now include variables on the left-hand side to indicate the subset of columns to summarize: datasummary_balance(mpg + hp ~ am, data = mtcars) Thanks to @etiennebacher for feature request #751.\n\nBugs:\n\nescape argument not respected in datasummary_df(). Thanks to @adamaltmejd for report #740\ndatasummary_correlation() supports data.table. Thanks to volatilehead on Twitter for report #737.\nAccepts named estimate argument when using shape and statistics are horizontal. Thanks to @iago-pssjd for report #745.\nLabelled values but no label for variable broke datasummary(). Thanks to @marklhc for report #752.\ncoef_map does not work when there is a group. Thanks to @mccarthy-m-g for report #757.\nkableExtra: fix spanning column headers when using the shape argument.\nMultiple footnotes and line breaks in footnotes are now allowed in tinytable output. Thanks to",
+    "text": "glance_custom.fixest ships with modelsummary",
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-    "text": "MAJOR BREAKING CHANGE: The default output format is now tinytable instead of kableExtra. Learn more about tinytable here:\nhttps://vincentarelbundock.github.io/tinytable/\nTo revert to the previous behavior persistently, users can call:\nlibrary(modelsummary) config_modelsummary(factory_default = “kableExtra”)\nOther breaking changes:\n\nThe statistic_override argument was replaced by vcov over 1 year ago, with appropriate deprecation warnings. It is now fully removed.\nThe group argument was replaced by shape several releases ago. It is now fully removed.\ndatasummary_skim()\n\nhistograms are only available with the tinytable backend. This allows a lot of code simplification and more customization.\nThe order of arguments type and output is switched for consistency with other functions.\nhistogram argument is deprecated.\n\n\nNew features:\n\ndatasummary_skim():\n\nNew type=\"all\" by default to display both numeric and categorical variables in a single table with distinct panels. This feature is only available with the tinytable backend (default).\nby argument allows summarizing numeric variables by group.\nfun_numeric argument accepts a list of functions to control the summary columns.\n\nmodelsummary():\n\nstatistic and estimate can be specified as named vectors to control the names of statistics when displayed in different columns using the shape argument. (Thanks to @mps9506 for bug report #722)\nmodelsummary(panels, shape = \"cbind\") automatically adds column-spanning labels when panels is a named nested list of models.\n\nconfig_modelsummary() gets a startup_message argument to silence the startup message persistently.\nImproved documentation and vignettes, providing clearer instructions and examples.\nUpdated tests and snapshots to ensure reliability and consistency across changes.\n\nBug fixes:\n\nFixed Issue #399: datasummary_balance() siunitx formatting.\nFixed Issue #782: Useless warning in some modelplot() calls. Thanks to @iago-pssjd for the report and @florence-laflamme for the fix.\nAddressed various bugs and made optimizations for better performance and user experience.",
+    "text": "datasummary\ndatasummary_skim\ndatasummary_balance\ndatasummary_correlation\nmodelplot\nallow duplicate model names\nbug: can’t use coef_map with multiple statistics (thanks @sbw78)\nbug: wrong number of stars w/ statistic=‘p.value’ (thanks @torfason)\noutput=‘data.frame’. extract is no longer documented.",
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-    "text": "tinytable supports histograms in datasummary_skim()\nconfig_modelsummary() supports tinytable factory.",
+    "text": "add_rows now accepts a data.frame with “position” and “section” columns\nadd_rows_location is deprecated\nbug in sanity_output prevented overwriting files",
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-    "text": "Support the tinytable package as an output format (“factory”): https://vincentarelbundock.github.io/tinytable/\nQuarto: md output format is recognized.\noptions(modelsummary_factory_default) is respected, even in qmd-&gt;md documents.\n\nBugs:\n\nSome omitted coefficients with I() operator in formulas. Issue #693.",
+    "text": "huxtable support\nflextable support\nestimate argument\nfixest tidiers\nwebsite and vignette improvements\ngof_map additions\nglance_custom\ntidy_custom",
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-    "text": "Misc:\n\nDuplicate values in shape groups are removed automatically for cleaner labels.\n“Title” line no longer indented in markdown tables. Thanks to Ryan Briggs for report #671.\n\nBugs:\n\nSmall p values were not displayed properly in HTML output using kableExtra. Issue #669.",
+    "text": "Out-of-the-box Rmarkdown compilation to HTML, PDF, RTF\nkableExtra output format for LaTeX and Markdown\nSupport for threeparttable, colors, and many other LaTeX options\nDeprecated arguments: filename, subtitle\nDeprecated functions: clean_latex, knit_latex\npkgdown website and doc improvements\nmitools tidiers\nNew tests",
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-    "text": "New:\n\nMinimal support for Typst output, with auto-detection in Quarto documents.\nstrip argument in dvnames.\ns.value statistic is now available whenever p.value is available. See Greenland (2019).\ndatasummary_skim() now includes histograms in gt tables.\n\nBugs:\n\nGOF term names get escaped in LaTeX. Thanks to @shreyasgm for reviving Issue #546.\nConflict with furrr generated errors for some models. Thanks to @sammo3182 for Issue #647.",
+    "text": "Convenience function to render markdown in row/column labels\nbug: breakage when all GOF were omitted\nClean up manual with @keywords internal\nbug: tidyr import",
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-    "text": "New:\n\nfmt_sci() can now be used in the fmt argument for rounding with scientific notation.\n\nBugs:\n\nGroup separators respect add_rows with shape=\"rbind\". Thanks to @lrose1 for Report #626.\nBad column with horizontal models in shape and grouped estimates. Thanks to @iago-pssjd for Report #631.\ncoef_rename=TRUE bug with grouped estimates. Thanks to @iago-pssjd for Report #631.\nUpstream issue #881 in parameters meant that vcov was no longer used for confidence intervals.",
+    "text": "gt is now available on CRAN\nnew latex_env argument for knit_latex and clean_latex\nbug when all gof omitted\nbug in statistic_override with functions\nbug caused by upstream changes in tab_style\nbug caused by upstream changes in filename=‘rtf’\nAllow multiple rows of uncertainty estimates per coefficient\nPreserve add_rows order\nDisplay uncertainty estimates next to the coefficient with statistic_vertical = FALSE\nBetter clean_latex function\nCan display R2 and confidence intervals for mice-imputed lm-models\nInternal functions have @keywords internal to avoid inclusion in docs\nStatistic override accepts pre-formatted character vectors",
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-    "text": "Built-in support for markdown tables.\nPackage no longer depends on kableExtra. Recommends an additional install for other formats.\nPersistent configuration of default output format: config_modelsummary(factory_default = \"gt\")\nshape = \"rcollapse\" and shape = \"rbind\"\nglance_custom() can drop GOF by assigning NA: https://stackoverflow.com/questions/75215355/assigning-different-coefficient-names-and-goodness-of-fit-statistics-to-differen\nWhen a statistic is not available, modelsummary prints an empty cell instead of returning an error.\n“\\label{tab:something}” works in title even when escape=TRUE\nMultiple fixest_multi objects supported.\noptions(modelsummary_future = FALSE) disables future parallelism.\n\nBug fixes:\n\nstatistic=NULL is now respected when shape=\"rbind\". Thanks to Panos Mavros for report #620.\nget_estimates() supports vcov string shortcuts and formulas. Thanks to @ethans-carl for report #605.\nQuarto and Rmarkdown documents include situnix in header automatically for decimal alignement with align=\"ddd\"\nescape is now respected by modelsummary with shape=\"rbind\". Thanks to @chickymonkeys for report #622.",
+    "text": "Initial release (gt still needs to be installed from github)",
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-    "text": "Breaking change:\n\nThe default column label style in modelsummary() has changed from “Model 1” to “(1)”. The benefits are: labels are no longer in English by default; use less horizontal space; eliminate the “Model” redundancy. Unfortunately, this could break code in some edge cases where users rely on column names to manipulate tables. The old behavior can be restored by calling: options(modelsummary_model_labels=\"model\")\n\nNew features:\n\nshape=\"rbind\" to stack multiple regression tables and create “panels” with labelled groups of models.\nfmt: new helper functions for different formatting styles\n\nfmt = fmt_decimal(2): decimal digits\nfmt = fmt_decimal(digits = 2, pdigits = 4): decimal digits with p value-specific setting\nfmt = fmt_sprintf(\"%.3f\"): sprintf() decimal\nfmt = fmt_sprintf(\"%.3e\"): sprintf() scientific\nfmt = fmt_significant(3): significant digits\nfmt = fmt_statistic(\"estimate\" = 2, \"std.error\" = 3): statistic-specific formatting\nfmt = fmt_term(\"(Intercept)\" = 2, \"hp\" = 3): term-specific formatting\nfmt = fmt_identity(): raw values\n\nNew styles for default column labels in modelsummary, such as Roman Numerals or letters in parentheses.\n\nSet the style with a global option: options(modelsummary_model_labels = \"roman\")\nSupported styles: “model”, “arabic”, “letters”, “roman”, “(arabic)”, “(letters)”, “(roman)””\n\nmodelplot(draw = FALSE) now returns a p.value column. This allows conditional aesthetics (see the modelplot vignette).\nBetter integration with the marginaleffects package.\n\nBugs:\n\nSome fixest models returns useless “group.x” and “group.y” columns. Isse #591. Thanks to Adam Altmejd for the report.",
+    "text": "Version 3, 29 June 2007\nCopyright © 2007 Free Software Foundation, Inc. &lt;http://fsf.org/&gt;\nEveryone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.\n\n\n\nThe GNU General Public License is a free, copyleft license for software and other kinds of works.\nThe licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.\nWhen we speak of free software, we are referring to freedom, not price. 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Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate.\n\n\n\nYou may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways:\n\na) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by the Corresponding Source fixed on a durable physical medium customarily used for software interchange.\nb) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge.\nc) Convey individual copies of the object code with a copy of the written offer to provide the Corresponding Source. This alternative is allowed only occasionally and noncommercially, and only if you received the object code with such an offer, in accord with subsection 6b.\nd) Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements.\ne) Convey the object code using peer-to-peer transmission, provided you inform other peers where the object code and Corresponding Source of the work are being offered to the general public at no charge under subsection 6d.\n\nA separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work.\nA “User Product” is either (1) a “consumer product”, which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, “normally used” refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product.\n“Installation Information” for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made.\nIf you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM).\nThe requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.\nCorresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.\n\n\n\n“Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.\nWhen you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.\nNotwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:\n\na) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or\nb) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or\nc) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or\nd) Limiting the use for publicity purposes of names of licensors or authors of the material; or\ne) Declining to grant rights under trademark law for use of some trade names, trademarks, or service marks; or\nf) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors.\n\nAll other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.\nIf you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.\nAdditional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.\n\n\n\nYou may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).\nHowever, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.\nMoreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.\nTermination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.\n\n\n\nYou are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.\n\n\n\nEach time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.\nAn “entity transaction” is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party’s predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.\nYou may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.\n\n\n\nA “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor’s “contributor version”.\nA contributor’s “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.\nEach contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor’s essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.\nIn the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.\nIf you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient’s use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.\nIf, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.\nA patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.\nNothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.\n\n\n\nIf conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.\n\n\n\nNotwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.\n\n\n\nThe Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.\nEach version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.\nIf the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.\nLater license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.\n\n\n\nTHERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n\n\nIN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\n\n\nIf the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.\nEND OF TERMS AND CONDITIONS\n\n\n\n\nIf you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.\nTo do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.\n&lt;one line to give the program's name and a brief idea of what it does.&gt;\nCopyright (C) 2019 Vincent Arel-Bundock\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see &lt;http://www.gnu.org/licenses/&gt;.\nAlso add information on how to contact you by electronic and paper mail.\nIf the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:\nmodelsummary Copyright (C) 2019 Vincent Arel-Bundock\nThis program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.\nThis is free software, and you are welcome to redistribute it\nunder certain conditions; type 'show c' for details.\nThe hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.\nYou should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see &lt;http://www.gnu.org/licenses/&gt;.\nThe GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read &lt;http://www.gnu.org/philosophy/why-not-lgpl.html&gt;.",
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-    "text": "Breaking change:\n\nWith the shape and output=\"dataframe\" arguments, there always used to be a group column. Now, this column has the same name as the variable in the shape formula (“response”, “component”, etc.).\n\nNew features:\n\nshape can include multiple groups.\ncoef_rename can be an unnamed vector of length equal to the number of terms in the final table, obtained after coef_map and coef_omit are applied and models are merged.\ncoef_omit accepts numeric indices. Positive values: coefficients to omit. Negative values: coefficients to keep.\ndatasummary_skim: Increased maximum number of variables to 250.\nQuarto notebooks compile to Word and Markdown automatically.\n\nBug fixes:\n\nOrder of notes preserved in some output format (Issue #577)",
+    "text": "Version 3, 29 June 2007\nCopyright © 2007 Free Software Foundation, Inc. &lt;http://fsf.org/&gt;\nEveryone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.",
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-    "text": "Breaking change:\n\nRequires siunitx version 3.0.25 LaTeX package.\nThe title argument now respects the escape argument for all kableExtra output formats. This can break tables in which users manually escaped titles.\n\nNew features:\n\n“d” is accepted for decimal-alignment in the align argument for all output formats. modelsummary(mod, align = \"ld\")\nNew update_modelsummary() function makes it easy to install the dev versions of modelsummary and its dependencies (mostly useful for Vincent and people who report bugs).\nRounding: display at least one significant digit by default.\nAutomatic renaming of haven labels in modelsummary(), datasummary(), datasummary_skim()\nAllow output = \"filename.csv\"\nAllow output = \"filename.xlsx\"\nadd_columns argument supported in modelsummary()\ndatasummary_balance supports the stars argument.\nAllow stars and confidence intervals with align = \"d\" column.\n\nBug fixes:\n\nIn some locales, the HTML minus sign created problems in the output. We only use it in “known” locales.\nMany minor bug fixes",
+    "text": "The GNU General Public License is a free, copyleft license for software and other kinds of works.\nThe licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.\nWhen we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.\nTo protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.\nFor example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.\nDevelopers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.\nFor the developers’ and authors’ protection, the GPL clearly explains that there is no warranty for this free software. For both users’ and authors’ sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.\nSome devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users’ freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.\nFinally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.\nThe precise terms and conditions for copying, distribution and modification follow.",
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-    "text": "Minor release to fix CRAN failure",
+    "text": "“This License” refers to version 3 of the GNU General Public License.\n“Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.\n“The Program” refers to any copyrightable work licensed under this License. Each licensee is addressed as “you”. “Licensees” and “recipients” may be individuals or organizations.\nTo “modify” a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a “modified version” of the earlier work or a work “based on” the earlier work.\nA “covered work” means either the unmodified Program or a work based on the Program.\nTo “propagate” a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well.\nTo “convey” a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying.\nAn interactive user interface displays “Appropriate Legal Notices” to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion.\n\n\n\nThe “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work.\nA “Standard Interface” means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language.\nThe “System Libraries” of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A “Major Component”, in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it.\nThe “Corresponding Source” for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities. However, it does not include the work’s System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work. For example, Corresponding Source includes interface definition files associated with source files for the work, and the source code for shared libraries and dynamically linked subprograms that the work is specifically designed to require, such as by intimate data communication or control flow between those subprograms and other parts of the work.\nThe Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source.\nThe Corresponding Source for a work in source code form is that same work.\n\n\n\nAll rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law.\nYou may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you.\nConveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary.\n\n\n\nNo covered work shall be deemed part of an effective technological measure under any applicable law fulfilling obligations under article 11 of the WIPO copyright treaty adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention of such measures.\nWhen you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work’s users, your or third parties’ legal rights to forbid circumvention of technological measures.\n\n\n\nYou may convey verbatim copies of the Program’s source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program.\nYou may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee.\n\n\n\nYou may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions:\n\na) The work must carry prominent notices stating that you modified it, and giving a relevant date.\nb) The work must carry prominent notices stating that it is released under this License and any conditions added under section 7. 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-    "text": "shape argument accepts interactions with the colon “:” character. This combines two columns into one, which can be useful to display terms and group names in a single column.\nParallelization using parallel::mclapply. See ?modelsummary\nmodelsummary no longer computes confidence intervals when not necessary, which can save some time. Also see: conf_level=NULL\nAdded log likelihood to GOF for lm and glm models.\nRemoved extraneous warnings\nBug fixes",
+    "text": "If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.\nTo do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.\n&lt;one line to give the program's name and a brief idea of what it does.&gt;\nCopyright (C) 2019 Vincent Arel-Bundock\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see &lt;http://www.gnu.org/licenses/&gt;.\nAlso add information on how to contact you by electronic and paper mail.\nIf the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:\nmodelsummary Copyright (C) 2019 Vincent Arel-Bundock\nThis program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.\nThis is free software, and you are welcome to redistribute it\nunder certain conditions; type 'show c' for details.\nThe hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.\nYou should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see &lt;http://www.gnu.org/licenses/&gt;.\nThe GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read &lt;http://www.gnu.org/philosophy/why-not-lgpl.html&gt;.",
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-    "text": "This first major release accompanies the publication of an article in the Journal of Statistical Software:\nArel-Bundock, Vincent (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. doi:10.18637/jss.v103.i01 https://doi.org/10.18637/jss.v103.i01.’\nIf you like modelsummary, please cite the JSS article and tell your friends about it.\nMinor changes:\n\ngof_map=\"all\" includes all available statistics. gof_map=\"none\" excludes all statistics.\nBug fixes",
+    "text": "Dot-Whisker plot of coefficient estimates with confidence intervals. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nmodelplot Vignette.\n\n\nmodelplot(\n  models,\n  conf_level = 0.95,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = NULL,\n  vcov = NULL,\n  exponentiate = FALSE,\n  add_rows = NULL,\n  facet = FALSE,\n  draw = TRUE,\n  background = NULL,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nfacet\n\n\nTRUE or FALSE. When the ‘models’ argument includes several model objects, TRUE draws terms in separate facets, and FALSE draws terms side-by-side (dodged).\n\n\n\n\ndraw\n\n\nTRUE returns a ‘ggplot2’ object, FALSE returns the data.frame used to draw the plot.\n\n\n\n\nbackground\n\n\nA list of ‘ggplot2’ geoms to add to the background of the plot. This is especially useful to display annotations \"behind\" the ‘geom_pointrange’ that ‘modelplot’ draws.\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# single model\nmod &lt;- lm(hp ~ vs + drat, mtcars)\nmodelplot(mod)\n\n\n\n\n\n\n# omit terms with string matches or regexes\nmodelplot(mod, coef_omit = 'Interc')\n\n\n\n\n\n\n# rename, reorder and subset with 'coef_map'\ncm &lt;- c('vs' = 'V-shape engine',\n  'drat' = 'Rear axle ratio')\nmodelplot(mod, coef_map = cm)\n\n\n\n\n\n\n# several models\nmodels &lt;- list()\nmodels[['Small model']] &lt;- lm(hp ~ vs, mtcars)\nmodels[['Medium model']] &lt;- lm(hp ~ vs + factor(cyl), mtcars)\nmodels[['Large model']] &lt;- lm(hp ~ vs + drat + factor(cyl), mtcars)\nmodelplot(models)\n\n\n\n\n\n\n# add_rows: add an empty reference category\n\nmod &lt;- lm(hp ~ factor(cyl), mtcars)\n\nadd_rows = data.frame(\n  term = \"factory(cyl)4\",\n  model = \"(1)\",\n  estimate = NA)\nattr(add_rows, \"position\") = 3\nmodelplot(mod, add_rows = add_rows)\n\n\n\n\n\n\n# customize your plots with 'ggplot2' functions\nlibrary(ggplot2)\n\nmodelplot(models) +\n  scale_color_brewer(type = 'qual') +\n  theme_classic()\n\n\n\n\n\n\n# pass arguments to 'geom_pointrange' through the ... ellipsis\nmodelplot(mod, color = 'red', size = 1, fatten = .5)\n\n\n\n\n\n\n# add geoms to the background, behind geom_pointrange\nb &lt;- list(geom_vline(xintercept = 0, color = 'orange'),\n  annotate(\"rect\", alpha = .1,\n    xmin = -.5, xmax = .5,\n    ymin = -Inf, ymax = Inf),\n  geom_point(aes(y = term, x = estimate), alpha = .3,\n    size = 10, color = 'red', shape = 'square'))\nmodelplot(mod, background = b)\n\n\n\n\n\n\n# logistic regression example\ndf &lt;- as.data.frame(Titanic)\nmod_titanic &lt;- glm(\n  Survived ~ Class + Sex,\n  family = binomial,\n  weight = Freq,\n  data = df\n)\n\n# displaying odds ratio using a log scale\nmodelplot(mod_titanic, exponentiate = TRUE) +\n  scale_x_log10() +\n  xlab(\"Odds Ratios and 95% confidence intervals\")",
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-    "text": "Better printout for term names in mixed-effects models\n{brms} and {stanreg} models now extracted with diagnostic=NULL and test=NULL by default for speed.",
+    "text": "Dot-Whisker plot of coefficient estimates with confidence intervals. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nmodelplot Vignette.\n\n\nmodelplot(\n  models,\n  conf_level = 0.95,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = NULL,\n  vcov = NULL,\n  exponentiate = FALSE,\n  add_rows = NULL,\n  facet = FALSE,\n  draw = TRUE,\n  background = NULL,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nfacet\n\n\nTRUE or FALSE. When the ‘models’ argument includes several model objects, TRUE draws terms in separate facets, and FALSE draws terms side-by-side (dodged).\n\n\n\n\ndraw\n\n\nTRUE returns a ‘ggplot2’ object, FALSE returns the data.frame used to draw the plot.\n\n\n\n\nbackground\n\n\nA list of ‘ggplot2’ geoms to add to the background of the plot. This is especially useful to display annotations \"behind\" the ‘geom_pointrange’ that ‘modelplot’ draws.\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# single model\nmod &lt;- lm(hp ~ vs + drat, mtcars)\nmodelplot(mod)\n\n\n\n\n\n\n# omit terms with string matches or regexes\nmodelplot(mod, coef_omit = 'Interc')\n\n\n\n\n\n\n# rename, reorder and subset with 'coef_map'\ncm &lt;- c('vs' = 'V-shape engine',\n  'drat' = 'Rear axle ratio')\nmodelplot(mod, coef_map = cm)\n\n\n\n\n\n\n# several models\nmodels &lt;- list()\nmodels[['Small model']] &lt;- lm(hp ~ vs, mtcars)\nmodels[['Medium model']] &lt;- lm(hp ~ vs + factor(cyl), mtcars)\nmodels[['Large model']] &lt;- lm(hp ~ vs + drat + factor(cyl), mtcars)\nmodelplot(models)\n\n\n\n\n\n\n# add_rows: add an empty reference category\n\nmod &lt;- lm(hp ~ factor(cyl), mtcars)\n\nadd_rows = data.frame(\n  term = \"factory(cyl)4\",\n  model = \"(1)\",\n  estimate = NA)\nattr(add_rows, \"position\") = 3\nmodelplot(mod, add_rows = add_rows)\n\n\n\n\n\n\n# customize your plots with 'ggplot2' functions\nlibrary(ggplot2)\n\nmodelplot(models) +\n  scale_color_brewer(type = 'qual') +\n  theme_classic()\n\n\n\n\n\n\n# pass arguments to 'geom_pointrange' through the ... ellipsis\nmodelplot(mod, color = 'red', size = 1, fatten = .5)\n\n\n\n\n\n\n# add geoms to the background, behind geom_pointrange\nb &lt;- list(geom_vline(xintercept = 0, color = 'orange'),\n  annotate(\"rect\", alpha = .1,\n    xmin = -.5, xmax = .5,\n    ymin = -Inf, ymax = Inf),\n  geom_point(aes(y = term, x = estimate), alpha = .3,\n    size = 10, color = 'red', shape = 'square'))\nmodelplot(mod, background = b)\n\n\n\n\n\n\n# logistic regression example\ndf &lt;- as.data.frame(Titanic)\nmod_titanic &lt;- glm(\n  Survived ~ Class + Sex,\n  family = binomial,\n  weight = Freq,\n  data = df\n)\n\n# displaying odds ratio using a log scale\nmodelplot(mod_titanic, exponentiate = TRUE) +\n  scale_x_log10() +\n  xlab(\"Odds Ratios and 95% confidence intervals\")",
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-    "text": "Breaking changes:\n\nmodelsummary_wide is no longer available. Use the shape argument of modelsummary instead.\nmodelsummary now uses the easystats packages (performance and parameters) to extract estimates and goodness-of-fit statistics instead of broom. This can be reverted by setting a global option: options(modelsummary_get=\"broom\"). This change aims to (1) increase consistency across models, (2) improve the developers’ ability to push bug fixes upstream when necessary, and (3) improve support for mixed effects, bayesian, and GAM models. The two main drawbacks are: (a) The set of printed statistics may be slightly different from previous versions of modelsummary (b) The group identifiers used in the shape formula will also be different for certain models (e.g., in nnet::multinom, y.level becomes response).\n\nNew features:\n\nThe shape argument accepts a formula and can reshape information in myriad ways. Deprecates the group argument. Examples:\n\n~ statistic: statistics are shown horizontally in distinct columns.\nmodel ~ term: models in rows and terms in columns.\nterm + y.level + statistic ~ model: grouped coefficients for multivariate outcome in nnet::multinom\ny.level ~ model: partial match is the same as the previous formula\n\nFormat distinct statistics differently by passing a named list to fmt:\n\nmodelsummary(mod, fmt = list(estimate = 2, std.error = 1, rmse = 4))\n\nUse glue to apply functions to numeric values by setting fmt = NULL. Example:\n\nmodelsummary(model, fmt = NULL, estimate = \"{log(estimate)}\")\n\nUpdate for breaking changes after fixest 0.10.4\n\nBug fixes:\n\ngroup_map rename issue\nResidual standard error mistakenly labelled “RMSE” in lm models.\ndatasummary_skim output to jpg should now works\nescape fixes",
+    "text": "A unified approach to extract results from a wide variety of models. For some models get_gof attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_gof(model, gof_function = NULL, vcov_type = NULL, ...)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\nvcov_type\n\n\nstring vcov type to add at the bottom of the table\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.",
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-    "text": "New exponentiate argument for modelsummary() and modelplot()\ngof_map accepts a vector such as c(\"rmse\", \"nobs\", \"r.squared\")\nDrop rlang dependency\nBug fixes",
+    "text": "A unified approach to extract results from a wide variety of models. For some models get_gof attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_gof(model, gof_function = NULL, vcov_type = NULL, ...)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\nvcov_type\n\n\nstring vcov type to add at the bottom of the table\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.",
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-    "text": "datasummary_balance:\n\nAccepts ~ 1 as a formula to summarize all data.\n\nMisc:\n\ndocumentation improvements\nRMSE included by default in models of class lm",
+    "text": "Draw a table from a data.frame\n\n\n\ndatasummary_df(\n  data,\n  output = \"default\",\n  fmt = 2,\n  align = NULL,\n  hrule = NULL,\n  title = NULL,\n  notes = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nhrule\n\n\nposition of horizontal rules (integer vector)\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\n\n\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’"
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+    "text": "Draw a table from a data.frame\n\n\n\ndatasummary_df(\n  data,\n  output = \"default\",\n  fmt = 2,\n  align = NULL,\n  hrule = NULL,\n  title = NULL,\n  notes = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nhrule\n\n\nposition of horizontal rules (integer vector)\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\n\n\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’"
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-    "text": "modelsummary:\n\nvcov strings like HC1 and Robust are now case-insensitive\ngof_map now accepts a data.frame or tibble with a fmt list-column which includes functions (see Examples in docs)\nR2 is no longer computed by default for bayesian and mixed effects models. An informative one-time warning is printed about the metrics argument.\n\ndatasummary_skim:\n\nHistograms now work in Jupyter\nBugfix: harmless error message is no longer printed\n\nkableExtra factory:\n\nThe col.names argument can now be passed to kableExtra::kbl through the … ellipsis.\n\nMisc:\n\nMany small improvements to the vignettes and docs\noutput = \"github_document\" is now supported",
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+    "text": "A unified approach to extract results from a wide variety of models. For some models get_estimates attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_estimates(\n  model,\n  conf_level = 0.95,\n  vcov = NULL,\n  shape = NULL,\n  coef_rename = FALSE,\n  ...\n)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.",
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-    "text": "modelsummary:\n\nF statistic takes into account vcov argument\nSupport group = group ~ model + term\n\ndatasummary_balance:\n\nWeighted means and standard deviations are now supported. Counts and percentages are not, but raise a warning.\n\nMisc:\n\nBugfix: rounding in LaTeX w/ siunitx and NaN entries.\noutput=‘jupyter’ no longer prints an extraneous TRUE to the notebook",
+    "text": "A unified approach to extract results from a wide variety of models. For some models get_estimates attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_estimates(\n  model,\n  conf_level = 0.95,\n  vcov = NULL,\n  shape = NULL,\n  coef_rename = FALSE,\n  ...\n)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. 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-    "text": "modelsummary:\n\nImproved vcov argument handling for fixest models (#357 by @grantmcdermott)\nFix display of fixest::i() variables and interactions (#361 by @grantmcdermott)\nConsistent display of clustered SEs (#356, #363 and #366 by @grantmcdermott)\n\ndatasummary_correlation:\n\nadd_rows and add_columns arguments are now available here.\n\nMisc:\n\nGlobal options for output factories are renamed: modelsummary_factory_default, modelsummary_factory_html, etc.\nHot fix for change in R-devel behavior or intersect\n\nBug fixes:\n\ndatasummary_balance: escape variable names when escape=TRUE\nBlogdown LaTeX dependency bug when output is HTML",
+    "text": "By default, this data frame is passed to the ‘gof_map’ argument of the ‘modelsummary’ function. Users can modify this data frame to customize the list of statistics to display and their format. See example below.\n\ngof_map\n\n\ndata.frame with 4 columns of character data: raw, clean, fmt, omit\n\n\nlibrary(modelsummary)\n\nif (identical(Sys.getenv(\"pkgdown\"),  \"true\")) {\n\nlibrary(modelsummary)\nmod &lt;- lm(wt ~ drat, data = mtcars)\ngm &lt;- modelsummary::gof_map\ngm$omit[gm$raw == 'deviance'] &lt;- FALSE\ngm$fmt[gm$raw == 'r.squared'] &lt;- \"%.5f\"\nmodelsummary(mod, gof_map = gm)\n}",
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-    "text": "Breaking change:\n\nSupport for dcolumn for dot-aligned columns is deprecated. Use “d” in the align argument instead.\n\nOther changes:\n\nLaTeX output: Numeric entries are wrapped in the \\num{} function from the siunitx package by default. This produces much nicer formatting. This can be disabled with a global option. See ?modelsummary\nThe align argument accepts a “d” column for dot-alignment using the siunitx LaTeX package: align=\"ldd\".\nHTML tables display proper minus signs.\nNew escape argument in most table-building functions.\nLaTeX output accepts the threeparttable=TRUE argument through ...\nNo more dependency on tidyr\n\nmodelsummary:\n\ngroup: The order of terms in the formula determines the order of rows/columns\n\nmodelsummary_wide:\n\nNote: This function will eventually be deprecated\nBugfix with statistic=NULL.\n\nmodelplot:\n\nPreserves order of models in the user-supplied list\n\ndatasummary_crosstab:\n\nstatistic=NULL produces a very basic crosstab\n\ndatasummary_crosstab:\n\nDefault alignment “lrrrrr” consistent with other datasummary_* functions",
+    "text": "By default, this data frame is passed to the ‘gof_map’ argument of the ‘modelsummary’ function. Users can modify this data frame to customize the list of statistics to display and their format. See example below.\n\ngof_map\n\n\ndata.frame with 4 columns of character data: raw, clean, fmt, omit\n\n\nlibrary(modelsummary)\n\nif (identical(Sys.getenv(\"pkgdown\"),  \"true\")) {\n\nlibrary(modelsummary)\nmod &lt;- lm(wt ~ drat, data = mtcars)\ngm &lt;- modelsummary::gof_map\ngm$omit[gm$raw == 'deviance'] &lt;- FALSE\ngm$fmt[gm$raw == 'r.squared'] &lt;- \"%.5f\"\nmodelsummary(mod, gof_map = gm)\n}",
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-    "text": "modelsummary:\n\nDisable stars footnote with options(\"modelsummary_stars_note\" = FALSE)\nlongtable=TRUE works for LaTeX output\nInteractions with “:” are no longer converted to “x” when coef_map or coef_rename are used.\ngroup = model ~ term + group is now supported.\n\ndatasummary_skim:\n\ndatasummary_skim(\"categorical\") keeps NA by default. Users can convert variables to factors before calling datasummary_skim to exclude NA.\n\nOther:\n\nImproved warnings for bad calls: modelsummary(model1, model2)\ngt titles use the new caption argument in the gt 0.3.0 function\nBug fix: Overaggressive tests for glue strings prevented functions inside {}",
+    "text": "Mostly for internal use, but can be useful when users supply a function to the method argument of datasummary_correlation.\n\ndatasummary_correlation_format(\n  x,\n  fmt,\n  leading_zero = FALSE,\n  diagonal = NULL,\n  upper_triangle = NULL\n)\n\n\n\n\n\nx\n\n\nsquare numeric matrix\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nleading_zero\n\n\nboolean. If FALSE, leading zeros are removed\n\n\n\n\ndiagonal\n\n\ncharacter or NULL. If character, all elements of the diagonal are replaced by the same character (e.g., \"1\").\n\n\n\n\nupper_triangle\n\n\ncharacter or NULL. If character, all elements of the upper triangle are replaced by the same character (e.g., \"\" or \".\").\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_392nnoahgseq58zawjya\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .",
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-    "text": "Breaking change:\n\nThe default significance markers stars=TRUE have been updated to be consistent with the default output from base R (e.g., in summary.lm). The new significance thresholds are: “+” p &lt; 0.1, “” p &lt; 0.05, ”” p &lt; 0.01, ”” p &lt; 0.001\n\ndatasummary_crosstab:\n\nNew function to produce cross-tabulations\n\ndatasummary:\n\nN is smart enough to return either the number of elements in a subset or the number of non-missing observations in a variable\n\ndatasummary_balance:\n\nKeeps NAs in factor variables by default. Users can convert their variables with the factor() function to omit NAs automatically.\n\nmodelsummary:\n\nthemes can be set using global options (experimental)\nnew vcov options: “bootstrap”, “HAC”, “NeweyWest”, “Andrews”, “panel-corrected”, “weave”, “outer-product”\nA valid get_gof (glance) is now optional.\n… is pushed through to sandwich, which allows things like: modelsummary(model, vcov = \"bootstrap\", R = 1000, cluster = \"firm\")\n\nOther:\n\nJupyter notebook support via output=\"jupyter\"\nBug fixes",
+    "text": "Mostly for internal use, but can be useful when users supply a function to the method argument of datasummary_correlation.\n\ndatasummary_correlation_format(\n  x,\n  fmt,\n  leading_zero = FALSE,\n  diagonal = NULL,\n  upper_triangle = NULL\n)\n\n\n\n\n\nx\n\n\nsquare numeric matrix\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nleading_zero\n\n\nboolean. If FALSE, leading zeros are removed\n\n\n\n\ndiagonal\n\n\ncharacter or NULL. If character, all elements of the diagonal are replaced by the same character (e.g., \"1\").\n\n\n\n\nupper_triangle\n\n\ncharacter or NULL. If character, all elements of the upper triangle are replaced by the same character (e.g., \"\" or \".\").\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_392nnoahgseq58zawjya\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .",
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-    "text": "modelsummary:\n\nnew arguments for modelsummary: group and group_map for grouped parameters (e.g., outcome levels in multinomial logit or components of gamlss model).\ndvnames() makes it easy to get dependent variable column titles (thanks to @NickCH-K)\noutput=\"modelsummary_list\" to save a lightweight list-based representation of the table which can be saved and fed to modelsummary once more to get a full table.\nvcov adds a row to note the type of standard errors.\nmodelsummary accepts a single model with multiple vcovs.\nget_gof forwards … to model_performance\ncoef_map accepts unnamed vectors for easy subsetting\nfixest::fixest_multi support\noptions(modelsummary_get) to set the order of extraction functions to use under the hood (broom vs. easystats vs. all)\nmetrics argument of performance::model_performance is available via modelsummary’s … ellipsis to limit the GOF statistics in Bayesian models.\nusers can omit the stars legend note by using glue strings: estimate=\"{estimate}{stars}\"\noutput=“html” can use gt by setting options(modelsummary_factory_html=\"gt\")\n\ndatasummary_correlation:\n\npasses ... forward\nnew function: datasummary_correlation_format\ndatasummary_correlation’s method argument accepts functions and “pearspear” (thanks to @joachim-gassen)\n\ndatasummary:\n\ndatasummary functions and rounding accept …, big.mark, etc.\n\ndatasummary_skim:\n\nnow works with haven_labeled numeric\nfaster tables with bayesian models.\n\nBug fixes and lints",
+    "text": "Balance table: Summary statistics for different subsets of the data (e.g., control and treatment groups)\nDescription\nCreates balance tables with summary statistics for different subsets of the data (e.g., control and treatment groups). It can also be used to create summary tables for full data sets. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\nUsage\ndatasummary_balance(\n  formula,\n  data,\n  output = \"default\",\n  fmt = fmt_decimal(digits = 1, pdigits = 3),\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  stars = FALSE,\n  add_columns = NULL,\n  add_rows = NULL,\n  dinm = TRUE,\n  dinm_statistic = \"std.error\",\n  escape = TRUE,\n  ...\n)\n\nArguments\n\n\n\nformula\n\n\n\n\n~1: show summary statistics for the full dataset\n\n\none-sided formula: with the \"condition\" or \"column\" variable on the right-hand side.\n\n\ntwo-side formula: with the subset of variables to summarize on the left-hand side and the condition variable on the right-hand side.\n\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble). If this data includes columns called \"blocks\", \"clusters\", and/or \"weights\", the \"estimatr\" package will consider them when calculating the difference in means. If there is a weights column, the reported mean and standard errors will also be weighted.\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nstars\n\n\nto indicate statistical significance\n\n\nFALSE (default): no significance stars.\n\n\nTRUE: +=.1, =.05, =.01, =0.001\n\n\nNamed numeric vector for custom stars such as c(’*’ = .1, ‘+’ = .05)\n\n\nNote: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use \"glue strings\" with stars.\n\n\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\ndinm\n\n\nTRUE calculates a difference in means with uncertainty estimates. This option is only available if the estimatr package is installed. If data includes columns named \"blocks\", \"clusters\", or \"weights\", this information will be taken into account automatically by estimatr::difference_in_means.\n\n\n\n\ndinm_statistic\n\n\nstring: \"std.error\" or \"p.value\"\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\nGlobal Options\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nModel labels: default column names\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nTable-making packages\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nTable themes\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nModel extraction functions\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nFormatting numeric entries\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nLaTeX preamble\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\nExamples\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\ndatasummary_balance(~am, mtcars)\n\n \n\n  \n    \n\ntinytable_ulh2ykegwv72f064kn1v\n\n\n      \n\n\n \n0\n1\n \n \n\n\n \n                Mean\n                Std. Dev.\n                Mean\n                Std. Dev.\n                Diff. in Means\n                Std. Error\n              \n\n\n\nmpg \n                  17.1 \n                  3.8  \n                  24.4 \n                  6.2 \n                  7.2   \n                  1.9 \n                \n\ncyl \n                  6.9  \n                  1.5  \n                  5.1  \n                  1.6 \n                  -1.9  \n                  0.6 \n                \n\ndisp\n                  290.4\n                  110.2\n                  143.5\n                  87.2\n                  -146.8\n                  35.0\n                \n\nhp  \n                  160.3\n                  53.9 \n                  126.8\n                  84.1\n                  -33.4 \n                  26.4\n                \n\ndrat\n                  3.3  \n                  0.4  \n                  4.0  \n                  0.4 \n                  0.8   \n                  0.1 \n                \n\nwt  \n                  3.8  \n                  0.8  \n                  2.4  \n                  0.6 \n                  -1.4  \n                  0.2 \n                \n\nqsec\n                  18.2 \n                  1.8  \n                  17.4 \n                  1.8 \n                  -0.8  \n                  0.6 \n                \n\nvs  \n                  0.4  \n                  0.5  \n                  0.5  \n                  0.5 \n                  0.2   \n                  0.2 \n                \n\ngear\n                  3.2  \n                  0.4  \n                  4.4  \n                  0.5 \n                  1.2   \n                  0.2 \n                \n\ncarb\n                  2.7  \n                  1.1  \n                  2.9  \n                  2.2 \n                  0.2   \n                  0.7",
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-    "text": "new output format: latex_tabular\ntidy_custom allows partial term matches\nmodelsummary(coef_rename) accepts functions\nnew function coef_rename for use in modelsummary(coef_rename=coef_rename)\nmodelplot accepts add_rows to add reference categories\ninformative error message when estimate or statistic is not available\nbug fixes",
+    "text": "The names of the variables displayed in the correlation table are the names of the columns in the data. You can rename those columns (with or without spaces) to produce a table of human-readable variables. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_correlation(\n  data,\n  output = \"default\",\n  method = \"pearson\",\n  fmt = 2,\n  align = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  title = NULL,\n  notes = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nmethod\n\n\ncharacter or function\n\n\ncharacter: \"pearson\", \"kendall\", \"spearman\", or \"pearspear\" (Pearson correlations above and Spearman correlations below the diagonal)\n\n\nfunction: takes a data.frame with numeric columns and returns a square matrix or data.frame with unique row.names and colnames corresponding to variable names. Note that the datasummary_correlation_format can often be useful for formatting the output of custom correlation functions.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nother parameters are passed through to the table-making packages.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# clean variable names (base R)\ndat &lt;- mtcars[, c(\"mpg\", \"hp\")]\ncolnames(dat) &lt;- c(\"Miles / Gallon\", \"Horse Power\")\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_grzw8xi4ix63dmd78n0p\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# clean variable names (tidyverse)\nlibrary(tidyverse)\ndat &lt;- mtcars %&gt;%\n  select(`Miles / Gallon` = mpg,\n         `Horse Power` = hp)\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_5ox0wtvehi0xjlxiovka\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# alternative methods\ndatasummary_correlation(dat, method = \"pearspear\")\n\n \n\n  \n    \n\ntinytable_xqd02z3d30gyj47o4kgz\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  -.78\n                \n\nHorse Power   \n                  -.89\n                  1   \n                \n\n\n\n\n    \n\n# custom function\ncor_fun &lt;- function(x) cor(x, method = \"kendall\")\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_2olu6wk3h3qqthxs8g0z\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1.00\n                  -.74\n                \n\nHorse Power   \n                  -.74\n                  1.00\n                \n\n\n\n\n    \n\n# rename columns alphabetically and include a footnote for reference\nnote &lt;- sprintf(\"(%s) %s\", letters[1:ncol(dat)], colnames(dat))\nnote &lt;- paste(note, collapse = \"; \")\n\ncolnames(dat) &lt;- sprintf(\"(%s)\", letters[1:ncol(dat)])\n\ndatasummary_correlation(dat, notes = note)\n\n \n\n  \n    \n\ntinytable_unbyis56d7465brso0gh\n\n\n      \n\n \n                (a)\n                (b)\n              \n(a) Miles / Gallon; (b) Horse Power\n\n\n(a)\n                  1   \n                  .\n                \n\n(b)\n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# `datasummary_correlation_format`: custom function with formatting\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_290nz6nezudmuuclwueb\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .\n                \n\n\n\n\n    \n\n# use kableExtra and psych to color significant cells\nlibrary(psych)\nlibrary(kableExtra)\n\ndat &lt;- mtcars[, c(\"vs\", \"hp\", \"gear\")]\n\ncor_fun &lt;- function(dat) {\n  # compute correlations and format them\n  correlations &lt;- data.frame(cor(dat))\n  correlations &lt;- datasummary_correlation_format(correlations, fmt = 2)\n\n  # calculate pvalues using the `psych` package\n  pvalues &lt;- psych::corr.test(dat)$p\n\n  # use `kableExtra::cell_spec` to color significant cells\n  for (i in 1:nrow(correlations)) {\n    for (j in 1:ncol(correlations)) {\n      if (pvalues[i, j] &lt; 0.05 && i != j) {\n        correlations[i, j] &lt;- cell_spec(correlations[i, j], background = \"pink\")\n      }\n    }\n  }\n  return(correlations)\n}\n\n# The `escape=FALSE` is important here!\ndatasummary_correlation(dat, method = cor_fun, escape = FALSE)\n\n \n\n  \n    \n\ntinytable_mq9s8yxqc31x4l6kzddz\n\n\n      \n\n \n                vs\n                hp\n                gear\n              \n\n\nvs  \n                  1.00                                                                                                                         \n                  -.72\n                  .21 \n                \n\nhp  \n                  -.72\n                  1.00                                                                                                                         \n                  -.13\n                \n\ngear\n                  .21                                                                                                                          \n                  -.13                                                                                                                         \n                  1.00",
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-    "text": "statistic_override becomes vcov\nvcov accepts shortcuts: “robust”, “stata”, “HC0”, etc.\nvcov accepts formulas for clustered SEs: ~group\nmodelsummary_wide has a new “stacking” argument\nhtml horizontal rule to separate estimates form gof\ngof_map accepts list of lists. only needs 3 columns.\nsupport officedown Rmd\nestimate accepts a vector for per model estimates\noptions(modelsummary_default) can be markdown, html, latex\nbug: passing arguments through …\nbug: stars and rounding",
+    "text": "The names of the variables displayed in the correlation table are the names of the columns in the data. You can rename those columns (with or without spaces) to produce a table of human-readable variables. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_correlation(\n  data,\n  output = \"default\",\n  method = \"pearson\",\n  fmt = 2,\n  align = NULL,\n  add_rows = NULL,\n  add_columns = NULL,\n  title = NULL,\n  notes = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nmethod\n\n\ncharacter or function\n\n\ncharacter: \"pearson\", \"kendall\", \"spearman\", or \"pearspear\" (Pearson correlations above and Spearman correlations below the diagonal)\n\n\nfunction: takes a data.frame with numeric columns and returns a square matrix or data.frame with unique row.names and colnames corresponding to variable names. Note that the datasummary_correlation_format can often be useful for formatting the output of custom correlation functions.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as your main table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nother parameters are passed through to the table-making packages.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# clean variable names (base R)\ndat &lt;- mtcars[, c(\"mpg\", \"hp\")]\ncolnames(dat) &lt;- c(\"Miles / Gallon\", \"Horse Power\")\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_grzw8xi4ix63dmd78n0p\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# clean variable names (tidyverse)\nlibrary(tidyverse)\ndat &lt;- mtcars %&gt;%\n  select(`Miles / Gallon` = mpg,\n         `Horse Power` = hp)\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_5ox0wtvehi0xjlxiovka\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  .\n                \n\nHorse Power   \n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# alternative methods\ndatasummary_correlation(dat, method = \"pearspear\")\n\n \n\n  \n    \n\ntinytable_xqd02z3d30gyj47o4kgz\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1   \n                  -.78\n                \n\nHorse Power   \n                  -.89\n                  1   \n                \n\n\n\n\n    \n\n# custom function\ncor_fun &lt;- function(x) cor(x, method = \"kendall\")\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_2olu6wk3h3qqthxs8g0z\n\n\n      \n\n \n                Miles / Gallon\n                Horse Power\n              \n\n\nMiles / Gallon\n                  1.00\n                  -.74\n                \n\nHorse Power   \n                  -.74\n                  1.00\n                \n\n\n\n\n    \n\n# rename columns alphabetically and include a footnote for reference\nnote &lt;- sprintf(\"(%s) %s\", letters[1:ncol(dat)], colnames(dat))\nnote &lt;- paste(note, collapse = \"; \")\n\ncolnames(dat) &lt;- sprintf(\"(%s)\", letters[1:ncol(dat)])\n\ndatasummary_correlation(dat, notes = note)\n\n \n\n  \n    \n\ntinytable_unbyis56d7465brso0gh\n\n\n      \n\n \n                (a)\n                (b)\n              \n(a) Miles / Gallon; (b) Horse Power\n\n\n(a)\n                  1   \n                  .\n                \n\n(b)\n                  -.78\n                  1\n                \n\n\n\n\n    \n\n# `datasummary_correlation_format`: custom function with formatting\ndat &lt;- mtcars[, c(\"mpg\", \"hp\", \"disp\")]\n\ncor_fun &lt;- function(x) {\n  out &lt;- cor(x, method = \"kendall\")\n  datasummary_correlation_format(\n    out,\n    fmt = 2,\n    upper_triangle = \"x\",\n    diagonal = \".\")\n}\n\ndatasummary_correlation(dat, method = cor_fun)\n\n \n\n  \n    \n\ntinytable_290nz6nezudmuuclwueb\n\n\n      \n\n \n                mpg\n                hp\n                disp\n              \n\n\nmpg \n                  .   \n                  x  \n                  x\n                \n\nhp  \n                  -.74\n                  .  \n                  x\n                \n\ndisp\n                  -.77\n                  .67\n                  .\n                \n\n\n\n\n    \n\n# use kableExtra and psych to color significant cells\nlibrary(psych)\nlibrary(kableExtra)\n\ndat &lt;- mtcars[, c(\"vs\", \"hp\", \"gear\")]\n\ncor_fun &lt;- function(dat) {\n  # compute correlations and format them\n  correlations &lt;- data.frame(cor(dat))\n  correlations &lt;- datasummary_correlation_format(correlations, fmt = 2)\n\n  # calculate pvalues using the `psych` package\n  pvalues &lt;- psych::corr.test(dat)$p\n\n  # use `kableExtra::cell_spec` to color significant cells\n  for (i in 1:nrow(correlations)) {\n    for (j in 1:ncol(correlations)) {\n      if (pvalues[i, j] &lt; 0.05 && i != j) {\n        correlations[i, j] &lt;- cell_spec(correlations[i, j], background = \"pink\")\n      }\n    }\n  }\n  return(correlations)\n}\n\n# The `escape=FALSE` is important here!\ndatasummary_correlation(dat, method = cor_fun, escape = FALSE)\n\n \n\n  \n    \n\ntinytable_mq9s8yxqc31x4l6kzddz\n\n\n      \n\n \n                vs\n                hp\n                gear\n              \n\n\nvs  \n                  1.00                                                                                                                         \n                  -.72\n                  .21 \n                \n\nhp  \n                  -.72\n                  1.00                                                                                                                         \n                  -.13\n                \n\ngear\n                  .21                                                                                                                          \n                  -.13                                                                                                                         \n                  1.00",
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-    "text": "glue format for estimate and statistic\neasystats support for model info extraction\ndeprecate statistic_vertical\ndeprecate extract_models. Use modelsummary(output=“dataframe”) instead.\nmodelplot pushes … through to modelsummary(output=“dataframe”)\ndatasummary_skim(type=“dataset”)\ngof_map omits by default\ndatasummary_balance uses row percentages\nstatistic_override does not require a list\nstatistic_override accepts a single model\nN function for well formatted N in datasummary\nBug fixes",
+    "text": "The number of decimal digits to keep after the decimal is assessed\n\n\n\nfmt_significant(digits = 3, ...)\n\n\n\n\n\n\n\ndigits\n\n\nNumber of significant digits to keep.\n\n\n\n\n…\n\n\nAdditional arguments are passed to the format() function (e.g., big.marks, scientific). See ?format",
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-    "text": "new function: modelsummary_wide\ncoef_omit and gof_omit use grepl(perl=TRUE)\nfmt accepts integer, string or function and respects options(OutDec=“,”)\nalign argument for modelsummary\nalign is more liberal to accept dcolumn alignment\nglance_custom methods for lfe and fixest\nbug fixes",
+    "text": "The number of decimal digits to keep after the decimal is assessed\n\n\n\nfmt_significant(digits = 3, ...)\n\n\n\n\n\n\n\ndigits\n\n\nNumber of significant digits to keep.\n\n\n\n\n…\n\n\nAdditional arguments are passed to the format() function (e.g., big.marks, scientific). See ?format",
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-    "text": "new argument: coef_rename\nnew function: datasummary_df\npreserve term order in modelsummary\nrefactor datasummary_balance\ndatasummary_skim uses svg histograms instead of unicode\nremoved 5 dependencies\npass … to kableExtra::kbl for more customization\ntest improvements\ninternal code style\nbug fixes",
+    "text": "This function was inspired by the excellent skimr package for R. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_skim(\n  data,\n  output = \"default\",\n  type = \"all\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  escape = TRUE,\n  by = NULL,\n  fun_numeric = list(Unique = NUnique, `Missing Pct.` = PercentMissing, Mean = Mean, SD =\n    SD, Min = Min, Median = Median, Max = Max, Histogram = function(x) \"\"),\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\ntype\n\n\nString. Variables to summarize: \"all\", \"numeric\", \"categorical\", \"dataset\"\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\nby\n\n\nCharacter vector of grouping variables to compute statistics over.\n\n\n\n\nfun_numeric\n\n\nNamed list of funtions to apply to each column of data. If fun_numeric includes \"Histogram\" or \"Density\", inline plots are inserted.\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\ndat &lt;- mtcars\ndat$vs &lt;- as.logical(dat$vs)\ndat$cyl &lt;- as.factor(dat$cyl)\ndatasummary_skim(dat)\ndatasummary_skim(dat, type = \"categorical\")",
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+    "text": "This function was inspired by the excellent skimr package for R. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_skim(\n  data,\n  output = \"default\",\n  type = \"all\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  escape = TRUE,\n  by = NULL,\n  fun_numeric = list(Unique = NUnique, `Missing Pct.` = PercentMissing, Mean = Mean, SD =\n    SD, Min = Min, Median = Median, Max = Max, Histogram = function(x) \"\"),\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\ntype\n\n\nString. Variables to summarize: \"all\", \"numeric\", \"categorical\", \"dataset\"\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\nby\n\n\nCharacter vector of grouping variables to compute statistics over.\n\n\n\n\nfun_numeric\n\n\nNamed list of funtions to apply to each column of data. If fun_numeric includes \"Histogram\" or \"Density\", inline plots are inserted.\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\ndat &lt;- mtcars\ndat$vs &lt;- as.logical(dat$vs)\ndat$cyl &lt;- as.factor(dat$cyl)\ndatasummary_skim(dat)\ndatasummary_skim(dat, type = \"categorical\")",
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-    "text": "default HTML output factory is now kableExtra\ninteraction “:” gsubbed by “0d7”\ndependencies: removed 1 depends, 3 imports, and 3 suggests\nword_document knitr works out-of-the-box\nbug fixes",
+    "text": "library(modelsummary)\n\nUnsupported models: modelsummary_list\n\nThe simplest way to summarize an unsupported model is to create a modelsummary_list object. This approach is super flexible, but it requires manual intervention, and it can become tedious if you need to summarize many models. The next section shows how to add formal support for an unsupported model type.\nA modelsummary_list is a list with two element that conform to the broom package specification: tidy and glance. tidy is a data.frame with at least three columns: term, estimate, and std.error. glance is a data.frame with only a single row, and where each column will be displayed at the bottom of the table in the goodness-of-fit section. Finally, we wrap those two elements in a list and assign it a modelsummary_list class:\n\nti &lt;- data.frame(\n  term = c(\"coef1\", \"coef2\", \"coef3\"),\n  estimate = 1:3,\n  std.error = c(pi, exp(1), sqrt(2)))\n\ngl &lt;- data.frame(\n  stat1 = \"blah\",\n  stat2 = \"blah blah\")\n\nmod &lt;- list(\n  tidy = ti,\n  glance = gl)\nclass(mod) &lt;- \"modelsummary_list\"\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_jksl040r3pxtt6k384tx\n\n\n      \n\n \n                (1)\n              \n\n\ncoef1\n                  1.000    \n                \n\n     \n                  (3.142)  \n                \n\ncoef2\n                  2.000    \n                \n\n     \n                  (2.718)  \n                \n\ncoef3\n                  3.000    \n                \n\n     \n                  (1.414)  \n                \n\nstat1\n                  blah     \n                \n\nstat2\n                  blah blah\n                \n\n\n\n\n    \n\n\nUnsupported models: glance and tidy\n\nmodelsummary relies on two functions from the broom package to extract model information: tidy and glance. If broom doesn’t support the type of model you are trying to summarize, modelsummary won’t support it out of the box. Thankfully, it is extremely easy to add support for most models using custom methods.\nFor example, models produced by the MCMCglmm package are not currently supported by broom. To add support, you simply need to create a tidy and a glance method:\n# load packages and data\nlibrary(modelsummary)\nlibrary(MCMCglmm)\ndata(PlodiaPO)\n\n# add custom functions to extract estimates (tidy) and goodness-of-fit (glance) information\ntidy.MCMCglmm &lt;- function(x, ...) {\n    s &lt;- summary(x, ...)\n    ret &lt;- data.frame(\n      term      = row.names(s$solutions),\n      estimate  = s$solutions[, 1],\n      conf.low  = s$solutions[, 2],\n      conf.high = s$solutions[, 3])\n    ret\n}\n\nglance.MCMCglmm &lt;- function(x, ...) {\n    ret &lt;- data.frame(\n      dic = x$DIC,\n      n   = nrow(x$X))\n    ret\n}\n\n# estimate a simple model\nmodel &lt;- MCMCglmm(PO ~ 1 + plate, random = ~ FSfamily, data = PlodiaPO, verbose=FALSE, pr=TRUE)\n\n# summarize the model\nmodelsummary(model, statistic = 'conf.int')\nThree important things to note.\nFirst, the methods are named tidy.MCMCglmm and glance.MCMCglmm because the model object I am trying to summarize is of class MCMCglmm. You can find the class of a model by running: class(model).\nSecond, both of the methods include the ellipsis ... argument.\nThird, in the example above we used the statistic = 'conf.int' argument. This is because the tidy method produces conf.low and conf.high columns. In most cases, users will define std.error column in their custom tidy methods, so the statistic argument will need to be adjusted.\nIf you create new tidy and glance methods, please consider contributing them to broom so that the rest of the community can benefit from your work: https://github.com/tidymodels/broom\nModifying information: tidy_custom and glance_custom\n\nUsers may want to include more information than is made available by the default extractor function. For example, models produced by the MASS::polr do not produce p values by default, which means that we cannot use the stars=TRUE argument in modelsummary. However, it is possible to extract this information by using the lmtest::coeftest function. To include such custom information, we will define new glance_custom and tidy_custom methods.\nWe begin by estimating a model with the MASS::polr:\n\nlibrary(MASS)\n\nmod_ordinal &lt;- polr(as.ordered(gear) ~ mpg + drat, data = mtcars)\n\nget_estimates(mod_ordinal)\n\n  term     estimate  std.error conf.level   conf.low  conf.high   statistic df.error      p.value component s.value group\n1  3|4 13.962948761 4.04107300       0.95  5.6851860 22.2407116  3.45525774       28 0.0017706303     alpha     9.1      \n2  4|5 16.898937342 4.39497069       0.95  7.8962480 25.9016267  3.84506258       28 0.0006356348     alpha    10.6      \n3  mpg -0.008646682 0.09034201       0.95 -0.1916706  0.1708667 -0.09571053       28 0.9244322098      beta     0.1      \n4 drat  3.949431923 1.30665144       0.95  1.6191505  6.8457246  3.02255965       28 0.0053120619      beta     7.6      \n\n\nThe get_estimates function shows that our default extractor does not produce a p.value column. As a result, setting stars=TRUE in modelsummary will produce an error.\nWe know that the MASS::polr produces an object of class polr:\n\nclass(mod_ordinal)\n\n[1] \"polr\"\n\n\nTo extract more (custom) information from a model of this class, we thus define a method called tidy_custom.polr which returns a data.frame with two columns: term and p.value:\n\ntidy_custom.polr &lt;- function(x, ...) {\n  s &lt;- lmtest::coeftest(x)\n  out &lt;- data.frame(\n    term = row.names(s),\n    p.value = s[, \"Pr(&gt;|t|)\"])\n  out\n}\n\nWhen this method is defined, modelsummary can automatically extract p values from all models of this class, and will now work properly with stars=TRUE:\n\nmodelsummary(mod_ordinal, stars = TRUE)\n\n \n\n  \n    \n\ntinytable_5twluwvpajnufzz0zw3n\n\n\n      \n\n \n                (1)\n              \n+ p \n\n\n3|4     \n                  13.963** \n                \n\n        \n                  (4.041)  \n                \n\n4|5     \n                  16.899***\n                \n\n        \n                  (4.395)  \n                \n\nmpg     \n                  -0.009   \n                \n\n        \n                  (0.090)  \n                \n\ndrat    \n                  3.949**  \n                \n\n        \n                  (1.307)  \n                \n\nNum.Obs.\n                  32       \n                \n\nAIC     \n                  51.1     \n                \n\nBIC     \n                  57.0     \n                \n\nRMSE    \n                  3.44     \n                \n\n\n\n\n    \n\n\nNew information: tidy_custom and glance_custom\n\nSometimes users will want to include information that is not supplied by those functions. A pretty easy way to include extra information is to define new glance_custom and tidy_custom methods. To illustrate, we estimate two linear regression models using the lm function:\n\nlibrary(modelsummary)\n\nmod &lt;- list()\nmod[[1]] &lt;- lm(hp ~ mpg + drat, mtcars)\nmod[[2]] &lt;- lm(wt ~ mpg + drat + am, mtcars)\n\nIn R, the lm function produces models of class “lm”:\n\nclass(mod[[1]])\n\n[1] \"lm\"\n\n\nLet’s say you would like to print the dependent variable for each model of this particular class. All you need to do is define a new method called glance_custom.lm. This method should return a data.frame (or tibble) with 1 row, and 1 column per piece of information you want to display. For example:\n\nglance_custom.lm &lt;- function(x, ...) {\n    dv &lt;- as.character(formula(x)[2])\n    out &lt;- data.frame(\"DV\" = dv)\n    return(out)\n}\n\nNow, let’s customize the body of the table. The vcov argument already allows users to customize uncertainty estimates. But imagine you want to override the coefficient estimates of your “lm” models. Easy! All you need to do is define a tidy_custom.lm method which returns a data.frame (or tibble) with one column called “term” and one column called “estimate”.\nHere, we’ll substitute estimates by an up/down-pointing triangles which represents their signs:\n\ntidy_custom.lm &lt;- function(x, ...) {\n    s &lt;- summary(x)$coefficients\n    out &lt;- data.frame(\n      term = row.names(s),\n      estimate = ifelse(s[,1] &gt; 0, '▲', '▼'))\n    return(out)\n}\n\nAfter you define the glance_custom and tidy_custom methods, modelsummary will automatically display your customized model information:\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_m5fjx7sp2ezy631papdo\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  ▲       \n                  ▲      \n                \n\n           \n                  (55.415)\n                  (0.728)\n                \n\nmpg        \n                  ▼       \n                  ▼      \n                \n\n           \n                  (1.792) \n                  (0.019)\n                \n\ndrat       \n                  ▲       \n                  ▼      \n                \n\n           \n                  (20.198)\n                  (0.245)\n                \n\nam         \n                          \n                  ▼      \n                \n\n           \n                          \n                  (0.240)\n                \n\nNum.Obs.   \n                  32      \n                  32     \n                \n\nR2         \n                  0.614   \n                  0.803  \n                \n\nR2 Adj.    \n                  0.588   \n                  0.782  \n                \n\nAIC        \n                  337.9   \n                  46.4   \n                \n\nBIC        \n                  343.7   \n                  53.7   \n                \n\nLog.Lik.   \n                  -164.940\n                  -18.201\n                \n\nF          \n                  23.100  \n                  38.066 \n                \n\nRMSE       \n                  41.91   \n                  0.43   \n                \n\nDV         \n                  hp      \n                  wt     \n                \n\n\n\n\n    \n\n\nNote that you can define a std.error column in tidy_custom.lm to replace the uncertainty estimates instead of the coefficients.\nCustomization: New model class\nAn even more fundamental way to customize the output would be to completely bypass modelsummary’s extractor functions by assigning a new class name to your model. For example,\n\n# estimate a linear model\nmod_custom &lt;- lm(hp ~ mpg + drat, mtcars)\n\n# assign it a new class\nclass(mod_custom) &lt;- \"custom\"\n\n# define tidy and glance methods\ntidy.custom &lt;- function(x, ...) {\n  data.frame(\n    term = names(coef(x)),\n    estimate = letters[1:length(coef(x))],\n    std.error = seq_along(coef(x))\n  )\n}\n\nglance.custom &lt;- function(x, ...) {\n  data.frame(\n    \"Model\" = \"Custom\",\n    \"nobs\" = stats:::nobs.lm(x)\n  )\n}\n\n# summarize\nmodelsummary(mod_custom)\n\n \n\n  \n    \n\ntinytable_h4l0flseibxj7c69crx7\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  a      \n                \n\n           \n                  (1.000)\n                \n\nmpg        \n                  b      \n                \n\n           \n                  (2.000)\n                \n\ndrat       \n                  c      \n                \n\n           \n                  (3.000)\n                \n\nNum.Obs.   \n                  32     \n                \n\nModel      \n                  Custom \n                \n\n\n\n\n    \n\n\nWarning: When defining new tidy and glance methods, it is important to include an ellipsis argument (...).\nNote that in the glance.custom() method, we called stats:::nobs.lm() instead of the default stats::nobs() method, because the latter know does not know where to dispatch models of our new “custom” class. Being more explicit solves the problem.\nAn alternative would be to set a new class that inherits from the previous one, and to use a global option to set broom as the default extractor function (otherwise modelsummary will use its standard lm extractors by inheritance):\n\noptions(modelsummary_get = \"broom\")\nclass(mod_custom) &lt;- c(\"custom\", \"lm\")\n\nCustomization: modelsummary_list\n\nAnother flexible way to customize model output is to use output = \"modelsummary_list\". With this output option, modelsummary() returns a list with two elements: tidy contains parameter estimates, standard errors, etc., and glance contains model statistics such as the AIC. For example,\n\nmod &lt;- lm(hp ~ mpg + drat, mtcars)\nmod_list &lt;- modelsummary(mod, output = \"modelsummary_list\")\nmod_list$tidy\n\n         term   estimate std.error  statistic df.error      p.value s.value group conf.low conf.high\n1 (Intercept) 278.515455 55.414866  5.0260061       29 2.359726e-05    15.4             NA        NA\n2         mpg  -9.985499  1.791837 -5.5727709       29 5.172030e-06    17.6             NA        NA\n3        drat  19.125752 20.197756  0.9469246       29 3.515013e-01     1.5             NA        NA\n\nmod_list$glance\n\n       aic      bic r.squared adj.r.squared     rmse nobs        F    logLik\n1 337.8809 343.7438 0.6143611     0.5877653 41.90687   32 23.09994 -164.9404\n\n\nBoth tidy and glance can now be customized, and the updated model can be passed back to modelsummary using modelsummary(mod_list). All information that is displayed in the table is contained in mod_list, so this pattern allows for very flexible adjustments of output tables.\nA useful example for this pattern concerns mixed models using lme4. Assume we want to compare the effect of using different degrees-of-freedom adjustments on the significance of the coefficients. The models have identical parameter estimates, standard errors, and model fit statistics - we only want to change the p-values. We use the parameters package to compute the adjusted p-values.\n\nlibrary(\"lme4\")\nmod &lt;- lmer(mpg ~ drat + (1 | am), data = mtcars)\nmod_list &lt;- modelsummary(mod, output = \"modelsummary_list\", effects = \"fixed\")\n# create a copy, where we'll change the p-values\nmod_list_kenward &lt;- as.list(mod_list)\nmod_list_kenward$tidy$p.value &lt;- parameters::p_value_kenward(mod)$p\n\nmodelsummary(list(\"Wald\" = mod_list, \"Kenward\" = mod_list_kenward), \n            statistic = \"{std.error} ({p.value}) {stars}\")\n\n \n\n  \n    \n\ntinytable_utn1rzbcwfztkd0j2ikt\n\n\n      \n\n \n                Wald\n                Kenward\n              \n\n\n(Intercept)\n                  -5.159            \n                  -5.159         \n                \n\n           \n                  6.409 (0.428)     \n                  6.409 (0.680)  \n                \n\ndrat       \n                  7.045             \n                  7.045          \n                \n\n           \n                  1.736 (\n                  \n1.736 (0.086) +\n                \n\nNum.Obs.   \n                  32                \n                  32             \n                \n\nR2 Marg.   \n                  0.402             \n                  0.402          \n                \n\nR2 Cond.   \n                  0.440             \n                  0.440          \n                \n\nAIC        \n                  188.7             \n                  188.7          \n                \n\nBIC        \n                  194.6             \n                  194.6          \n                \n\nICC        \n                  0.1               \n                  0.1            \n                \n\nRMSE       \n                  4.28              \n                  4.28",
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-    "text": "glance_custom.fixest ships with modelsummary",
+    "text": "modelsummary includes a powerful set of utilities to customize the information displayed in your model summary tables. You can easily rename, reorder, subset or omit parameter estimates; choose the set of goodness-of-fit statistics to display; display various “robust” standard errors or confidence intervals; add titles, footnotes, or source notes; insert stars or custom characters to indicate levels of statistical significance; or add rows with supplemental information about your models.\nBefore starting, we set modelsummary to use the tinytable package to draw table, and we set a few CSS properties to make tables more display in a more compact format on this website. This step is optional.\nNow, we download data, fit some models, and summarize them in a table using the modelsummary() function:\nlibrary(modelsummary)\n\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'\ndat &lt;- read.csv(url)\n\nmodels &lt;- list(\n  \"OLS 1\"     = lm(Donations ~ Literacy + Clergy, data = dat),\n  \"Poisson\" = glm(Donations ~ Literacy + Commerce, family = poisson, data = dat),\n  \"OLS 2\"     = lm(Crime_pers ~ Literacy + Clergy, data = dat)\n)\n\nmodelsummary(models)\n\n \n\n  \n    \n\ntinytable_99h4l9rdsyut9f7et9pf\n\n\n      \n\n \n                OLS 1\n                Poisson\n                OLS 2\n              \n\n\n(Intercept)\n                  7948.667  \n                  8.241      \n                  16259.384 \n                \n\n           \n                  (2078.276)\n                  (0.006)    \n                  (2611.140)\n                \n\nLiteracy   \n                  -39.121   \n                  0.003      \n                  3.680     \n                \n\n           \n                  (37.052)  \n                  (0.000)    \n                  (46.552)  \n                \n\nClergy     \n                  15.257    \n                             \n                  77.148    \n                \n\n           \n                  (25.735)  \n                             \n                  (32.334)  \n                \n\nCommerce   \n                            \n                  0.011      \n                            \n                \n\n           \n                            \n                  (0.000)    \n                            \n                \n\nNum.Obs.   \n                  86        \n                  86         \n                  86        \n                \n\nR2         \n                  0.020     \n                             \n                  0.065     \n                \n\nR2 Adj.    \n                  -0.003    \n                             \n                  0.043     \n                \n\nAIC        \n                  1740.8    \n                  274160.8   \n                  1780.0    \n                \n\nBIC        \n                  1750.6    \n                  274168.2   \n                  1789.9    \n                \n\nLog.Lik.   \n                  -866.392  \n                  -137077.401\n                  -886.021  \n                \n\nF          \n                  0.866     \n                  18294.559  \n                  2.903     \n                \n\nRMSE       \n                  5740.99   \n                  5491.61    \n                  7212.97",
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-    "text": "datasummary\ndatasummary_skim\ndatasummary_balance\ndatasummary_correlation\nmodelplot\nallow duplicate model names\nbug: can’t use coef_map with multiple statistics (thanks @sbw78)\nbug: wrong number of stars w/ statistic=‘p.value’ (thanks @torfason)\noutput=‘data.frame’. extract is no longer documented.",
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+    "text": "Formula\nThe left side of the formula represents the rows and the right side represents the columns. The default formula is term + statistic ~ model:\n\nm &lt;- list(\n    lm(mpg ~ hp, data = mtcars),\n    lm(mpg ~ hp + drat, data = mtcars))\n\nmodelsummary(m, shape = term + statistic ~ model, gof_map = NA)\n\n \n\n  \n    \n\ntinytable_dbt1j652k5dsry1dv6vw\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  30.099 \n                  10.790 \n                \n\n           \n                  (1.634)\n                  (5.078)\n                \n\nhp         \n                  -0.068 \n                  -0.052 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\ndrat       \n                         \n                  4.698  \n                \n\n           \n                         \n                  (1.192)\n                \n\n\n\n\n    \n\n\nWe can display statistics horizontally with:\n\nmodelsummary(m,\n             shape = term ~ model + statistic,\n             statistic = \"conf.int\",\n             gof_map = NA)\n\n \n\n  \n    \n\ntinytable_6wq9xs0pmvbqoj2c9917\n\n\n      \n\n\n \n(1)\n(2)\n\n\n \n                Est.\n                2.5 %\n                97.5 %\n                Est.\n                2.5 %\n                97.5 %\n              \n\n\n\n(Intercept)\n                  30.099\n                  26.762\n                  33.436\n                  10.790\n                  0.405 \n                  21.175\n                \n\nhp         \n                  -0.068\n                  -0.089\n                  -0.048\n                  -0.052\n                  -0.071\n                  -0.033\n                \n\ndrat       \n                        \n                        \n                        \n                  4.698 \n                  2.261 \n                  7.135 \n                \n\n\n\n\n    \n\n\nThe order of terms in the formula determines the order of headers in the table.\n\nmodelsummary(m,\n             shape = term ~ statistic + model,\n             statistic = \"conf.int\",\n             gof_map = NA)\n\n \n\n  \n    \n\ntinytable_y8gy4u2z9y6uu125wr19\n\n\n      \n\n\n \nEst.\n2.5 %\n97.5 %\n\n\n \n                (1)\n                (2)\n                (1)\n                (2)\n                (1)\n                (2)\n              \n\n\n\n(Intercept)\n                  30.099\n                  10.790\n                  26.762\n                  0.405 \n                  33.436\n                  21.175\n                \n\nhp         \n                  -0.068\n                  -0.052\n                  -0.089\n                  -0.071\n                  -0.048\n                  -0.033\n                \n\ndrat       \n                        \n                  4.698 \n                        \n                  2.261 \n                        \n                  7.135 \n                \n\n\n\n\n    \n\n\nshape does partial matching and will try to fill-in incomplete formulas:\n\nmodelsummary(m, shape = ~ statistic)\n\nSome models like multinomial logit or GAMLSS produce “grouped” parameter estimates. To display these groups, we can include a group identifier in the shape formula. This group identifier must be one of the column names produced by get_estimates(model). For example, in models produced by nnet::multinom, the group identifier is called “response”:\n\nlibrary(nnet)\n\ndat_multinom &lt;- mtcars\ndat_multinom$cyl &lt;- sprintf(\"Cyl: %s\", dat_multinom$cyl)\n\nmod &lt;- list(\n    nnet::multinom(cyl ~ mpg, data = dat_multinom, trace = FALSE),\n    nnet::multinom(cyl ~ mpg + drat, data = dat_multinom, trace = FALSE))\n\nget_estimates(mod[[1]])\n\n         term  estimate std.error conf.level    conf.low   conf.high statistic df.error    p.value response s.value group\n1 (Intercept) 47.252432 34.975171       0.95 -21.2976435 115.8025065  1.351028      Inf 0.17668650   Cyl: 6     2.5      \n2         mpg -2.205418  1.637963       0.95  -5.4157653   1.0049299 -1.346440      Inf 0.17816078   Cyl: 6     2.5      \n3 (Intercept) 72.440246 37.175162       0.95  -0.4217332 145.3022247  1.948619      Inf 0.05134088   Cyl: 8     4.3      \n4         mpg -3.579991  1.774693       0.95  -7.0583242  -0.1016573 -2.017246      Inf 0.04366989   Cyl: 8     4.5      \n\n\nTo summarize the results, we can type:\n\n# modelsummary(mod, shape = term + response ~ statistic)\n\nThe terms of the shape formula above can of course be rearranged to reshape the table. For example:\n\nmodelsummary(mod, shape = model + term ~ response)\n\n \n\n  \n    \n\ntinytable_acr2zvourq9zwecvqt70\n\n\n      \n\n \n                  \n                Cyl: 6\n                Cyl: 8\n              \n\n\n(1)\n                  (Intercept)\n                  47.252  \n                  72.440  \n                \n\n   \n                             \n                  (34.975)\n                  (37.175)\n                \n\n   \n                  mpg        \n                  -2.205  \n                  -3.580  \n                \n\n   \n                             \n                  (1.638) \n                  (1.775) \n                \n\n(2)\n                  (Intercept)\n                  89.573  \n                  117.971 \n                \n\n   \n                             \n                  (86.884)\n                  (87.998)\n                \n\n   \n                  mpg        \n                  -3.627  \n                  -4.838  \n                \n\n   \n                             \n                  (3.869) \n                  (3.915) \n                \n\n   \n                  drat       \n                  -3.210  \n                  -5.028  \n                \n\n   \n                             \n                  (3.810) \n                  (4.199) \n                \n\n\n\n\n    \n\n\nWe can combine the term and group identifier columns by inserting an interaction colon : instead of the + in the formula:\n\nlibrary(marginaleffects)\nmod &lt;- glm(am ~ mpg + factor(cyl), family = binomial, data = mtcars)\nmfx &lt;- avg_slopes(mod)\n\nmodelsummary(mfx, shape = term + contrast ~ model)\n\n \n\n  \n    \n\ntinytable_6jv7kmo5efo0m52s8kuh\n\n\n      \n\n \n                  \n                (1)\n              \n\n\ncyl     \n                  mean(6) - mean(4)\n                  0.097  \n                \n\n        \n                                   \n                  (0.166)\n                \n\n        \n                  mean(8) - mean(4)\n                  0.093  \n                \n\n        \n                                   \n                  (0.234)\n                \n\nmpg     \n                  mean(dY/dX)      \n                  0.056  \n                \n\n        \n                                   \n                  (0.027)\n                \n\nNum.Obs.\n                                   \n                  32     \n                \n\nAIC     \n                                   \n                  37.4   \n                \n\nBIC     \n                                   \n                  43.3   \n                \n\nLog.Lik.\n                                   \n                  -14.702\n                \n\nF       \n                                   \n                  2.236  \n                \n\nRMSE    \n                                   \n                  0.39   \n                \n\n\n\n\n    \n\n\n\nmodelsummary(mfx, shape = term : contrast ~ model)\n\n \n\n  \n    \n\ntinytable_csdz8yqwhu6ghauoumv4\n\n\n      \n\n \n                (1)\n              \n\n\ncyl mean(6) - mean(4)\n                  0.097  \n                \n\n                     \n                  (0.166)\n                \n\ncyl mean(8) - mean(4)\n                  0.093  \n                \n\n                     \n                  (0.234)\n                \n\nmpg mean(dY/dX)      \n                  0.056  \n                \n\n                     \n                  (0.027)\n                \n\nNum.Obs.             \n                  32     \n                \n\nAIC                  \n                  37.4   \n                \n\nBIC                  \n                  43.3   \n                \n\nLog.Lik.             \n                  -14.702\n                \n\nF                    \n                  2.236  \n                \n\nRMSE                 \n                  0.39",
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+    "text": "String (“rbind” or “rcollapse”): Panels of models in stacked regression tables\nNote: The code in this section requires version 1.3.0 or the development version of modelsummary. See the website for installation instructions.\nThis section shows how to “stack/bind” multiple regression tables on top of one another, to display the results several models side-by-side and top-to-bottom. For example, imagine that we want to present 4 different models, half of which are estimated using a different outcome variable. When using modelsummary, we store models in a list. When using modelsummary with shape=\"rbind\" or shape=\"rbind\", we store models in a list of lists:\n\ngm &lt;- c(\"r.squared\", \"nobs\", \"rmse\")\n\npanels &lt;- list(\n  list(\n    lm(mpg ~ 1, data = mtcars),\n    lm(mpg ~ qsec, data = mtcars)\n  ),\n  list(\n    lm(hp ~ 1, data = mtcars),\n    lm(hp ~ qsec, data = mtcars)\n  )\n)\n\nmodelsummary(\n  panels,\n  shape = \"rbind\",\n  gof_map = gm)\n\n \n\n  \n    \n\ntinytable_y983v9oe0oiaouncc3kt\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  20.091  \n                  -5.114  \n                \n\n           \n                  (1.065) \n                  (10.030)\n                \n\nqsec       \n                          \n                  1.412   \n                \n\n           \n                          \n                  (0.559) \n                \n\nR2         \n                  0.000   \n                  0.175   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  5.93    \n                  5.39    \n                \n\n(Intercept)\n                  146.688 \n                  631.704 \n                \n\n           \n                  (12.120)\n                  (88.700)\n                \n\nqsec       \n                          \n                  -27.174 \n                \n\n           \n                          \n                  (4.946) \n                \n\nR2         \n                  0.000   \n                  0.502   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  67.48   \n                  47.64   \n                \n\n\n\n\n    \n\n\nLike with modelsummary(), we can can name models and panels by naming elements of our nested list:\n\npanels &lt;- list(\n  \"Outcome: mpg\" = list(\n    \"(I)\" = lm(mpg ~ 1, data = mtcars),\n    \"(II)\" = lm(mpg ~ qsec, data = mtcars)\n  ),\n  \"Outcome: hp\" = list(\n    \"(I)\" = lm(hp ~ 1, data = mtcars),\n    \"(II)\" = lm(hp ~ qsec, data = mtcars)\n  )\n)\n\nmodelsummary(\n  panels,\n  shape = \"rbind\",\n  gof_map = gm)\n\n \n\n  \n    \n\ntinytable_mh0rh4x36zji6idjis27\n\n\n      \n\n \n                (I)\n                (II)\n              \n\n\n(Intercept)\n                  20.091  \n                  -5.114  \n                \n\n           \n                  (1.065) \n                  (10.030)\n                \n\nqsec       \n                          \n                  1.412   \n                \n\n           \n                          \n                  (0.559) \n                \n\nR2         \n                  0.000   \n                  0.175   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  5.93    \n                  5.39    \n                \n\n(Intercept)\n                  146.688 \n                  631.704 \n                \n\n           \n                  (12.120)\n                  (88.700)\n                \n\nqsec       \n                          \n                  -27.174 \n                \n\n           \n                          \n                  (4.946) \n                \n\nR2         \n                  0.000   \n                  0.502   \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nRMSE       \n                  67.48   \n                  47.64",
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-    "text": "huxtable support\nflextable support\nestimate argument\nfixest tidiers\nwebsite and vignette improvements\ngof_map additions\nglance_custom\ntidy_custom",
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+    "text": "String (“cbind”): Automatic spanning column labels\nWhen using the default tinytable backend, we can use a named nested list to automatically add spanning column labels to a table:\n\nmodelsummary(panels, shape = \"cbind\")\n\n \n\n  \n    \n\ntinytable_o7l69z8rk6pj9qinpdy5\n\n\n      \n\n\n \nOutcome: mpg\nOutcome: hp\n\n\n \n                (I)\n                (II)\n                (I) \n                (II) \n              \n\n\n\n(Intercept)\n                  20.091  \n                  -5.114  \n                  146.688 \n                  631.704 \n                \n\n           \n                  (1.065) \n                  (10.030)\n                  (12.120)\n                  (88.700)\n                \n\nqsec       \n                          \n                  1.412   \n                          \n                  -27.174 \n                \n\n           \n                          \n                  (0.559) \n                          \n                  (4.946) \n                \n\nNum.Obs.   \n                  32      \n                  32      \n                  32      \n                  32      \n                \n\nR2         \n                  0.000   \n                  0.175   \n                  0.000   \n                  0.502   \n                \n\nR2 Adj.    \n                  0.000   \n                  0.148   \n                  0.000   \n                  0.485   \n                \n\nAIC        \n                  208.8   \n                  204.6   \n                  364.4   \n                  344.1   \n                \n\nBIC        \n                  211.7   \n                  209.0   \n                  367.3   \n                  348.5   \n                \n\nLog.Lik.   \n                  -102.378\n                  -99.294 \n                  -180.186\n                  -169.045\n                \n\nF          \n                          \n                  6.377   \n                          \n                  30.190  \n                \n\nRMSE       \n                  5.93    \n                  5.39    \n                  67.48   \n                  47.64",
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+    "text": "Renaming estimates and statistics\nWhen statistics are displayed in different columns, users may want control over the statistic names. To do so, we specify the estimate and statistic as named vectors:\n\nmod &lt;- lm(mpg ~ factor(cyl), mtcars)\n\nmodelsummary(\n    mod, \n    estimate = c(\"$\\\\hat{\\\\beta}$\" = \"estimate\"),\n    statistic = c(\"$\\\\sigma_{\\\\hat{\\\\beta}}$\" = \"std.error\"),\n    shape = term ~ model + statistic)\n\n \n\n  \n    \n\ntinytable_glh2tfj40vrzbc6l8hzb\n\n\n      \n\n\n \n(1)\n\n\n \n                $\\hat{\\beta}$\n                $\\sigma_{\\hat{\\beta}}$\n              \n\n\n\n(Intercept) \n                  26.664 \n                  0.972\n                \n\nfactor(cyl)6\n                  -6.921 \n                  1.558\n                \n\nfactor(cyl)8\n                  -11.564\n                  1.299\n                \n\nNum.Obs.    \n                  32     \n                       \n                \n\nR2          \n                  0.732  \n                       \n                \n\nR2 Adj.     \n                  0.714  \n                       \n                \n\nAIC         \n                  170.6  \n                       \n                \n\nBIC         \n                  176.4  \n                       \n                \n\nLog.Lik.    \n                  -81.282\n                       \n                \n\nF           \n                  39.698 \n                       \n                \n\nRMSE        \n                  3.07   \n                       \n                \n\n\n\n\n    \n\n\nfixest\nThe fixest package offers powerful tools to estimate multiple models using a concise syntax. fixest functions are also convenient because they return named lists of models which are easy to subset and manipulate using standard R functions like grepl.\nFor example, to introduce regressors in stepwise fashion, and to estimate models on different subsets of the data, we can do:\n\n# estimate 4 models\nlibrary(fixest)\nmod &lt;- feols(\n  c(hp, mpg) ~ csw(qsec, drat) | gear,\n  data = mtcars)\n\n# select models with different outcome variables\npanels &lt;- list(\n  \"Miles per gallon\" = mod[grepl(\"mpg\", names(mod))],\n  \"Horsepower\" = mod[grepl(\"hp\", names(mod))]\n)\n\nmodelsummary(\n  panels,\n  shape = \"rcollapse\",\n  gof_omit = \"IC|R2\")\n\n \n\n  \n    \n\ntinytable_rx0se4nak144lxodtco6\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\nqsec      \n                  1.436   \n                  1.519   \n                \n\n          \n                  (0.594) \n                  (0.529) \n                \n\ndrat      \n                          \n                  5.765   \n                \n\n          \n                          \n                  (2.381) \n                \n\nRMSE      \n                  4.03    \n                  3.67    \n                \n\nqsec      \n                  -22.175 \n                  -22.676 \n                \n\n          \n                  (12.762)\n                  (13.004)\n                \n\ndrat      \n                          \n                  -35.106 \n                \n\n          \n                          \n                  (28.509)\n                \n\nRMSE      \n                  40.45   \n                  39.14   \n                \n\nNum.Obs.  \n                  32      \n                  32      \n                \n\nStd.Errors\n                  by: gear\n                  by: gear\n                \n\nFE: gear  \n                  X       \n                  X       \n                \n\n\n\n\n    \n\n\nWe can use all the typical extension systems to add information, such as the mean of the dependent variable:\n\nglance_custom.fixest &lt;- function(x, ...) {\n  dv &lt;- insight::get_response(x)\n  dv &lt;- sprintf(\"%.2f\", mean(dv, na.rm = TRUE))\n  data.table::data.table(`Mean of DV` = dv)\n}\n\nmodelsummary(\n  panels,\n  shape = \"rcollapse\",\n  gof_omit = \"IC|R2\")\n\n \n\n  \n    \n\ntinytable_avz1abqpghyn15q768ft\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\nqsec      \n                  1.436   \n                  1.519   \n                \n\n          \n                  (0.594) \n                  (0.529) \n                \n\ndrat      \n                          \n                  5.765   \n                \n\n          \n                          \n                  (2.381) \n                \n\nRMSE      \n                  4.03    \n                  3.67    \n                \n\nMean of DV\n                  20.09   \n                  20.09   \n                \n\nqsec      \n                  -22.175 \n                  -22.676 \n                \n\n          \n                  (12.762)\n                  (13.004)\n                \n\ndrat      \n                          \n                  -35.106 \n                \n\n          \n                          \n                  (28.509)\n                \n\nRMSE      \n                  40.45   \n                  39.14   \n                \n\nMean of DV\n                  146.69  \n                  146.69  \n                \n\nNum.Obs.  \n                  32      \n                  32      \n                \n\nStd.Errors\n                  by: gear\n                  by: gear\n                \n\nFE: gear  \n                  X       \n                  X       \n                \n\n\n\n\n    \n\nrm(\"glance_custom.fixest\")",
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-    "text": "Convenience function to render markdown in row/column labels\nbug: breakage when all GOF were omitted\nClean up manual with @keywords internal\nbug: tidyr import",
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+    "text": "Reference categories\nWhen estimating models with factor regressors, R will typically omit the reference category. We can include an empty row for the reference level automatically by using the include_reference argument. This argument is supplied by the parameters package, which modelsummary uses behind the scenes to extract model estimates. We can supply it directly to modelsummary(), which will then pass the argument forward automatically:\n\ntmp &lt;- transform(mtcars, cyl = factor(cyl))\nmod &lt;- lm(mpg ~ cyl, tmp)\nmodelsummary(mod, include_reference = TRUE)\n\n \n\n  \n    \n\ntinytable_ilm86p0fvjfgpjpfr2ms\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  26.664 \n                \n\n           \n                  (0.972)\n                \n\ncyl4       \n                  -      \n                \n\ncyl6       \n                  -6.921 \n                \n\n           \n                  (1.558)\n                \n\ncyl8       \n                  -11.564\n                \n\n           \n                  (1.299)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.732  \n                \n\nR2 Adj.    \n                  0.714  \n                \n\nAIC        \n                  170.6  \n                \n\nBIC        \n                  176.4  \n                \n\nLog.Lik.   \n                  -81.282\n                \n\nF          \n                  39.698 \n                \n\nRMSE       \n                  3.07",
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-    "text": "gt is now available on CRAN\nnew latex_env argument for knit_latex and clean_latex\nbug when all gof omitted\nbug in statistic_override with functions\nbug caused by upstream changes in tab_style\nbug caused by upstream changes in filename=‘rtf’\nAllow multiple rows of uncertainty estimates per coefficient\nPreserve add_rows order\nDisplay uncertainty estimates next to the coefficient with statistic_vertical = FALSE\nBetter clean_latex function\nCan display R2 and confidence intervals for mice-imputed lm-models\nInternal functions have @keywords internal to avoid inclusion in docs\nStatistic override accepts pre-formatted character vectors",
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+    "text": "Standardization\nIn some cases, it is useful to standardize coefficients before reporting them. modelsummary extracts coefficients from model objects using the parameters package, and that package offers several options for standardization: https://easystats.github.io/parameters/reference/model_parameters.default.html\nWe can pass the standardize argument directly to modelsummary or modelplot, and that argument will be forwarded to parameters. For example to refit the model on standardized data and plot the results, we can do:\n\nmod &lt;- lm(mpg ~ hp + am, data = mtcars)\n\nmodelplot(mod, standardize = \"refit\")\n\n\n\n\n\n\n\nCompare to the unstandardized plot:\n\nmodelplot(mod)",
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+    "text": "Rmarkdown\nYou can use modelsummary to insert tables into dynamic documents with knitr or Rmarkdown. This minimal .Rmd file can produce tables in PDF, HTML, or RTF documents:\n\nminimal.Rmd\n\nThis .Rmd file shows illustrates how to use table numbering and cross-references to produce PDF documents using bookdown:\n\ncross_references.Rmd\n\nThis .Rmd file shows how to customize tables in PDF and HTML files using gt and kableExtra functions:\n\nappearance.Rmd",
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-    "text": "To customize the appearance of tables, modelsummary supports five of the most popular table-making packages:\nUsers are encouraged to visit these websites to determine which package suits their needs best.\nTo create customized tables, we proceed as follows:\nTo illustrate, we download data from the Rdatasets repository and we estimate 5 models:\nlibrary(modelsummary)\n\nurl &lt;- \"https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv\"\ndat &lt;- read.csv(url, na.strings = \"\")\n\nmodels &lt;- list(\n    I = lm(Donations ~ Literacy, data = dat),\n    II = lm(Crime_pers ~ Literacy, data = dat),\n    III = lm(Crime_prop ~ Literacy + Clergy, data = dat),\n    IV = glm(Crime_pers ~ Literacy + Clergy, family = poisson, data = dat),\n    V = glm(Donations ~ Literacy + Clergy, family = poisson, data = dat)\n)\nIn the rest of this vignette, we will customize tables using tools tinytable and gt. The same process can be used to customize kableExtra, flextable, huxtable, and DT tables.",
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+    "text": "Quarto\nQuarto is an open source publishing system built on top of Pandoc. It was designed as a “successor” to Rmarkdown, and includes useful features for technical writing, such as built-in support for cross-references. modelsummary works automatically with Quarto. This is a minimal document with cross-references which should render automatically to PDF, HTML, and more:\n---\nformat: pdf\ntitle: Example\n---\n\n@tbl-mtcars shows that cars with high horse power get low miles per gallon.\n\n::: {#tbl-mtcars .cell tbl-cap='Horse Powers vs. Miles per Gallon'}\n\n```{.r .cell-code}\nlibrary(modelsummary)\nmod &lt;- lm(mpg ~ hp, mtcars)\nmodelsummary(mod)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n&lt;!DOCTYPE html&gt; \n&lt;html lang=\"en\"&gt;\n  &lt;head&gt;\n    &lt;meta charset=\"UTF-8\"&gt;\n    &lt;meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\"&gt;\n    &lt;title&gt;tinytable_wd2p3rre3btolfnvj1e1&lt;/title&gt;\n    &lt;style&gt;\n.table td.tinytable_css_c0xazs972bxdfed0u1t0, .table th.tinytable_css_c0xazs972bxdfed0u1t0 {    border-bottom: solid 0.1em #d3d8dc; }\n.table td.tinytable_css_3dp8uus41ymhe93e5b6h, .table th.tinytable_css_3dp8uus41ymhe93e5b6h {    text-align: left; }\n.table 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-    "text": "Themes: Data Frame\nA particularly flexible strategy is to apply a theme to the dataframe output format. To illustrate, recall that setting output=\"dataframe\" produces a data frame with a lot of extraneous meta information. To produce a nice table, we have to process that output a bit:\n\nmod &lt;- lm(mpg ~ hp + drat, mtcars)\n\nmodelsummary(mod, output = \"dataframe\")\n\n        part        term statistic     (1)\n1  estimates (Intercept)  estimate  10.790\n2  estimates (Intercept) std.error (5.078)\n3  estimates          hp  estimate  -0.052\n4  estimates          hp std.error (0.009)\n5  estimates        drat  estimate   4.698\n6  estimates        drat std.error (1.192)\n7        gof    Num.Obs.                32\n8        gof          R2             0.741\n9        gof     R2 Adj.             0.723\n10       gof         AIC             169.5\n11       gof         BIC             175.4\n12       gof    Log.Lik.           -80.752\n13       gof           F            41.522\n14       gof        RMSE              3.02\n\n\nmodelsummary supports the DT table-making package out of the box. But for the sake of illustration, imagine we want to create a table using the DT package with specific customization and options, in a repeatable fashion. To do this, we can create a theming function:\n\nlibrary(DT)\n\ntheme_df &lt;- function(tab) {\n    out &lt;- tab\n    out$term[out$statistic == \"modelsummary_tmp2\"] &lt;- \" \"\n    out$part &lt;- out$statistic &lt;- NULL\n    colnames(out)[1] &lt;- \" \"\n    datatable(out, rownames = FALSE,\n              options = list(pageLength = 30))\n}\n\noptions(\"modelsummary_theme_dataframe\" = theme_df)\nmodelsummary(mod, output = \"dataframe\")\n\n\n\n\n\nRestore default theme:\n\noptions(\"modelsummary_theme_dataframe\" = NULL)",
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+    "text": "Emacs Org-Mode\nYou can use modelsummary to insert tables into Emacs Org-Mode documents, which can be exported to a variety of formats, including HTML and PDF (via LaTeX). As with anything Emacs-related, there are many ways to achieve the outcomes you want. Here is one example of an Org-Mode document which can automatically export tables to HTML and PDF without manual tweaks:\n#+PROPERTY: header-args:R :var orgbackend=(prin1-to-string org-export-current-backend)\n#+MACRO: Rtable (eval (concat \"#+header: :results output \" (prin1-to-string org-export-current-backend)))\n\n{{{Rtable}}}\n#+BEGIN_SRC R :exports both\nlibrary(modelsummary)\noptions(modelsummary_factory_default = orgbackend)\n\nmod = lm(hp ~ mpg, data = mtcars)\n\nmodelsummary(mod)\n#+END_SRC\nThe first line tells Org-mode to assign a variable called orgbackend. This variable will be accessible by the R session, and will be equal to “html” or “latex”, depending on the export format.\nThe second line creates an Org macro which we will use to automatically add useful information to the header of source blocks. For instance, when we export to HTML, the macro will expand to :results output html. This tells Org-Mode to insert the last printed output from the R session, and to treat it as raw HTML.\nThe {{Rtable}} call expands the macro to add information to the header of the block that follows.\n#+BEGIN_SRC R :exports both says that we want to print both the original code and the output (:exports results would omit the code, for example).\nFinally, options(modelsummary_factory_default=orgbackend uses the variable we defined to set the default output format. That way, we don’t have to use the output argument every time.\nOne potentially issue to keep in mind is that the code above extracts the printout from the R console. However, when we customize tables with kableExtra or gt functions, those functions do not always return printed raw HTML or LaTeX code. Sometimes, it can be necessary to add a call to cat at the end of a table customization pipeline. For example:\n{{{Rtable}}}\n#+BEGIN_SRC R :exports both\nlibrary(modelsummary)\nlibrary(kableExtra)\n\nmod = lm(hp ~ mpg, data = mtcars)\n\nmodelsummary(mod, output = orgbackend) %&gt;%\n  row_spec(1, background = \"pink\") %&gt;%\n  cat()\n#+END_SRC",
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-    "text": "To begin, load the modelsummary package and download data from the Rdatasets archive:\n\nlibrary(modelsummary)\nlibrary(tinytable)\n\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'\ndat &lt;- read.csv(url) \ndat$Small &lt;- dat$Pop1831 &gt; median(dat$Pop1831)\ndat &lt;- dat[, \n  c(\"Donations\", \"Literacy\", \"Commerce\", \"Crime_pers\", \"Crime_prop\", \"Clergy\", \"Small\")\n]\n\nData Summaries\nQuick overview of the data:\n\ndatasummary_skim(dat)\n\nWarning: The `replace_na` argument was renamed `replace`.\n\n\n \n\n  \n    \n\ntinytable_38zsoedied9ecmg1qdo8\n\n\n      \n\n \n                Unique\n                Missing Pct.\n                Mean\n                SD\n                Min\n                Median\n                Max\n                Histogram\n              \n\n\nDonations\n                  85\n                  0\n                  7075.5\n                  5834.6\n                  1246.0\n                  5020.0\n                  37015.0\n                  \n                \n\nLiteracy\n                  50\n                  0\n                  39.3\n                  17.4\n                  12.0\n                  38.0\n                  74.0\n                  \n                \n\nCommerce\n                  84\n                  0\n                  42.8\n                  25.0\n                  1.0\n                  42.5\n                  86.0\n                  \n                \n\nCrime_pers\n                  85\n                  0\n                  19754.4\n                  7504.7\n                  2199.0\n                  18748.5\n                  37014.0\n                  \n                \n\nCrime_prop\n                  86\n                  0\n                  7843.1\n                  3051.4\n                  1368.0\n                  7595.0\n                  20235.0\n                  \n                \n\nClergy\n                  85\n                  0\n                  43.4\n                  25.0\n                  1.0\n                  43.5\n                  86.0\n                  \n                \n\nSmall\n                  N\n                  %\n                  \n                  \n                  \n                  \n                  \n                  \n                \n\nFALSE\n                  43\n                  50.0\n                  \n                  \n                  \n                  \n                  \n                  \n                \n\nTRUE\n                  43\n                  50.0\n                  \n                  \n                  \n                  \n                  \n                  \n                \n\n\n\n\n    \n\n\nBalance table (aka “Table 1”) with differences in means by subgroups:\n\ndatasummary_balance(~Small, dat)\n\n \n\n  \n    \n\ntinytable_384z899wjzagdbs0nw2y\n\n\n      \n\n\n \nFALSE (N=43)\nTRUE (N=43)\n \n \n\n\n \n                Mean\n                Std. Dev.\n                Mean\n                Std. Dev.\n                Diff. in Means\n                Std. Error\n              \n\n\n\nDonations \n                  7258.5 \n                  6194.1\n                  6892.6 \n                  5519.0\n                  -365.9 \n                  1265.2\n                \n\nLiteracy  \n                  37.9   \n                  19.1  \n                  40.6   \n                  15.6  \n                  2.7    \n                  3.8   \n                \n\nCommerce  \n                  42.7   \n                  24.6  \n                  43.0   \n                  25.7  \n                  0.3    \n                  5.4   \n                \n\nCrime_pers\n                  18040.6\n                  7638.4\n                  21468.2\n                  7044.3\n                  3427.7 \n                  1584.6\n                \n\nCrime_prop\n                  8422.5 \n                  3406.7\n                  7263.7 \n                  2559.3\n                  -1158.8\n                  649.8 \n                \n\nClergy    \n                  39.1   \n                  26.7  \n                  47.7   \n                  22.7  \n                  8.6    \n                  5.3   \n                \n\n\n\n\n    \n\n\nCorrelation table:\n\ndatasummary_correlation(dat)\n\n \n\n  \n    \n\ntinytable_r92eaqgo94qsihqb2nz2\n\n\n      \n\n \n                Donations\n                Literacy\n                Commerce\n                Crime_pers\n                Crime_prop\n                Clergy\n              \n\n\nDonations \n                  1   \n                  .   \n                  .   \n                  .  \n                  .   \n                  .\n                \n\nLiteracy  \n                  -.13\n                  1   \n                  .   \n                  .  \n                  .   \n                  .\n                \n\nCommerce  \n                  .30 \n                  -.58\n                  1   \n                  .  \n                  .   \n                  .\n                \n\nCrime_pers\n                  -.04\n                  -.04\n                  .05 \n                  1  \n                  .   \n                  .\n                \n\nCrime_prop\n                  -.13\n                  -.37\n                  .41 \n                  .27\n                  1   \n                  .\n                \n\nClergy    \n                  .09 \n                  -.17\n                  -.12\n                  .26\n                  -.07\n                  1\n                \n\n\n\n\n    \n\n\nTwo variables and two statistics, nested in subgroups:\n\ndatasummary(Literacy + Commerce ~ Small * (mean + sd), dat)\n\n \n\n  \n    \n\ntinytable_79m5i46umqjzdlj0tv90\n\n\n      \n\n\n \nFALSE\nTRUE\n\n\n \n                mean\n                sd\n                mean\n                sd\n              \n\n\n\nLiteracy\n                  37.88\n                  19.08\n                  40.63\n                  15.57\n                \n\nCommerce\n                  42.65\n                  24.59\n                  42.95\n                  25.75\n                \n\n\n\n\n    \n\n\nModel Summaries\nEstimate a linear model and display the results:\n\nmod &lt;- lm(Donations ~ Crime_prop, data = dat)\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_266fvgi9qes3d7bgbf5m\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  9065.287  \n                \n\n           \n                  (1738.926)\n                \n\nCrime_prop \n                  -0.254    \n                \n\n           \n                  (0.207)   \n                \n\nNum.Obs.   \n                  86        \n                \n\nR2         \n                  0.018     \n                \n\nR2 Adj.    \n                  0.006     \n                \n\nAIC        \n                  1739.0    \n                \n\nBIC        \n                  1746.4    \n                \n\nLog.Lik.   \n                  -866.516  \n                \n\nF          \n                  1.505     \n                \n\nRMSE       \n                  5749.29   \n                \n\n\n\n\n    \n\n\nNow estimate five regression models, display the results side-by-side, and use the group_tt() function from the tinytable package to add column labels:\n\nlibrary(tinytable)\n\nmodels &lt;- list(\n    \"I\" = lm(Donations ~ Literacy + Clergy, data = dat),\n    \"II\" = lm(Crime_pers ~ Literacy + Clergy, data = dat),\n    \"III\" = lm(Crime_prop ~ Literacy + Clergy, data = dat),\n    \"IV\" = glm(Crime_pers ~ Literacy + Commerce, family = poisson, data = dat),\n    \"V\" = glm(Donations ~ Literacy + Commerce, family = poisson, data = dat)\n)\n\nmodelsummary(models, stars = TRUE, gof_omit = \"IC|Adj|F|RMSE|Log\") |&gt;\n    group_tt(j = list(\"Linear\" = 2:4, \"Poisson\" = 5:6))\n\n \n\n  \n    \n\ntinytable_v03brg46tmdcfyzugmzq\n\n\n      \n\n\n \nLinear\nPoisson\n\n\n \n                I\n                II\n                III\n                IV\n                V\n              \n\n+ p \n\n\n(Intercept)\n                  7948.667***\n                  16259.384***\n                  11243.544***\n                  9.876***\n                  8.241***\n                \n\n           \n                  (2078.276) \n                  (2611.140)  \n                  (1011.240)  \n                  (0.003) \n                  (0.006) \n                \n\nLiteracy   \n                  -39.121    \n                  3.680       \n                  -68.507***  \n                  0.000***\n                  0.003***\n                \n\n           \n                  (37.052)   \n                  (46.552)    \n                  (18.029)    \n                  (0.000) \n                  (0.000) \n                \n\nClergy     \n                  15.257     \n                  77.148*     \n                  -16.376     \n                          \n                          \n                \n\n           \n                  (25.735)   \n                  (32.334)    \n                  (12.522)    \n                          \n                          \n                \n\nCommerce   \n                             \n                              \n                              \n                  0.001***\n                  0.011***\n                \n\n           \n                             \n                              \n                              \n                  (0.000) \n                  (0.000) \n                \n\nNum.Obs.   \n                  86         \n                  86          \n                  86          \n                  86      \n                  86      \n                \n\nR2         \n                  0.020      \n                  0.065       \n                  0.152       \n                          \n                          \n                \n\n\n\n\n    \n\n\nNow, save it to a Microsoft Word document:\n\nmodelsummary(models, output = \"table.docx\")\n\nAnd draw a coefficient plot:\n\nols &lt;- models[1:3]\nmodelplot(ols, coef_omit = \"Intercept\")",
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+    "section": "Collapse control variables into an indicator",
+    "text": "Collapse control variables into an indicator\nIn some cases, analysts want to display a single checkmark in their table if a full set of control variables is available. This can easily be achieved with the glance_custom mechanism described in detail in the extension vignette. Here is a simple example:\n\nlibrary(modelsummary)\n\nmod &lt;- list(\n  glm(am ~ hp + factor(cyl), data = mtcars, family = binomial),\n  lm(mpg ~ hp + factor(cyl) + gear + qsec, data = mtcars)\n)\n\nglance_custom.lm &lt;- function(x, ...) {\n  controls &lt;- c(\"gear\", \"qsec\")\n  if (all(controls %in% names(coef(x)))) {\n    out &lt;- data.frame(Controls = \"✓\")\n  } else {\n    out &lt;- data.frame(Controls = \"✗\")\n  }\n  return(out)\n}\n\nglance_custom.glm &lt;- glance_custom.lm\n\nmodelsummary(mod, \"html\", coef_omit = \"qsec|gear\")\n\n \n\n  \n    \n\ntinytable_bgyur1ad2rtggxu697lm\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept) \n                  −2.029 \n                  33.178  \n                \n\n            \n                  (1.604)\n                  (13.713)\n                \n\nhp          \n                  0.038  \n                  −0.057  \n                \n\n            \n                  (0.019)\n                  (0.018) \n                \n\nfactor(cyl)6\n                  −2.874 \n                  −4.818  \n                \n\n            \n                  (1.360)\n                  (1.805) \n                \n\nfactor(cyl)8\n                  −8.644 \n                  −4.037  \n                \n\n            \n                  (3.423)\n                  (3.585) \n                \n\nNum.Obs.    \n                  32     \n                  32      \n                \n\nR2          \n                         \n                  0.812   \n                \n\nR2 Adj.     \n                         \n                  0.776   \n                \n\nAIC         \n                  35.2   \n                  165.2   \n                \n\nBIC         \n                  41.1   \n                  175.5   \n                \n\nLog.Lik.    \n                  −13.602\n                  −75.598 \n                \n\nF           \n                  2.514  \n                  22.526  \n                \n\nRMSE        \n                  0.38   \n                  2.57    \n                \n\nControls    \n                  ✗      \n                  ✓       \n                \n\n\n\n\n    \n\n\nReturn to nomal:\n\nrm(list = c(\"glance_custom.glm\", \"glance_custom.lm\"))",
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-    "text": "modelplot is a function from the modelsummary package. It allows you to plot model estimates and confidence intervals. It makes it easy to subset, rename, reorder, and customize plots using same mechanics as in modelsummary.\nTo illustrate how the function works, we fit a linear model to data about the Palmer Penguins:\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv'\ndat &lt;- read.csv(url)\n\n# rescale mm -&gt; cm\ndat$bill_length_cm &lt;- dat$bill_length_mm / 10\ndat$flipper_length_cm &lt;- dat$flipper_length_mm / 10\n\nmod &lt;- lm(bill_length_cm ~ flipper_length_cm + species, data = dat)\nThen, we load the modelsummary library and call modelplot:\nlibrary(modelsummary)\n\nmodelplot(mod)",
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+    "section": "Subgroup estimation with nest_by\n",
+    "text": "Subgroup estimation with nest_by\n\nSometimes, it is useful to estimate multiple regression models on subsets of the data. To do this efficiently, we can use the nest_by function from the dplyr package. Then, estimate the models with lm, extract them and name them with pull, and finally summarize them with modelsummary:\n\nlibrary(tidyverse)\n\nmtcars %&gt;%\n    nest_by(cyl) %&gt;%\n    mutate(models = list(lm(mpg ~ hp, data))) %&gt;%\n    pull(models, name = cyl) %&gt;%\n    modelsummary\n\n \n\n  \n    \n\ntinytable_dnpksjevzf413uytw944\n\n\n      \n\n \n                4\n                6\n                8\n              \n\n\n(Intercept)\n                  35.983 \n                  20.674 \n                  18.080 \n                \n\n           \n                  (5.201)\n                  (3.304)\n                  (2.988)\n                \n\nhp         \n                  -0.113 \n                  -0.008 \n                  -0.014 \n                \n\n           \n                  (0.061)\n                  (0.027)\n                  (0.014)\n                \n\nNum.Obs.   \n                  11     \n                  7      \n                  14     \n                \n\nR2         \n                  0.274  \n                  0.016  \n                  0.080  \n                \n\nR2 Adj.    \n                  0.193  \n                  -0.181 \n                  0.004  \n                \n\nAIC        \n                  65.8   \n                  29.9   \n                  69.8   \n                \n\nBIC        \n                  67.0   \n                  29.7   \n                  71.8   \n                \n\nLog.Lik.   \n                  -29.891\n                  -11.954\n                  -31.920\n                \n\nRMSE       \n                  3.66   \n                  1.33   \n                  2.37",
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-    "text": "Conditional colors and shape\nNote: This section requires a version of modelsummary greater than 1.2.0 or the development version.\nIn a very nice Stack Overflow answer, Allan Cameron shows how we can use the aes() function from ggplot2 to add conditional aesthetics. For example, if we want to display statistically significant coefficients in a different color:\n\nlibrary(ggplot2)\nmod &lt;- lm(hp ~ factor(gear) + factor(cyl), data = mtcars)\n\nmodelplot(mod, coef_rename = TRUE) +\n  aes(color = ifelse(p.value &lt; 0.001, \"Significant\", \"Not significant\")) +\n  scale_color_manual(values = c(\"grey\", \"black\"))",
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+    "text": "Bootstrap\nUsers often want to use estimates or standard errors that have been obtained using a custom strategy. To achieve this in an automated and replicable way, it can be useful to use the tidy_custom strategy described above in the “Cutomizing Existing Models” section.\nFor example, we can use the modelr package to draw 500 resamples of a dataset, and compute bootstrap standard errors by taking the standard deviation of estimates computed in all of those resampled datasets. To do this, we defined tidy_custom.lm function that will automatically bootstrap any lm model supplied to modelsummary, and replace the values in the table automatically.\nNote that the tidy_custom_lm returns a data.frame with 3 columns: term, estimate, and std.error:\n\nlibrary(\"broom\")\nlibrary(\"tidyverse\")\nlibrary(\"modelr\")\n\ntidy_custom.lm &lt;- function(x, ...) {\n  # extract data from the model\n  model.frame(x) %&gt;%\n    # draw 500 bootstrap resamples\n    modelr::bootstrap(n = 500) %&gt;%\n    # estimate the model 500 times\n    mutate(results = map(strap, ~ update(x, data = .))) %&gt;%\n    # extract results using `broom::tidy`\n    mutate(results = map(results, tidy)) %&gt;%\n    # unnest and summarize\n    unnest(results) %&gt;%\n    group_by(term) %&gt;%\n    summarize(std.error = sd(estimate),\n              estimate = mean(estimate))\n}\n\nmod = list(\n  lm(hp ~ mpg, mtcars) ,\n  lm(hp ~ mpg + drat, mtcars))\n\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_gnxqelpt3e58rikaxvvx\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  326.012 \n                  281.967 \n                \n\n           \n                  (30.638)\n                  (41.371)\n                \n\nmpg        \n                  -8.966  \n                  -10.205 \n                \n\n           \n                  (1.390) \n                  (2.339) \n                \n\ndrat       \n                          \n                  19.036  \n                \n\n           \n                          \n                  (20.568)\n                \n\nNum.Obs.   \n                  32      \n                  32      \n                \n\nR2         \n                  0.602   \n                  0.614   \n                \n\nR2 Adj.    \n                  0.589   \n                  0.588   \n                \n\nAIC        \n                  336.9   \n                  337.9   \n                \n\nBIC        \n                  341.3   \n                  343.7   \n                \n\nLog.Lik.   \n                  -165.428\n                  -164.940\n                \n\nF          \n                  45.460  \n                  23.100  \n                \n\nRMSE       \n                  42.55   \n                  41.91",
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-    "text": "Example: Using facets to compare models\nCustomizing plots with ggplot2 makes modelplot very flexible. For example, imagine you want to compare the coefficients of three models with different dependent variables. First, we load the packages and estimate our models:\n\nlibrary(ggplot2)\nlibrary(modelsummary)\n\nmodels &lt;- list(\n  lm(vs ~ carb + mpg + cyl, data = mtcars),\n  lm(disp ~ carb + mpg + cyl, data = mtcars),\n  lm(hp ~ carb + mpg + cyl, data = mtcars))\n\nThen, we use the dvnames function to rename our list with names matching the the dependent variable in each model:\n\nmodels &lt;- dvnames(models)\n\nBy calling modelplot with the draw=FALSE argument, we see the raw data used to draw the plot. Here, we see that there is a model column:\n\nmodelplot(models, draw = FALSE)\n\n          term model     estimate    std.error      conf.low    conf.high      p.value\n1  (Intercept)    vs   2.41742511   0.67622094    1.03224931   3.80260091 1.296718e-03\n5  (Intercept)  disp 112.57276339 114.86315481 -122.71374324 347.85927003 3.354494e-01\n9  (Intercept)    hp -10.56116383  68.75946117 -151.40853516 130.28620751 8.790301e-01\n2         carb    vs  -0.06945116   0.03943402   -0.15022810   0.01132577 8.912324e-02\n6         carb  disp -12.30144724   6.69827859  -26.02224894   1.41935446 7.692105e-02\n10        carb    hp  17.75593287   4.00972816    9.54237706  25.96948867 1.320972e-04\n3          mpg    vs  -0.01513960   0.01716410   -0.05029868   0.02001947 3.852593e-01\n7          mpg  disp  -7.14964651   2.91550156  -13.12178072  -1.17751230 2.068858e-02\n11         mpg    hp  -1.00486469   1.74527956   -4.57990780   2.57017842 5.693755e-01\n4          cyl    vs  -0.23926135   0.05687969   -0.35577411  -0.12274859 2.410214e-04\n8          cyl  disp  47.90105842   9.66160634   28.11015499  67.69196184 3.111898e-05\n12         cyl    hp  20.60581208   5.78363747    8.75856779  32.45305638 1.338485e-03\n\n\nFinally, we use the model column as our identifier in ggplot2’s facet_grid command to display models side by side:\n\nmodelplot(models, color = \"black\") + facet_grid(~model)",
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+    "title": "Model Summaries",
+    "section": "\nfixest: Fixed effects and instrumental variable regression",
+    "text": "fixest: Fixed effects and instrumental variable regression\nOne common use-case for glance_custom is to include additional goodness-of-fit statistics. For example, in an instrumental variable estimation computed by the fixest package, we may want to include an IV-Wald statistic for the first-stage regression of each endogenous regressor:\n\nlibrary(fixest)\nlibrary(tidyverse)\n\n# create a toy dataset\nbase &lt;- iris\nnames(base) &lt;- c(\"y\", \"x1\", \"x_endo_1\", \"x_inst_1\", \"fe\")\nbase$x_inst_2 &lt;- 0.2 * base$y + 0.2 * base$x_endo_1 + rnorm(150, sd = 0.5)\nbase$x_endo_2 &lt;- 0.2 * base$y - 0.2 * base$x_inst_1 + rnorm(150, sd = 0.5)\n\n# estimate an instrumental variable model\nmod &lt;- feols(y ~ x1 | fe | x_endo_1 + x_endo_2 ~ x_inst_1 + x_inst_2, base)\n\n# custom extractor function returns a one-row data.frame (or tibble)\nglance_custom.fixest &lt;- function(x) {\n  tibble(\n    \"Wald (x_endo_1)\" = fitstat(x, \"ivwald\")[[1]]$stat,\n    \"Wald (x_endo_2)\" = fitstat(x, \"ivwald\")[[2]]$stat\n  )\n}\n\n# draw table\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_vnnj050bm86wrx2jlr2i\n\n\n      \n\n \n                (1)\n              \n\n\nfit_x_endo_1   \n                  -0.174           \n                \n\n               \n                  (4.108)          \n                \n\nfit_x_endo_2   \n                  3.060            \n                \n\n               \n                  (11.055)         \n                \n\nx1             \n                  0.329            \n                \n\n               \n                  (0.508)          \n                \n\nNum.Obs.       \n                  150              \n                \n\nR2             \n                  -2.741           \n                \n\nR2 Adj.        \n                  -2.871           \n                \n\nR2 Within      \n                  -8.812           \n                \n\nR2 Within Adj. \n                  -9.017           \n                \n\nAIC            \n                  578.0            \n                \n\nBIC            \n                  596.1            \n                \n\nRMSE           \n                  1.60             \n                \n\nStd.Errors     \n                  by: fe           \n                \n\nFE: fe         \n                  X                \n                \n\nWald (x_endo_1)\n                  50.8615423860559 \n                \n\nWald (x_endo_2)\n                  0.581592469806361\n                \n\n\n\n\n    \n\n\n\nrm(\"glance_custom.fixest\")",
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-    "text": "Rounding with decimal digits on a per-statistic basis in the fmt argument for modelsummary()\n\n\n\nfmt_statistic(..., default = 3)\n\n\n\n\n\n\n\n…\n\n\nStatistic names and fmt value\n\n\n\n\ndefault\n\n\nNumber of decimal digits to keep for unspecified terms",
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+    "text": "Multiple imputation\nmodelsummary can pool and display analyses on several datasets imputed using the mice or Amelia packages. This code illustrates how:\n\nlibrary(mice)\n\nWarning in check_dep_version(): ABI version mismatch: \nlme4 was built with Matrix ABI version 2\nCurrent Matrix ABI version is 1\nPlease re-install lme4 from source or restore original 'Matrix' package\n\nlibrary(Amelia)\n\n# Download data from `Rdatasets`\nurl &lt;- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'\ndat &lt;- read.csv(url)[, c('Clergy', 'Commerce', 'Literacy')]\n\n# Insert missing values\ndat$Clergy[sample(1:nrow(dat), 10)] &lt;- NA\ndat$Commerce[sample(1:nrow(dat), 10)] &lt;- NA\ndat$Literacy[sample(1:nrow(dat), 10)] &lt;- NA\n\n# Impute with `mice` and `Amelia`\ndat_mice &lt;- mice(dat, m = 5, printFlag = FALSE)\ndat_amelia &lt;- amelia(dat, m = 5, p2s = 0)$imputations\n\n# Estimate models\nmod &lt;- list()\nmod[['Listwise deletion']] &lt;- lm(Clergy ~ Literacy + Commerce, dat)\nmod[['Mice']] &lt;- with(dat_mice, lm(Clergy ~ Literacy + Commerce)) \nmod[['Amelia']] &lt;- lapply(dat_amelia, function(x) lm(Clergy ~ Literacy + Commerce, x))\n\n# Pool results\nmod[['Mice']] &lt;- mice::pool(mod[['Mice']])\nmod[['Amelia']] &lt;- mice::pool(mod[['Amelia']])\n\n# Summarize\nmodelsummary(mod)\n\n \n\n  \n    \n\ntinytable_3a5oeypthhhlna2s2bdl\n\n\n      \n\n \n                Listwise deletion\n                Mice\n                Amelia\n              \n\n\n(Intercept)\n                  81.719  \n                  75.699  \n                  79.645  \n                \n\n           \n                  (14.584)\n                  (13.342)\n                  (13.468)\n                \n\nLiteracy   \n                  -0.638  \n                  -0.499  \n                  -0.541  \n                \n\n           \n                  (0.234) \n                  (0.205) \n                  (0.216) \n                \n\nCommerce   \n                  -0.305  \n                  -0.283  \n                  -0.331  \n                \n\n           \n                  (0.168) \n                  (0.145) \n                  (0.148) \n                \n\nNum.Obs.   \n                  59      \n                  86      \n                  86      \n                \n\nNum.Imp.   \n                          \n                  5       \n                  5       \n                \n\nR2         \n                  0.121   \n                  0.078   \n                  0.102   \n                \n\nR2 Adj.    \n                  0.089   \n                  0.055   \n                  0.079   \n                \n\nAIC        \n                  557.0   \n                          \n                          \n                \n\nBIC        \n                  565.3   \n                          \n                          \n                \n\nLog.Lik.   \n                  -274.488\n                          \n                          \n                \n\nF          \n                  3.843   \n                          \n                          \n                \n\nRMSE       \n                  25.37",
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-    "text": "Rounding with decimal digits on a per-statistic basis in the fmt argument for modelsummary()\n\n\n\nfmt_statistic(..., default = 3)\n\n\n\n\n\n\n\n…\n\n\nStatistic names and fmt value\n\n\n\n\ndefault\n\n\nNumber of decimal digits to keep for unspecified terms",
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+    "title": "Model Summaries",
+    "section": "Stack Overflow is useful",
+    "text": "Stack Overflow is useful\n\nStandardized coefficients\nRow group labels\nCustomizing Word tables\nHow to add p values to datasummary_correlation",
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-    "text": "A convenience function for use with a regression model or list of regression models. Returns a named list of models, where the names are the models’ respective dependent variables. Pass your list of models to dvnames before sending to modelsummary to automatically get dependent variable-titled columns.\n\ndvnames(models, number = FALSE, strip = FALSE, fill = \"Model\")\n\n\n\n\n\nmodels\n\n\nA regression model or list of regression models\n\n\n\n\nnumber\n\n\nShould the models be numbered (1), (2), etc., in addition to their dependent variable names?\n\n\n\n\nstrip\n\n\nboolean FALSE returns the dependent variable names as they appear in the model. TRUE returns the dependent variable names as they appear in the data, without transformations.\n\n\n\n\nfill\n\n\nIf insight::find_response() cannot find a response, the column title to use in its place. Set to ’ ’ to leave blank.\n\n\n\n\nlibrary(modelsummary)\n\n\nm1 &lt;- lm(mpg ~ hp, data = mtcars)\nm2 &lt;- lm(mpg ~ hp + wt, data = mtcars)\n\n# Without dvnames, column names are (1) and (2)\nmodelsummary(list(m1, m2))\n\n \n\n  \n    \n\ntinytable_z26tymp7i2bbybq52sa0\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47   \n                \n\n\n\n\n    \n\n# With dvnames, they are \"mpg\" and \"mpg\"\nmodelsummary(dvnames(list(m1,m2)))\n\n \n\n  \n    \n\ntinytable_5fvwbd5zmtygbbzx5xpe\n\n\n      \n\n \n                mpg\n                mpg \n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47",
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+    "section": "Where can I get help?",
+    "text": "Where can I get help?\nFirst, please read the documentation in ?modelsummary and on the modelsummary website. The website includes dozens of worked examples and a lot of detailed explanation.\nSecond, try to use the [modelsummary] tag on StackOverflow.\nThird, if you think you found a bug or have a feature request, please file it on the Github issue tracker:",
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-    "text": "A convenience function for use with a regression model or list of regression models. Returns a named list of models, where the names are the models’ respective dependent variables. Pass your list of models to dvnames before sending to modelsummary to automatically get dependent variable-titled columns.\n\ndvnames(models, number = FALSE, strip = FALSE, fill = \"Model\")\n\n\n\n\n\nmodels\n\n\nA regression model or list of regression models\n\n\n\n\nnumber\n\n\nShould the models be numbered (1), (2), etc., in addition to their dependent variable names?\n\n\n\n\nstrip\n\n\nboolean FALSE returns the dependent variable names as they appear in the model. TRUE returns the dependent variable names as they appear in the data, without transformations.\n\n\n\n\nfill\n\n\nIf insight::find_response() cannot find a response, the column title to use in its place. Set to ’ ’ to leave blank.\n\n\n\n\nlibrary(modelsummary)\n\n\nm1 &lt;- lm(mpg ~ hp, data = mtcars)\nm2 &lt;- lm(mpg ~ hp + wt, data = mtcars)\n\n# Without dvnames, column names are (1) and (2)\nmodelsummary(list(m1, m2))\n\n \n\n  \n    \n\ntinytable_z26tymp7i2bbybq52sa0\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47   \n                \n\n\n\n\n    \n\n# With dvnames, they are \"mpg\" and \"mpg\"\nmodelsummary(dvnames(list(m1,m2)))\n\n \n\n  \n    \n\ntinytable_5fvwbd5zmtygbbzx5xpe\n\n\n      \n\n \n                mpg\n                mpg \n              \n\n\n(Intercept)\n                  30.099 \n                  37.227 \n                \n\n           \n                  (1.634)\n                  (1.599)\n                \n\nhp         \n                  -0.068 \n                  -0.032 \n                \n\n           \n                  (0.010)\n                  (0.009)\n                \n\nwt         \n                         \n                  -3.878 \n                \n\n           \n                         \n                  (0.633)\n                \n\nNum.Obs.   \n                  32     \n                  32     \n                \n\nR2         \n                  0.602  \n                  0.827  \n                \n\nR2 Adj.    \n                  0.589  \n                  0.815  \n                \n\nAIC        \n                  181.2  \n                  156.7  \n                \n\nBIC        \n                  185.6  \n                  162.5  \n                \n\nLog.Lik.   \n                  -87.619\n                  -74.326\n                \n\nF          \n                  45.460 \n                  69.211 \n                \n\nRMSE       \n                  3.74   \n                  2.47",
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+    "title": "Model Summaries",
+    "section": "How can I add or modify statistics in a table?",
+    "text": "How can I add or modify statistics in a table?\nSee the detailed documentation in the “Adding and Customizing Models” section of the modelsummary website.",
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-    "text": "Rounding with decimal digits in the fmt argument\n\n\n\nfmt_decimal(digits = 3, pdigits = NULL, ...)\n\n\n\n\n\n\n\ndigits\n\n\nNumber of decimal digits to keep, including trailing zeros.\n\n\n\n\npdigits\n\n\nNumber of decimal digits to keep for p values. If NULL, the value of digits is used.\n\n\n\n\n…\n\n\nAdditional arguments are passed to the format() function (e.g., big.marks, scientific). See ?format",
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+    "title": "Model Summaries",
+    "section": "How does modelsummary extract estimates and goodness-of-fit statistics?",
+    "text": "How does modelsummary extract estimates and goodness-of-fit statistics?\nA modelsummary table is divided in two parts: “Estimates” (top of the table) and “Goodness-of-fit” (bottom of the table). To populate those two parts, modelsummary tries using the broom, parameters and performance packages in sequence.\nEstimates:\n\nTry the broom::tidy function to see if that package supports this model type, or if the user defined a custom tidy function in their global environment. If this fails…\nTry the parameters::model_parameters function to see if the parameters package supports this model type.\n\nGoodness-of-fit:\n\nTry the performance::model_performance function to see if the performance package supports this model type.\nTry the broom::glance function to see if that package supports this model type, or if the user defined a custom glance function in their global environment. If this fails…\n\nYou can change the order in which those steps are executed by setting a global option:\n\n# tidymodels: broom \noptions(modelsummary_get = \"broom\")\n\n# easystats: performance + parameters\noptions(modelsummary_get = \"easystats\")\n\nIf all of this fails, modelsummary will return an error message.\nIf you have problems with a model object, you can often diagnose the problem by running the following commands from a clean R session:\n# see if parameters and performance support your model type\nlibrary(parameters)\nlibrary(performance)\nmodel_parameters(model)\nmodel_performance(model)\n\n# see if broom supports your model type\nlibrary(broom)\ntidy(model)\nglance(model)\n\n# see if broom.mixed supports your model type\nlibrary(broom.mixed)\ntidy(model)\nglance(model)\nIf none of these options work, you can create your own tidy and glance methods, as described in the Adding new models section.\nIf one of the extractor functions does not work well or takes too long to process, you can define a new “custom” model class and choose your own extractors, as described in the Adding new models section.",
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-    "text": "Rounding with decimal digits in the fmt argument\n\n\n\nfmt_decimal(digits = 3, pdigits = NULL, ...)\n\n\n\n\n\n\n\ndigits\n\n\nNumber of decimal digits to keep, including trailing zeros.\n\n\n\n\npdigits\n\n\nNumber of decimal digits to keep for p values. If NULL, the value of digits is used.\n\n\n\n\n…\n\n\nAdditional arguments are passed to the format() function (e.g., big.marks, scientific). See ?format",
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+    "section": "How can I speed up modelsummary?",
+    "text": "How can I speed up modelsummary?\nThe modelsummary function, by itself, is not slow: it should only take a couple seconds to produce a table in any output format. However, sometimes it can be computationally expensive (and long) to extract estimates and to compute goodness-of-fit statistics for your model.\nThe main options to speed up modelsummary are:\n\nSet gof_map=NA to avoid computing expensive goodness-of-fit statistics.\nUse the easystats extractor functions and the metrics argument to avoid computing expensive statistics (see below for an example).\nUse parallel computation if you are summarizing multiple models. See the “Parallel computation” section in the ?modelsummary documentation.\n\nTo diagnose the slowdown and find the bottleneck, you can try to benchmark the various extractor functions:\n\nlibrary(tictoc)\n\ndata(trade)\nmod &lt;- lm(mpg ~ hp + drat, mtcars)\n\ntic(\"tidy\")\nx &lt;- broom::tidy(mod)\ntoc()\n\ntidy: 0.003 sec elapsed\n\ntic(\"glance\")\nx &lt;- broom::glance(mod)\ntoc()\n\nglance: 0.004 sec elapsed\n\ntic(\"parameters\")\nx &lt;- parameters::parameters(mod)\ntoc()\n\nparameters: 0.026 sec elapsed\n\ntic(\"performance\")\nx &lt;- performance::performance(mod)\ntoc()\n\nperformance: 0.014 sec elapsed\n\n\nIn my experience, the main bottleneck tends to be computing goodness-of-fit statistics. The performance extractor allows users to specify a metrics argument to select a subset of GOF to include. Using this can speedup things considerably.\nWe call modelsummary with the metrics argument:\n\nmodelsummary(mod, metrics = \"rmse\")\n\n \n\n  \n    \n\ntinytable_wbc4i00xeuv5562ue5j1\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  10.790 \n                \n\n           \n                  (5.078)\n                \n\nhp         \n                  -0.052 \n                \n\n           \n                  (0.009)\n                \n\ndrat       \n                  4.698  \n                \n\n           \n                  (1.192)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.741  \n                \n\nR2 Adj.    \n                  0.723  \n                \n\nAIC        \n                  169.5  \n                \n\nBIC        \n                  175.4  \n                \n\nLog.Lik.   \n                  -80.752\n                \n\nF          \n                  41.522",
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-    "text": "A convenience function which can be passed to the coef_rename argument of the modelsummary function.\n\ncoef_rename(\n  x,\n  factor = TRUE,\n  factor_name = TRUE,\n  backticks = TRUE,\n  titlecase = TRUE,\n  underscore = TRUE,\n  asis = TRUE\n)\n\n\n\n\n\nx\n\n\ncharacter vector of term names to transform\n\n\n\n\nfactor\n\n\nboolean remove the \"factor()\" label\n\n\n\n\nfactor_name\n\n\nboolean remove the \"factor()\" label and the name of the variable\n\n\n\n\nbackticks\n\n\nboolean remove backticks\n\n\n\n\ntitlecase\n\n\nboolean convert to title case\n\n\n\n\nunderscore\n\n\nboolean replace underscores by spaces\n\n\n\n\nasis\n\n\nboolean remove the I from as-is formula calls\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\ndat &lt;- mtcars\ndat$horse_power &lt;- dat$hp\nmod &lt;- lm(mpg ~ horse_power + factor(cyl), dat)\nmodelsummary(mod, coef_rename = coef_rename)\n\n \n\n  \n    \n\ntinytable_txz8kask14tr5zhqsta0\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  28.650 \n                \n\n           \n                  (1.588)\n                \n\nHorse Power\n                  -0.024 \n                \n\n           \n                  (0.015)\n                \n\n6          \n                  -5.968 \n                \n\n           \n                  (1.639)\n                \n\n8          \n                  -8.521 \n                \n\n           \n                  (2.326)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.754  \n                \n\nR2 Adj.    \n                  0.727  \n                \n\nAIC        \n                  169.9  \n                \n\nBIC        \n                  177.2  \n                \n\nLog.Lik.   \n                  -79.948\n                \n\nF          \n                  28.585 \n                \n\nRMSE       \n                  2.94",
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+    "text": "Escaped LaTeX characters\nSometimes, users want to include raw LaTeX commands in their tables, such as coefficient names including math mode: Apple $\\times$ Orange. The result of these attempts is often a weird string such as: \\$\\textbackslash{}times\\$ instead of proper LaTeX-rendered characters.\nThe source of the problem is that kableExtra, default table-making package in modelsummary, automatically escapes weird characters to make sure that your tables compile properly in LaTeX. To avoid this, we need to pass the escape=FALSE to modelsummary:\n\nmodelsummary(mod, escape = FALSE)",
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-    "text": "A convenience function which can be passed to the coef_rename argument of the modelsummary function.\n\ncoef_rename(\n  x,\n  factor = TRUE,\n  factor_name = TRUE,\n  backticks = TRUE,\n  titlecase = TRUE,\n  underscore = TRUE,\n  asis = TRUE\n)\n\n\n\n\n\nx\n\n\ncharacter vector of term names to transform\n\n\n\n\nfactor\n\n\nboolean remove the \"factor()\" label\n\n\n\n\nfactor_name\n\n\nboolean remove the \"factor()\" label and the name of the variable\n\n\n\n\nbackticks\n\n\nboolean remove backticks\n\n\n\n\ntitlecase\n\n\nboolean convert to title case\n\n\n\n\nunderscore\n\n\nboolean replace underscores by spaces\n\n\n\n\nasis\n\n\nboolean remove the I from as-is formula calls\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\ndat &lt;- mtcars\ndat$horse_power &lt;- dat$hp\nmod &lt;- lm(mpg ~ horse_power + factor(cyl), dat)\nmodelsummary(mod, coef_rename = coef_rename)\n\n \n\n  \n    \n\ntinytable_txz8kask14tr5zhqsta0\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  28.650 \n                \n\n           \n                  (1.588)\n                \n\nHorse Power\n                  -0.024 \n                \n\n           \n                  (0.015)\n                \n\n6          \n                  -5.968 \n                \n\n           \n                  (1.639)\n                \n\n8          \n                  -8.521 \n                \n\n           \n                  (2.326)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.754  \n                \n\nR2 Adj.    \n                  0.727  \n                \n\nAIC        \n                  169.9  \n                \n\nBIC        \n                  177.2  \n                \n\nLog.Lik.   \n                  -79.948\n                \n\nF          \n                  28.585 \n                \n\nRMSE       \n                  2.94",
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+    "text": "Bayesian models\nMany bayesian models are supported out-of-the-box, including those produced by the rstanarm and brms packages. The statistics available for bayesian models are slightly different than those available for most frequentist models. Users can call get_estimates to see what is available:\n\nlibrary(rstanarm)\n\nThis is rstanarm version 2.32.1\n\n\n- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!\n\n\n- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.\n\n\n- For execution on a local, multicore CPU with excess RAM we recommend calling\n\n\n  options(mc.cores = parallel::detectCores())\n\n\n\nAttaching package: 'rstanarm'\n\n\nThe following object is masked from 'package:fixest':\n\n    se\n\nmod &lt;- stan_glm(am ~ hp + drat, data = mtcars)\n\n\nget_estimates(mod)\n\n         term      estimate         mad conf.level     conf.low    conf.high prior.distribution prior.location prior.scale group std.error statistic p.value\n1 (Intercept) -2.2246277000 0.599969846       0.95 -3.433826756 -1.055312644             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006919802 0.001109785       0.95 -0.001499742  0.002809575             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.7030982373 0.139586860       0.95  0.434089902  0.984676752             normal        0.00000  2.33313429              NA        NA      NA\n\n\nThis shows that there is no std.error column, but that there is a mad statistic (mean absolute deviation). So we can do:\n\nmodelsummary(mod, statistic = \"mad\")\n\nWarning: \n`modelsummary` uses the `performance` package to extract goodness-of-fit\nstatistics from models of this class. You can specify the statistics you wish\nto compute by supplying a `metrics` argument to `modelsummary`, which will then\npush it forward to `performance`. Acceptable values are: \"all\", \"common\",\n\"none\", or a character vector of metrics names. For example: `modelsummary(mod,\nmetrics = c(\"RMSE\", \"R2\")` Note that some metrics are computationally\nexpensive. See `?performance::performance` for details.\n This warning appears once per session.\n\n\n \n\n  \n    \n\ntinytable_qnegdj09ycari5ihgf74\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  -2.225 \n                \n\n           \n                  (0.600)\n                \n\nhp         \n                  0.001  \n                \n\n           \n                  (0.001)\n                \n\ndrat       \n                  0.703  \n                \n\n           \n                  (0.140)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.497  \n                \n\nR2 Adj.    \n                  0.431  \n                \n\nLog.Lik.   \n                  -12.071\n                \n\nELPD       \n                  -15.2  \n                \n\nELPD s.e.  \n                  3.1    \n                \n\nLOOIC      \n                  30.4   \n                \n\nLOOIC s.e. \n                  6.2    \n                \n\nWAIC       \n                  30.1   \n                \n\nRMSE       \n                  0.34   \n                \n\n\n\n\n    \n\n\nAs noted in the modelsummary() documentation, model results are extracted using the parameters package. Users can pass additional arguments to modelsummary(), which will then push forward those arguments to the parameters::parameters function to change the results. For example, the parameters documentation for bayesian models shows that there is a centrality argument, which allows users to report the mean and standard deviation of the posterior distribution, instead of the median and MAD:\n\nget_estimates(mod, centrality = \"mean\")\n\n         term      estimate     std.dev conf.level     conf.low    conf.high prior.distribution prior.location prior.scale group std.error statistic p.value\n1 (Intercept) -2.2256516592 0.604144261       0.95 -3.433826756 -1.055312644             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006990604 0.001121095       0.95 -0.001499742  0.002809575             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.7031661013 0.140682478       0.95  0.434089902  0.984676752             normal        0.00000  2.33313429              NA        NA      NA\n\nmodelsummary(mod, statistic = \"std.dev\", centrality = \"mean\")\n\n \n\n  \n    \n\ntinytable_yo5obmqtlumjxzrgh4yf\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  -2.226 \n                \n\n           \n                  (0.604)\n                \n\nhp         \n                  0.001  \n                \n\n           \n                  (0.001)\n                \n\ndrat       \n                  0.703  \n                \n\n           \n                  (0.141)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.497  \n                \n\nR2 Adj.    \n                  0.431  \n                \n\nLog.Lik.   \n                  -12.071\n                \n\nELPD       \n                  -15.2  \n                \n\nELPD s.e.  \n                  3.1    \n                \n\nLOOIC      \n                  30.4   \n                \n\nLOOIC s.e. \n                  6.2    \n                \n\nWAIC       \n                  30.1   \n                \n\nRMSE       \n                  0.34   \n                \n\n\n\n\n    \n\n\nWe can also get additional test statistics using the test argument:\n\nget_estimates(mod, test = c(\"pd\", \"rope\"))\n\n         term      estimate         mad conf.level     conf.low    conf.high     pd rope.percentage prior.distribution prior.location prior.scale group std.error statistic p.value\n1 (Intercept) -2.2246277000 0.599969846       0.95 -3.433826756 -1.055312644 0.9995               0             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006919802 0.001109785       0.95 -0.001499742  0.002809575 0.7330               1             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.7030982373 0.139586860       0.95  0.434089902  0.984676752 1.0000               0             normal        0.00000  2.33313429              NA        NA      NA",
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-    "text": "Rounding with the sprintf() function in the fmt argument\n\n\n\nfmt_sprintf(fmt)\n\n\n\n\n\n\n\nfmt\n\n\nA string to control sprintf(), such as “%.3f” to keep 3 decimal digits. See ?sprintf",
+    "text": "datasummary is a function from the modelsummary package. It allows us to create data summaries, frequency tables, crosstabs, correlation tables, balance tables (aka “Table 1”), and more. It has many benefits:\nThis tutorial will show how to draw tables like these (and more!):\ndatasummary is built around the fantastic tables package for R. It is a thin “wrapper” which adds convenience functions and arguments; a user-interface consistent with modelsummary; cleaner html output; and the ability to export tables to more formats, including gt tables, flextable objects, and Microsoft Word documents.\ndatasummary is a general-purpose table-making tool. It allows us to build (nearly) any summary table we want by using simple 2-sided formulae. For example, in the expression x + y ~ mean + sd, the left-hand side of the formula identifies the variables or statistics to display as rows, and the right-hand side defines the columns. Below, we will see how variables and statistics can be “nested” with the * operator to produce tables like the ones above.\nIn addition to datasummary, the modelsummary package includes a “family” of companion functions named datasummary_*. These functions facilitate the production of standard, commonly used tables. This family currently includes:\nIn the next three sections, we illustrate how to use datasummary_balance, datasummary_correlation, datasummary_skim, and datasummary_crosstab. Then, we dive into datasummary itself to highlight its ease and flexibility.",
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-    "text": "Rounding with the sprintf() function in the fmt argument\n\n\n\nfmt_sprintf(fmt)\n\n\n\n\n\n\n\nfmt\n\n\nA string to control sprintf(), such as “%.3f” to keep 3 decimal digits. See ?sprintf",
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+    "text": "Custom summary functions\nThe Mean function is a shortcut supplied by modelsummary, and it is equivalent to mean(x,na.rm=TRUE). Since the flipper_length_mm variable includes missing observation, using the mean formula (with default na.rm=FALSE) would produce a missing/empty cell:\n\ndatasummary(flipper_length_mm ~ mean,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_j5v7hmg8vynw9ut2e8lx\n\n\n      \n\n \n                mean\n              \n\nflipper_length_mm\n                  \n                \n\n\n\n    \n\n\nmodelsummary supplies these functions: Mean, SD, Min, Max, Median, P0, P25, P50, P75, P100, Histogram, and a few more (see the package documentation).\nUsers are also free to create and use their own custom summaries. Any R function which takes a vector and produces a single value is acceptable. For example, the Range functions return a numerical value, and the MinMax returns a string:\n\nRange &lt;- function(x) max(x, na.rm = TRUE) - min(x, na.rm = TRUE)\n\ndatasummary(flipper_length_mm ~ Range,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_7mdf14epg6tplt7461i1\n\n\n      \n\n \n                Range\n              \n\nflipper_length_mm\n                  59.00\n                \n\n\n\n    \n\nMinMax &lt;- function(x) paste0('[', min(x, na.rm = TRUE), ', ', max(x, na.rm = TRUE), ']')\n\ndatasummary(flipper_length_mm ~ MinMax,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_078za3ozts6f5b69kczq\n\n\n      \n\n \n                MinMax\n              \n\nflipper_length_mm\n                  [172, 231]",
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-    "text": "Create beautiful and customizable tables to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, Typst, PDF, PowerPoint, Excel, RTF, JPG, or PNG. The appearance of the tables can be customized extensively by specifying the output argument, and by using functions from one of the supported table customization packages: tinytable, kableExtra, gt, flextable, huxtable, DT. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nThe modelsummary Vignette includes dozens of examples of tables with extensive customizations.\n\n\nThe Appearance Vignette shows how to modify the look of tables.\n\n\nmodelsummary(\n  models,\n  output = \"default\",\n  fmt = 3,\n  estimate = \"estimate\",\n  statistic = \"std.error\",\n  vcov = NULL,\n  conf_level = 0.95,\n  exponentiate = FALSE,\n  stars = FALSE,\n  shape = term + statistic ~ model,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = FALSE,\n  gof_map = NULL,\n  gof_omit = NULL,\n  gof_function = NULL,\n  group_map = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  align = NULL,\n  notes = NULL,\n  title = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_decimal(digits = 2, pdigits = 3): Decimal digits for estimate and p values\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_term(“(Intercept)” = 1, “X” = 2): Format terms differently\n\n\nfmt = fmt_statistic(“estimate” = 1, “r.squared” = 6): Format statistics differently.\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\nstring:\n\n\nNote on LaTeX output: To ensure proper typography, all numeric entries are enclosed in the  command, which requires the siunitx package to be loaded in the LaTeX preamble. This behavior can be altered with global options. See the ‘Details’ section.\n\n\n\n\n\n\nestimate\n\n\na single string or a character vector of length equal to the number of models. Valid entries include any column name of the data.frame produced by get_estimates(model), and strings with curly braces compatible with the glue package format. Examples:\n\n\n“estimate”\n\n\n“{estimate} ({std.error}){stars}”\n\n\n“{estimate} [{conf.low}, {conf.high}]”\n\n\n\n\n\n\nstatistic\n\n\nvector of strings or glue strings which select uncertainty statistics to report vertically below the estimate. NULL omits all uncertainty statistics.\n\n\n\"conf.int\", \"std.error\", \"statistic\", \"p.value\", \"conf.low\", \"conf.high\", or any column name produced by get_estimates(model)\n\n\nglue package strings with braces, with or without R functions, such as:\n\n\n“{p.value} [{conf.low}, {conf.high}]”\n\n\n“Std.Error: {std.error}”\n\n\n“{exp(estimate) * std.error}”\n\n\nNumbers are automatically rounded and converted to strings. To apply functions to their numeric values, as in the last glue example, users must set fmt=NULL.\n\n\nParentheses are added automatically unless the string includes glue curly braces {}.\n\n\n\n\nNotes:\n\n\nThe names of the statistic are used a column names when using the shape argument to display statistics as columns:\n\n\nstatistic=c(“p”=“p.value”, “[”=”conf.low”, ”]”=“conf.high”)\n\n\n\n\nSome statistics are not supported for all models. See column names in get_estimates(model), and visit the website to learn how to add custom statistics.\n\n\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nstars\n\n\nto indicate statistical significance\n\n\nFALSE (default): no significance stars.\n\n\nTRUE: +=.1, =.05, =.01, =0.001\n\n\nNamed numeric vector for custom stars such as c(’*’ = .1, ‘+’ = .05)\n\n\nNote: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use \"glue strings\" with stars.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\ngof_map\n\n\nrename, reorder, and omit goodness-of-fit statistics and other model information. This argument accepts 4 types of values:\n\n\nNULL (default): the modelsummary::gof_map dictionary is used for formatting, and all unknown statistic are included.\n\n\ncharacter vector: \"all\", \"none\", or a vector of statistics such as c(“rmse”, “nobs”, “r.squared”). Elements correspond to colnames in the data.frame produced by get_gof(model). The modelsummary::gof_map default dictionary is used to format and rename statistics.\n\n\nNA: excludes all statistics from the bottom part of the table.\n\n\ndata.frame with 3 columns named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples’ section below. The fmt column in this data frame only accepts integers. For more flexibility, use a list of lists, as described in the next bullet.\n\n\nlist of lists, each of which includes 3 elements named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples section below’.\n\n\n\n\n\n\ngof_omit\n\n\nstring regular expression (perl-compatible) used to determine which statistics to omit from the bottom section of the table. A \"negative lookahead\" can be used to specify which statistics to keep in the table. Examples:\n\n\n“IC”: omit statistics matching the \"IC\" substring.\n\n\n“BIC|AIC”: omit statistics matching the \"AIC\" or \"BIC\" substrings.\n\n\n“^(?!.*IC)“: keep statistics matching the”IC\" substring.\n\n\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\ngroup_map\n\n\nnamed or unnamed character vector. Subset, rename, and reorder coefficient groups specified a grouping variable specified in the shape argument formula. This argument behaves like coef_map.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as #’ your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the columns positions. See Examples section below.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nThe modelsummary_list output is a lightweight format which can be used to save model results, so they can be fed back to modelsummary later to avoid extracting results again.\nWhen a file name with a valid extension is supplied to the output argument, the table is written immediately to file. If you want to customize your table by post-processing it with an external package, you need to choose a different output format and saving mechanism. Unfortunately, the approach differs from package to package:\n\n\ntinytable: set output=“tinytable”, post-process your table, and use the tinytable::save_tt function.\n\n\ngt: set output=“gt”, post-process your table, and use the gt::gtsave function.\n\n\nkableExtra: set output to your destination format (e.g., \"latex\", \"html\", \"markdown\"), post-process your table, and use kableExtra::save_kable function.\n\n\n\nTo use a string such as \"robust\" or \"HC0\", your model must be supported by the sandwich package. This includes objects such as: lm, glm, survreg, coxph, mlogit, polr, hurdle, zeroinfl, and more.\nNULL, \"classical\", \"iid\", and \"constant\" are aliases which do not modify uncertainty estimates and simply report the default standard errors stored in the model object.\nOne-sided formulas such as ~clusterid are passed to the sandwich::vcovCL function.\nMatrices and functions producing variance-covariance matrices are first passed to lmtest. If this does not work, modelsummary attempts to take the square root of the diagonal to adjust \"std.error\", but the other uncertainty estimates are not be adjusted.\nNumeric vectors are formatted according to fmt and placed in brackets. Character vectors printed as given, without parentheses.\nIf your model type is supported by the lmtest package, the vcov argument will try to use that package to adjust all the uncertainty estimates, including \"std.error\", \"statistic\", \"p.value\", and \"conf.int\". If your model is not supported by lmtest, only the \"std.error\" will be adjusted by, for example, taking the square root of the matrix’s diagonal.\n\na regression table in a format determined by the output argument.\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nIt can take a long time to compute and extract summary statistics from certain models (e.g., Bayesian). In those cases, users can parallelize the process. Since parallelization occurs at the model level, no speedup is available for tables with a single model. Users on mac or linux can launch parallel computation using the built-in parallel package. All they need to do is supply a mc.cores argument which will be pushed forward to the parallel::mclapply function:\n\nmodelsummary(model_list, mc.cores = 5)\n\n\nAll users can also use the future.apply package to parallelize model summaries. For example, to use 4 cores to extract results:\n\nlibrary(future.apply)\nplan(multicore, workers = 4)\noptions(\"modelsummary_future\" = TRUE)\nmodelsummary(model_list)\n\n\nNote that the \"multicore\" plan only parallelizes under mac or linux. Windows users can use plan(multisession) instead. However, note that the first time modelsummary() is called under multisession can be a fair bit longer, because of extra costs in passing data to and loading required packages on to workers. Subsequent calls to modelsummary() will often be much faster.\nSome users have reported difficult to reproduce errors when using the future package with some packages. The future parallelization in modelsummary can be disabled by calling:\noptions(“modelsummary_future” = FALSE)\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\n# The `modelsummary` website includes \\emph{many} examples and tutorials:\n# https://modelsummary.com\n\nlibrary(modelsummary)\n\n# load data and estimate models\nutils::data(trees)\nmodels &lt;- list()\nmodels[['Bivariate']] &lt;- lm(Girth ~ Height, data = trees)\nmodels[['Multivariate']] &lt;- lm(Girth ~ Height + Volume, data = trees)\n\n# simple table\nmodelsummary(models)\n\n# statistic\nmodelsummary(models, statistic = NULL)\n\nmodelsummary(models, statistic = 'p.value')\n\nmodelsummary(models, statistic = 'statistic')\n\nmodelsummary(models, statistic = 'conf.int', conf_level = 0.99)\n\nmodelsummary(models, statistic = c(\"t = {statistic}\",\n                                   \"se = {std.error}\",\n                                   \"conf.int\"))\n\n# estimate\nmodelsummary(models,\n  statistic = NULL,\n  estimate = \"{estimate} [{conf.low}, {conf.high}]\")\n\nmodelsummary(models,\n  estimate = c(\"{estimate}{stars}\",\n               \"{estimate} ({std.error})\"))\n\n# vcov\nmodelsummary(models, vcov = \"robust\")\n\nmodelsummary(models, vcov = list(\"classical\", \"stata\"))\n\nmodelsummary(models, vcov = sandwich::vcovHC)\n\nmodelsummary(models,\n  vcov = list(stats::vcov, sandwich::vcovHC))\n\nmodelsummary(models,\n  vcov = list(c(\"(Intercept)\"=\"\", \"Height\"=\"!\"),\n              c(\"(Intercept)\"=\"\", \"Height\"=\"!\", \"Volume\"=\"!!\")))\n\n# vcov with custom names\nmodelsummary(\n  models,\n  vcov = list(\"Stata Corp\" = \"stata\",\n              \"Newey Lewis & the News\" = \"NeweyWest\"))\n\n# fmt\nmod &lt;- lm(mpg ~ hp + drat + qsec, data = mtcars)\n\nmodelsummary(mod, fmt = 3)\n\nmodelsummary(mod, fmt = fmt_significant(3))\n\nmodelsummary(mod, fmt = NULL)\n\nmodelsummary(mod, fmt = fmt_decimal(4))\n\nmodelsummary(mod, fmt = fmt_sprintf(\"%.5f\"))\n\nmodelsummary(mod, fmt = fmt_statistic(estimate = 4, conf.int = 1), statistic = \"conf.int\")\n\nmodelsummary(mod, fmt = fmt_term(hp = 4, drat = 1, default = 2))\n\nm &lt;- lm(mpg ~ I(hp * 1000) + drat, data = mtcars)\nf &lt;- function(x) format(x, digits = 3, nsmall = 2, scientific = FALSE, trim = TRUE)\nmodelsummary(m, fmt = f, gof_map = NA)\n\n# coef_rename\nmodelsummary(models, coef_rename = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_rename = toupper)\n\nmodelsummary(models, coef_rename = coef_rename)\n\n# coef_rename = TRUE for variable labels\ndatlab &lt;- mtcars\ndatlab$cyl &lt;- factor(datlab$cyl)\nattr(datlab$hp, \"label\") &lt;- \"Horsepower\"\nattr(datlab$cyl, \"label\") &lt;- \"Cylinders\"\nmodlab &lt;- lm(mpg ~ hp * drat + cyl, data = datlab)\nmodelsummary(modlab, coef_rename = TRUE)\n\n# coef_rename: unnamed vector of length equal to the number of terms in the final table\nm &lt;- lm(hp ~ mpg + factor(cyl), data = mtcars)\nmodelsummary(m, coef_omit = -(3:4), coef_rename = c(\"Cyl 6\", \"Cyl 8\"))\n\n# coef_map\nmodelsummary(models, coef_map = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_map = c('Volume', 'Height'))\n\n# coef_omit: omit the first and second coefficients\nmodelsummary(models, coef_omit = 1:2)\n\n# coef_omit: omit coefficients matching one substring\nmodelsummary(models, coef_omit = \"ei\", gof_omit = \".*\")\n\n# coef_omit: omit a specific coefficient\nmodelsummary(models, coef_omit = \"^Volume$\", gof_omit = \".*\")\n\n# coef_omit: omit coefficients matching either one of two substring\n#modelsummary(models, coef_omit = \"ei|rc\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients starting with a substring (using a negative lookahead)\n#modelsummary(models, coef_omit = \"^(?!Vol)\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients matching a substring\nmodelsummary(models, coef_omit = \"^(?!.*ei|.*pt)\", gof_omit = \".*\")\n\n# shape: multinomial model\nlibrary(nnet)\nmulti &lt;- multinom(factor(cyl) ~ mpg + hp, data = mtcars, trace = FALSE) \n\n# shape: term names and group ids in rows, models in columns\nmodelsummary(multi, shape = response ~ model)\n\n# shape: term names and group ids in rows in a single column\nmodelsummary(multi, shape = term : response ~ model)\n\n# shape: term names in rows and group ids in columns\nmodelsummary(multi, shape = term ~ response:model)\n\n# shape = \"rcollapse\"\npanels &lt;- list(\n    \"Panel A: MPG\" = list(\n        \"A\" = lm(mpg ~ hp, data = mtcars),\n        \"B\" = lm(mpg ~ hp + factor(gear), data = mtcars)),\n    \"Panel B: Displacement\" = list(\n        \"A\" = lm(disp ~ hp, data = mtcars),\n        \"C\" = lm(disp ~ hp + factor(gear), data = mtcars))\n)\n\n# shape = \"cbind\"\nmodelsummary(panels, shape = \"cbind\")\n\nmodelsummary(\n    panels,\n    shape = \"rbind\",\n    gof_map = c(\"nobs\", \"r.squared\"))\n\n# title\nmodelsummary(models, title = 'This is the title')\n\n# title with LaTeX label (for numbering and referencing)\nmodelsummary(models, title = 'This is the title \\\\label{tab:description}')\n\n# add_rows\nrows &lt;- tibble::tribble(~term, ~Bivariate, ~Multivariate,\n  'Empty row', '-', '-',\n  'Another empty row', '?', '?')\nattr(rows, 'position') &lt;- c(1, 3)\nmodelsummary(models, add_rows = rows)\n\n# notes\nmodelsummary(models, notes = list('A first note', 'A second note'))\n\n# gof_map: tribble\nlibrary(tibble)\ngm &lt;- tribble(\n  ~raw,        ~clean,      ~fmt,\n  \"r.squared\", \"R Squared\", 5)\nmodelsummary(models, gof_map = gm)",
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+    "text": "Concatenating with +\n\nTo include more rows and columns, we use the + sign:\n\ndatasummary(flipper_length_mm + body_mass_g ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_4ndr0ikwtgv4ym7nlxgg\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nflipper_length_mm\n                  200.92 \n                  14.06 \n                \n\nbody_mass_g      \n                  4201.75\n                  801.95\n                \n\n\n\n\n    \n\n\nSometimes, it can be cumbersome to list all variables separated by + signs. The All() function is a useful shortcut:\n\ndatasummary(All(penguins) ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_0lrl0y7y9a6aetp3h30z\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nrownames         \n                  172.50 \n                  99.45 \n                \n\nbill_length_mm   \n                  43.92  \n                  5.46  \n                \n\nbill_depth_mm    \n                  17.15  \n                  1.97  \n                \n\nflipper_length_mm\n                  200.92 \n                  14.06 \n                \n\nbody_mass_g      \n                  4201.75\n                  801.95\n                \n\nyear             \n                  2008.03\n                  0.82  \n                \n\n\n\n\n    \n\n\nBy default, All selects all numeric variables. This behavior can be changed by modifying the function’s arguments. See ?All for details.",
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-    "text": "Create beautiful and customizable tables to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, Typst, PDF, PowerPoint, Excel, RTF, JPG, or PNG. The appearance of the tables can be customized extensively by specifying the output argument, and by using functions from one of the supported table customization packages: tinytable, kableExtra, gt, flextable, huxtable, DT. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nThe modelsummary Vignette includes dozens of examples of tables with extensive customizations.\n\n\nThe Appearance Vignette shows how to modify the look of tables.\n\n\nmodelsummary(\n  models,\n  output = \"default\",\n  fmt = 3,\n  estimate = \"estimate\",\n  statistic = \"std.error\",\n  vcov = NULL,\n  conf_level = 0.95,\n  exponentiate = FALSE,\n  stars = FALSE,\n  shape = term + statistic ~ model,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = FALSE,\n  gof_map = NULL,\n  gof_omit = NULL,\n  gof_function = NULL,\n  group_map = NULL,\n  add_columns = NULL,\n  add_rows = NULL,\n  align = NULL,\n  notes = NULL,\n  title = NULL,\n  escape = TRUE,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_decimal(digits = 2, pdigits = 3): Decimal digits for estimate and p values\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_term(“(Intercept)” = 1, “X” = 2): Format terms differently\n\n\nfmt = fmt_statistic(“estimate” = 1, “r.squared” = 6): Format statistics differently.\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\nstring:\n\n\nNote on LaTeX output: To ensure proper typography, all numeric entries are enclosed in the  command, which requires the siunitx package to be loaded in the LaTeX preamble. This behavior can be altered with global options. See the ‘Details’ section.\n\n\n\n\n\n\nestimate\n\n\na single string or a character vector of length equal to the number of models. Valid entries include any column name of the data.frame produced by get_estimates(model), and strings with curly braces compatible with the glue package format. Examples:\n\n\n“estimate”\n\n\n“{estimate} ({std.error}){stars}”\n\n\n“{estimate} [{conf.low}, {conf.high}]”\n\n\n\n\n\n\nstatistic\n\n\nvector of strings or glue strings which select uncertainty statistics to report vertically below the estimate. NULL omits all uncertainty statistics.\n\n\n\"conf.int\", \"std.error\", \"statistic\", \"p.value\", \"conf.low\", \"conf.high\", or any column name produced by get_estimates(model)\n\n\nglue package strings with braces, with or without R functions, such as:\n\n\n“{p.value} [{conf.low}, {conf.high}]”\n\n\n“Std.Error: {std.error}”\n\n\n“{exp(estimate) * std.error}”\n\n\nNumbers are automatically rounded and converted to strings. To apply functions to their numeric values, as in the last glue example, users must set fmt=NULL.\n\n\nParentheses are added automatically unless the string includes glue curly braces {}.\n\n\n\n\nNotes:\n\n\nThe names of the statistic are used a column names when using the shape argument to display statistics as columns:\n\n\nstatistic=c(“p”=“p.value”, “[”=”conf.low”, ”]”=“conf.high”)\n\n\n\n\nSome statistics are not supported for all models. See column names in get_estimates(model), and visit the website to learn how to add custom statistics.\n\n\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nstars\n\n\nto indicate statistical significance\n\n\nFALSE (default): no significance stars.\n\n\nTRUE: +=.1, =.05, =.01, =0.001\n\n\nNamed numeric vector for custom stars such as c(’*’ = .1, ‘+’ = .05)\n\n\nNote: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use \"glue strings\" with stars.\n\n\n\n\n\n\nshape\n\n\nNULL, formula, or string which determines the shape of a table.\n\n\nNULL: Default shape with terms in rows and models in columns.\n\n\nFormula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:\n\n\nterm + statistic ~ model: default\n\n\nterm ~ model + statistic: statistics in separate columns\n\n\nmodel + statistic ~ term: models in rows and terms in columns\n\n\nterm + response + statistic ~ model: term and group id in separate columns\n\n\nterm : response + statistic ~ model: term and group id in a single column\n\n\nterm ~ response\n\n\n\n\nString: \"cbind\", \"rbind\", \"rcollapse\"\n\n\n\"cbind\": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).\n\n\n\"rbind\" or \"rcollapse\": \"panels\" or \"stacks\" of regression models.\n\n\nthe models argument must be a (potentially named) nested list of models.\n\n\n\n\nUnnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))\n\n\nNamed nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))\n\n\nNamed panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))\n\n\n\n\n\"rbind\": Bind the rows of independent regression tables\n\n\n\"rcollapse\": Bind the rows of regression tables and create a panel at the bottom where we \"collapse\" goodness-of-fit statistics which are identical across models.\n\n\n\n\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\ngof_map\n\n\nrename, reorder, and omit goodness-of-fit statistics and other model information. This argument accepts 4 types of values:\n\n\nNULL (default): the modelsummary::gof_map dictionary is used for formatting, and all unknown statistic are included.\n\n\ncharacter vector: \"all\", \"none\", or a vector of statistics such as c(“rmse”, “nobs”, “r.squared”). Elements correspond to colnames in the data.frame produced by get_gof(model). The modelsummary::gof_map default dictionary is used to format and rename statistics.\n\n\nNA: excludes all statistics from the bottom part of the table.\n\n\ndata.frame with 3 columns named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples’ section below. The fmt column in this data frame only accepts integers. For more flexibility, use a list of lists, as described in the next bullet.\n\n\nlist of lists, each of which includes 3 elements named \"raw\", \"clean\", \"fmt\". Unknown statistics are omitted. See the ‘Examples section below’.\n\n\n\n\n\n\ngof_omit\n\n\nstring regular expression (perl-compatible) used to determine which statistics to omit from the bottom section of the table. A \"negative lookahead\" can be used to specify which statistics to keep in the table. Examples:\n\n\n“IC”: omit statistics matching the \"IC\" substring.\n\n\n“BIC|AIC”: omit statistics matching the \"AIC\" or \"BIC\" substrings.\n\n\n“^(?!.*IC)“: keep statistics matching the”IC\" substring.\n\n\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\ngroup_map\n\n\nnamed or unnamed character vector. Subset, rename, and reorder coefficient groups specified a grouping variable specified in the shape argument formula. This argument behaves like coef_map.\n\n\n\n\nadd_columns\n\n\na data.frame (or tibble) with the same number of rows as #’ your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the columns positions. See Examples section below.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nThe modelsummary_list output is a lightweight format which can be used to save model results, so they can be fed back to modelsummary later to avoid extracting results again.\nWhen a file name with a valid extension is supplied to the output argument, the table is written immediately to file. If you want to customize your table by post-processing it with an external package, you need to choose a different output format and saving mechanism. Unfortunately, the approach differs from package to package:\n\n\ntinytable: set output=“tinytable”, post-process your table, and use the tinytable::save_tt function.\n\n\ngt: set output=“gt”, post-process your table, and use the gt::gtsave function.\n\n\nkableExtra: set output to your destination format (e.g., \"latex\", \"html\", \"markdown\"), post-process your table, and use kableExtra::save_kable function.\n\n\n\nTo use a string such as \"robust\" or \"HC0\", your model must be supported by the sandwich package. This includes objects such as: lm, glm, survreg, coxph, mlogit, polr, hurdle, zeroinfl, and more.\nNULL, \"classical\", \"iid\", and \"constant\" are aliases which do not modify uncertainty estimates and simply report the default standard errors stored in the model object.\nOne-sided formulas such as ~clusterid are passed to the sandwich::vcovCL function.\nMatrices and functions producing variance-covariance matrices are first passed to lmtest. If this does not work, modelsummary attempts to take the square root of the diagonal to adjust \"std.error\", but the other uncertainty estimates are not be adjusted.\nNumeric vectors are formatted according to fmt and placed in brackets. Character vectors printed as given, without parentheses.\nIf your model type is supported by the lmtest package, the vcov argument will try to use that package to adjust all the uncertainty estimates, including \"std.error\", \"statistic\", \"p.value\", and \"conf.int\". If your model is not supported by lmtest, only the \"std.error\" will be adjusted by, for example, taking the square root of the matrix’s diagonal.\n\na regression table in a format determined by the output argument.\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nIt can take a long time to compute and extract summary statistics from certain models (e.g., Bayesian). In those cases, users can parallelize the process. Since parallelization occurs at the model level, no speedup is available for tables with a single model. Users on mac or linux can launch parallel computation using the built-in parallel package. All they need to do is supply a mc.cores argument which will be pushed forward to the parallel::mclapply function:\n\nmodelsummary(model_list, mc.cores = 5)\n\n\nAll users can also use the future.apply package to parallelize model summaries. For example, to use 4 cores to extract results:\n\nlibrary(future.apply)\nplan(multicore, workers = 4)\noptions(\"modelsummary_future\" = TRUE)\nmodelsummary(model_list)\n\n\nNote that the \"multicore\" plan only parallelizes under mac or linux. Windows users can use plan(multisession) instead. However, note that the first time modelsummary() is called under multisession can be a fair bit longer, because of extra costs in passing data to and loading required packages on to workers. Subsequent calls to modelsummary() will often be much faster.\nSome users have reported difficult to reproduce errors when using the future package with some packages. The future parallelization in modelsummary can be disabled by calling:\noptions(“modelsummary_future” = FALSE)\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\n# The `modelsummary` website includes \\emph{many} examples and tutorials:\n# https://modelsummary.com\n\nlibrary(modelsummary)\n\n# load data and estimate models\nutils::data(trees)\nmodels &lt;- list()\nmodels[['Bivariate']] &lt;- lm(Girth ~ Height, data = trees)\nmodels[['Multivariate']] &lt;- lm(Girth ~ Height + Volume, data = trees)\n\n# simple table\nmodelsummary(models)\n\n# statistic\nmodelsummary(models, statistic = NULL)\n\nmodelsummary(models, statistic = 'p.value')\n\nmodelsummary(models, statistic = 'statistic')\n\nmodelsummary(models, statistic = 'conf.int', conf_level = 0.99)\n\nmodelsummary(models, statistic = c(\"t = {statistic}\",\n                                   \"se = {std.error}\",\n                                   \"conf.int\"))\n\n# estimate\nmodelsummary(models,\n  statistic = NULL,\n  estimate = \"{estimate} [{conf.low}, {conf.high}]\")\n\nmodelsummary(models,\n  estimate = c(\"{estimate}{stars}\",\n               \"{estimate} ({std.error})\"))\n\n# vcov\nmodelsummary(models, vcov = \"robust\")\n\nmodelsummary(models, vcov = list(\"classical\", \"stata\"))\n\nmodelsummary(models, vcov = sandwich::vcovHC)\n\nmodelsummary(models,\n  vcov = list(stats::vcov, sandwich::vcovHC))\n\nmodelsummary(models,\n  vcov = list(c(\"(Intercept)\"=\"\", \"Height\"=\"!\"),\n              c(\"(Intercept)\"=\"\", \"Height\"=\"!\", \"Volume\"=\"!!\")))\n\n# vcov with custom names\nmodelsummary(\n  models,\n  vcov = list(\"Stata Corp\" = \"stata\",\n              \"Newey Lewis & the News\" = \"NeweyWest\"))\n\n# fmt\nmod &lt;- lm(mpg ~ hp + drat + qsec, data = mtcars)\n\nmodelsummary(mod, fmt = 3)\n\nmodelsummary(mod, fmt = fmt_significant(3))\n\nmodelsummary(mod, fmt = NULL)\n\nmodelsummary(mod, fmt = fmt_decimal(4))\n\nmodelsummary(mod, fmt = fmt_sprintf(\"%.5f\"))\n\nmodelsummary(mod, fmt = fmt_statistic(estimate = 4, conf.int = 1), statistic = \"conf.int\")\n\nmodelsummary(mod, fmt = fmt_term(hp = 4, drat = 1, default = 2))\n\nm &lt;- lm(mpg ~ I(hp * 1000) + drat, data = mtcars)\nf &lt;- function(x) format(x, digits = 3, nsmall = 2, scientific = FALSE, trim = TRUE)\nmodelsummary(m, fmt = f, gof_map = NA)\n\n# coef_rename\nmodelsummary(models, coef_rename = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_rename = toupper)\n\nmodelsummary(models, coef_rename = coef_rename)\n\n# coef_rename = TRUE for variable labels\ndatlab &lt;- mtcars\ndatlab$cyl &lt;- factor(datlab$cyl)\nattr(datlab$hp, \"label\") &lt;- \"Horsepower\"\nattr(datlab$cyl, \"label\") &lt;- \"Cylinders\"\nmodlab &lt;- lm(mpg ~ hp * drat + cyl, data = datlab)\nmodelsummary(modlab, coef_rename = TRUE)\n\n# coef_rename: unnamed vector of length equal to the number of terms in the final table\nm &lt;- lm(hp ~ mpg + factor(cyl), data = mtcars)\nmodelsummary(m, coef_omit = -(3:4), coef_rename = c(\"Cyl 6\", \"Cyl 8\"))\n\n# coef_map\nmodelsummary(models, coef_map = c('Volume' = 'Large', 'Height' = 'Tall'))\n\nmodelsummary(models, coef_map = c('Volume', 'Height'))\n\n# coef_omit: omit the first and second coefficients\nmodelsummary(models, coef_omit = 1:2)\n\n# coef_omit: omit coefficients matching one substring\nmodelsummary(models, coef_omit = \"ei\", gof_omit = \".*\")\n\n# coef_omit: omit a specific coefficient\nmodelsummary(models, coef_omit = \"^Volume$\", gof_omit = \".*\")\n\n# coef_omit: omit coefficients matching either one of two substring\n#modelsummary(models, coef_omit = \"ei|rc\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients starting with a substring (using a negative lookahead)\n#modelsummary(models, coef_omit = \"^(?!Vol)\", gof_omit = \".*\")\n\n# coef_omit: keep coefficients matching a substring\nmodelsummary(models, coef_omit = \"^(?!.*ei|.*pt)\", gof_omit = \".*\")\n\n# shape: multinomial model\nlibrary(nnet)\nmulti &lt;- multinom(factor(cyl) ~ mpg + hp, data = mtcars, trace = FALSE) \n\n# shape: term names and group ids in rows, models in columns\nmodelsummary(multi, shape = response ~ model)\n\n# shape: term names and group ids in rows in a single column\nmodelsummary(multi, shape = term : response ~ model)\n\n# shape: term names in rows and group ids in columns\nmodelsummary(multi, shape = term ~ response:model)\n\n# shape = \"rcollapse\"\npanels &lt;- list(\n    \"Panel A: MPG\" = list(\n        \"A\" = lm(mpg ~ hp, data = mtcars),\n        \"B\" = lm(mpg ~ hp + factor(gear), data = mtcars)),\n    \"Panel B: Displacement\" = list(\n        \"A\" = lm(disp ~ hp, data = mtcars),\n        \"C\" = lm(disp ~ hp + factor(gear), data = mtcars))\n)\n\n# shape = \"cbind\"\nmodelsummary(panels, shape = \"cbind\")\n\nmodelsummary(\n    panels,\n    shape = \"rbind\",\n    gof_map = c(\"nobs\", \"r.squared\"))\n\n# title\nmodelsummary(models, title = 'This is the title')\n\n# title with LaTeX label (for numbering and referencing)\nmodelsummary(models, title = 'This is the title \\\\label{tab:description}')\n\n# add_rows\nrows &lt;- tibble::tribble(~term, ~Bivariate, ~Multivariate,\n  'Empty row', '-', '-',\n  'Another empty row', '?', '?')\nattr(rows, 'position') &lt;- c(1, 3)\nmodelsummary(models, add_rows = rows)\n\n# notes\nmodelsummary(models, notes = list('A first note', 'A second note'))\n\n# gof_map: tribble\nlibrary(tibble)\ngm &lt;- tribble(\n  ~raw,        ~clean,      ~fmt,\n  \"r.squared\", \"R Squared\", 5)\nmodelsummary(models, gof_map = gm)",
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+    "text": "Nesting with *\n\ndatasummary can nest variables and statistics inside categorical variables using the * symbol. When applying the the * operator to factor, character, or logical variables, columns or rows will automatically be nested. For instance, if we want to display separate means for each value of the variable sex, we use mean * sex:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * sex,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_gmpomhntrat7k9d0i4e3\n\n\n      \n\n \n                female\n                male\n              \n\n\nflipper_length_mm\n                  197.36 \n                  204.51 \n                \n\nbody_mass_g      \n                  3862.27\n                  4545.68\n                \n\n\n\n\n    \n\n\nWe can use parentheses to nest several terms inside one another, using a call of this form: x * (y + z). Here is an example with nested columns:\n\ndatasummary(body_mass_g ~ sex * (mean + sd),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_61gfvsq6e63e6kvnf1gv\n\n\n      \n\n\n \nfemale\nmale\n\n\n \n                mean\n                sd\n                mean\n                sd\n              \n\n\nbody_mass_g\n                  3862.27\n                  666.17\n                  4545.68\n                  787.63\n                \n\n\n\n    \n\n\nHere is an example with nested rows:\n\ndatasummary(sex * (body_mass_g + flipper_length_mm) ~ mean + sd,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_5g2gknzl8ajp7jr2aw6y\n\n\n      \n\nsex\n                 \n                mean\n                sd\n              \n\n\nfemale\n                  body_mass_g      \n                  3862.27\n                  666.17\n                \n\n      \n                  flipper_length_mm\n                  197.36 \n                  12.50 \n                \n\nmale  \n                  body_mass_g      \n                  4545.68\n                  787.63\n                \n\n      \n                  flipper_length_mm\n                  204.51 \n                  14.55 \n                \n\n\n\n\n    \n\n\nThe order in which terms enter the formula determines the order in which labels are displayed. For example, this shows island above sex:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * island * sex,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_2o40nh8kxq8yip355hnc\n\n\n      \n\n\n \nBiscoe\nDream\nTorgersen\n\n\n \n                female\n                male\n                female\n                male\n                female\n                male\n              \n\n\n\nflipper_length_mm\n                  205.69 \n                  213.29 \n                  190.02 \n                  196.31 \n                  188.29 \n                  194.91 \n                \n\nbody_mass_g      \n                  4319.38\n                  5104.52\n                  3446.31\n                  3987.10\n                  3395.83\n                  4034.78\n                \n\n\n\n\n    \n\n\nThis shows sex above island values:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * sex * island,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_kpuz8qlbcbo5tfrcs1td\n\n\n      \n\n\n \nfemale\nmale\n\n\n \n                Biscoe\n                Dream\n                Torgersen\n                Biscoe\n                Dream\n                Torgersen\n              \n\n\n\nflipper_length_mm\n                  205.69 \n                  190.02 \n                  188.29 \n                  213.29 \n                  196.31 \n                  194.91 \n                \n\nbody_mass_g      \n                  4319.38\n                  3446.31\n                  3395.83\n                  5104.52\n                  3987.10\n                  4034.78\n                \n\n\n\n\n    \n\n\nBy default, datasummary omits column headers with a single value/label across all columns, except for the header that sits just above the data. If the header we want to see is not displayed, we may want to reorder the terms of the formula. To show all headers, set sparse_header=FALSE:\n\ndatasummary(flipper_length_mm + body_mass_g ~ mean * sex * island,\n            data = penguins,\n            sparse_header = FALSE)\n\n \n\n  \n    \n\ntinytable_1g19moklboiz58hvaspq\n\n\n      \n\n\n \nfemale\nmale\n\n\n \n                Biscoe\n                Dream\n                Torgersen\n                Biscoe\n                Dream\n                Torgersen\n              \n\n\n\nflipper_length_mm\n                  205.69 \n                  190.02 \n                  188.29 \n                  213.29 \n                  196.31 \n                  194.91 \n                \n\nbody_mass_g      \n                  4319.38\n                  3446.31\n                  3395.83\n                  5104.52\n                  3987.10\n                  4034.78\n                \n\n\n\n\n    \n\n\nWhen using sparse_header=FALSE, it is often useful to insert Heading() * in the table formula, in order to rename or omit some of the labels manually. Type ?tables::Heading for details and examples.",
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-    "text": "Dot-Whisker plot of coefficient estimates with confidence intervals. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nmodelplot Vignette.\n\n\nmodelplot(\n  models,\n  conf_level = 0.95,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = NULL,\n  vcov = NULL,\n  exponentiate = FALSE,\n  add_rows = NULL,\n  facet = FALSE,\n  draw = TRUE,\n  background = NULL,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nfacet\n\n\nTRUE or FALSE. When the ‘models’ argument includes several model objects, TRUE draws terms in separate facets, and FALSE draws terms side-by-side (dodged).\n\n\n\n\ndraw\n\n\nTRUE returns a ‘ggplot2’ object, FALSE returns the data.frame used to draw the plot.\n\n\n\n\nbackground\n\n\nA list of ‘ggplot2’ geoms to add to the background of the plot. This is especially useful to display annotations \"behind\" the ‘geom_pointrange’ that ‘modelplot’ draws.\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# single model\nmod &lt;- lm(hp ~ vs + drat, mtcars)\nmodelplot(mod)\n\n\n\n\n\n\n# omit terms with string matches or regexes\nmodelplot(mod, coef_omit = 'Interc')\n\n\n\n\n\n\n# rename, reorder and subset with 'coef_map'\ncm &lt;- c('vs' = 'V-shape engine',\n  'drat' = 'Rear axle ratio')\nmodelplot(mod, coef_map = cm)\n\n\n\n\n\n\n# several models\nmodels &lt;- list()\nmodels[['Small model']] &lt;- lm(hp ~ vs, mtcars)\nmodels[['Medium model']] &lt;- lm(hp ~ vs + factor(cyl), mtcars)\nmodels[['Large model']] &lt;- lm(hp ~ vs + drat + factor(cyl), mtcars)\nmodelplot(models)\n\n\n\n\n\n\n# add_rows: add an empty reference category\n\nmod &lt;- lm(hp ~ factor(cyl), mtcars)\n\nadd_rows = data.frame(\n  term = \"factory(cyl)4\",\n  model = \"(1)\",\n  estimate = NA)\nattr(add_rows, \"position\") = 3\nmodelplot(mod, add_rows = add_rows)\n\n\n\n\n\n\n# customize your plots with 'ggplot2' functions\nlibrary(ggplot2)\n\nmodelplot(models) +\n  scale_color_brewer(type = 'qual') +\n  theme_classic()\n\n\n\n\n\n\n# pass arguments to 'geom_pointrange' through the ... ellipsis\nmodelplot(mod, color = 'red', size = 1, fatten = .5)\n\n\n\n\n\n\n# add geoms to the background, behind geom_pointrange\nb &lt;- list(geom_vline(xintercept = 0, color = 'orange'),\n  annotate(\"rect\", alpha = .1,\n    xmin = -.5, xmax = .5,\n    ymin = -Inf, ymax = Inf),\n  geom_point(aes(y = term, x = estimate), alpha = .3,\n    size = 10, color = 'red', shape = 'square'))\nmodelplot(mod, background = b)\n\n\n\n\n\n\n# logistic regression example\ndf &lt;- as.data.frame(Titanic)\nmod_titanic &lt;- glm(\n  Survived ~ Class + Sex,\n  family = binomial,\n  weight = Freq,\n  data = df\n)\n\n# displaying odds ratio using a log scale\nmodelplot(mod_titanic, exponentiate = TRUE) +\n  scale_x_log10() +\n  xlab(\"Odds Ratios and 95% confidence intervals\")",
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+    "text": "Renaming with =\n\nPersonally, I prefer to rename variables and values before drawing my tables, using backticks when variable names include whitespace. For example,\n\ntmp &lt;- penguins %&gt;%\n       select(`Flipper length (mm)` = flipper_length_mm,\n              `Body mass (g)` = body_mass_g)\n\ndatasummary(`Flipper length (mm)` + `Body mass (g)` ~ Mean + SD,\n            data = tmp)\n\n \n\n  \n    \n\ntinytable_0dlbmbfoujhcltzgn62l\n\n\n      \n\n \n                Mean\n                SD\n              \n\n\nFlipper length (mm)\n                  200.92 \n                  14.06 \n                \n\nBody mass (g)      \n                  4201.75\n                  801.95\n                \n\n\n\n\n    \n\n\nHowever, thanks to the tables package, datasummary offers two additional mechanisms to rename. First, we can wrap a term in parentheses and use the equal = sign: (NewName=OldName):\n\ndatasummary((`Flipper length (mm)` = flipper_length_mm) + (`Body mass (g)` = body_mass_g) ~\n            island * ((Avg. = Mean) + (Std.Dev. = SD)),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_4lg5ajybsnt7mc21r5tt\n\n\n      \n\n\n \nBiscoe\nDream\nTorgersen\n\n\n \n                Avg.\n                Std.Dev.\n                Avg.\n                Std.Dev.\n                Avg.\n                Std.Dev.\n              \n\n\n\nFlipper length (mm)\n                  209.71 \n                  14.14 \n                  193.07 \n                  7.51  \n                  191.20 \n                  6.23  \n                \n\nBody mass (g)      \n                  4716.02\n                  782.86\n                  3712.90\n                  416.64\n                  3706.37\n                  445.11\n                \n\n\n\n\n    \n\n\nSecond, we can use the Heading() function:\n\ndatasummary(Heading(\"Flipper length (mm)\") * flipper_length_mm + Heading(\"Body mass (g)\") * body_mass_g ~ island * (Mean + SD),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_prmzsvq6vsc6j93abpup\n\n\n      \n\n\n \nBiscoe\nDream\nTorgersen\n\n\n \n                Mean\n                SD\n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nFlipper length (mm)\n                  209.71 \n                  14.14 \n                  193.07 \n                  7.51  \n                  191.20 \n                  6.23  \n                \n\nBody mass (g)      \n                  4716.02\n                  782.86\n                  3712.90\n                  416.64\n                  3706.37\n                  445.11\n                \n\n\n\n\n    \n\n\nThe Heading function also has a nearData argument which can be useful in cases where some rows are nested but others are not. Compare the last row of these two tables:\n\ndatasummary(sex * (flipper_length_mm + bill_length_mm) + Heading(\"Body mass (g)\") * body_mass_g ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_shg3lq1fzassz2vtqz4c\n\n\n      \n\nsex\n                 \n                Mean\n                SD\n              \n\n\nfemale\n                  flipper_length_mm\n                  197.36 \n                  12.50 \n                \n\n      \n                  bill_length_mm   \n                  42.10  \n                  4.90  \n                \n\nmale  \n                  flipper_length_mm\n                  204.51 \n                  14.55 \n                \n\n      \n                  bill_length_mm   \n                  45.85  \n                  5.37  \n                \n\n      \n                  Body mass (g)    \n                  4201.75\n                  801.95\n                \n\n\n\n\n    \n\ndatasummary(sex * (flipper_length_mm + bill_length_mm) + Heading(\"Body mass (g)\", nearData=FALSE) * body_mass_g ~ Mean + SD,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_1jwng5p5tqqkn755em2n\n\n\n      \n\nsex\n                 \n                Mean\n                SD\n              \n\n\nfemale       \n                  flipper_length_mm\n                  197.36 \n                  12.50 \n                \n\n             \n                  bill_length_mm   \n                  42.10  \n                  4.90  \n                \n\nmale         \n                  flipper_length_mm\n                  204.51 \n                  14.55 \n                \n\n             \n                  bill_length_mm   \n                  45.85  \n                  5.37  \n                \n\nBody mass (g)\n                                   \n                  4201.75\n                  801.95",
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-    "text": "Dot-Whisker plot of coefficient estimates with confidence intervals. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website: https://modelsummary.com/\n\n\nmodelplot Vignette.\n\n\nmodelplot(\n  models,\n  conf_level = 0.95,\n  coef_map = NULL,\n  coef_omit = NULL,\n  coef_rename = NULL,\n  vcov = NULL,\n  exponentiate = FALSE,\n  add_rows = NULL,\n  facet = FALSE,\n  draw = TRUE,\n  background = NULL,\n  ...\n)\n\n\n\n\n\nmodels\n\n\na model, (named) list of models, or nested list of models.\n\n\nSingle model: modelsummary(model)\n\n\nUnnamed list of models: modelsummary(list(model1, model2))\n\n\nModels are labelled automatically. The default label style can be altered by setting a global option. See below.\n\n\n\n\nNamed list of models: modelsummary(list(“A”=model1, “B”=model2))\n\n\nModels are labelled using the list names.\n\n\n\n\nNested list of models:\n\n\nWhen using the shape argument with \"rbind\", \"rcollapse\", or \"cbind\" values, models can be a nested list of models to display \"panels\" or \"stacks\" of regression models. See the shape argument documentation and examples below.\n\n\n\n\n\n\n\n\nconf_level\n\n\nnumeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.\n\n\n\n\ncoef_map\n\n\ncharacter vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.\n\n\n\n\ncoef_omit\n\n\ninteger vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible \"negative lookaheads\" can be used to specify which coefficients to keep in the table. Examples:\n\n\nc(2, 3, 5): omits the second, third, and fifth coefficients.\n\n\nc(-2, -3, -5): negative values keep the second, third, and fifth coefficients.\n\n\n“ei”: omit coefficients matching the \"ei\" substring.\n\n\n“^Volume$”: omit the \"Volume\" coefficient.\n\n\n“ei|rc”: omit coefficients matching either the \"ei\" or the \"rc\" substrings.\n\n\n“^(?!Vol)”: keep coefficients starting with \"Vol\" (inverse match using a negative lookahead).\n\n\n“^(?!.*ei)“: keep coefficients matching the”ei\" substring.\n\n\n“^(?!.ei|.pt)”: keep coefficients matching either the \"ei\" or the \"pt\" substrings.\n\n\nSee the Examples section below for complete code.\n\n\n\n\n\n\ncoef_rename\n\n\nlogical, named or unnamed character vector, or function\n\n\nLogical: TRUE renames variables based on the \"label\" attribute of each column. See the Example section below.\n\n\nUnnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.\n\n\nNamed character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c(\"hp:mpg\"=\"hp X mpg\")\n\n\nFunction: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)\n\n\n\n\n\n\nvcov\n\n\nrobust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):\n\n\nNULL returns the default uncertainty estimates of the model object\n\n\nstring, vector, or (named) list of strings. \"iid\", \"classical\", and \"constant\" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings \"HC\", \"HC0\", \"HC1\" (alias: \"stata\"), \"HC2\", \"HC3\" (alias: \"robust\"), \"HC4\", \"HC4m\", \"HC5\", \"HAC\", \"NeweyWest\", \"Andrews\", \"panel-corrected\", \"outer-product\", and \"weave\" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. \"BS\", \"bootstrap\", \"residual\", \"mammen\", \"webb\", \"xy\", \"wild\" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (…), but it is safer to define your own custom functions as described in the next bullet.\n\n\nfunction or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).\n\n\nformula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).\n\n\nnamed list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.\n\n\na named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.\n\n\n\n\n\n\nexponentiate\n\n\nTRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.\n\n\n\n\nadd_rows\n\n\na data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a \"position\" attribute of integers to set the row positions. See Examples section below.\n\n\n\n\nfacet\n\n\nTRUE or FALSE. When the ‘models’ argument includes several model objects, TRUE draws terms in separate facets, and FALSE draws terms side-by-side (dodged).\n\n\n\n\ndraw\n\n\nTRUE returns a ‘ggplot2’ object, FALSE returns the data.frame used to draw the plot.\n\n\n\n\nbackground\n\n\nA list of ‘ggplot2’ geoms to add to the background of the plot. This is especially useful to display annotations \"behind\" the ‘geom_pointrange’ that ‘modelplot’ draws.\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.\n\n\n\n\n\n\nlibrary(modelsummary)\n\nlibrary(modelsummary)\n\n# single model\nmod &lt;- lm(hp ~ vs + drat, mtcars)\nmodelplot(mod)\n\n\n\n\n\n\n# omit terms with string matches or regexes\nmodelplot(mod, coef_omit = 'Interc')\n\n\n\n\n\n\n# rename, reorder and subset with 'coef_map'\ncm &lt;- c('vs' = 'V-shape engine',\n  'drat' = 'Rear axle ratio')\nmodelplot(mod, coef_map = cm)\n\n\n\n\n\n\n# several models\nmodels &lt;- list()\nmodels[['Small model']] &lt;- lm(hp ~ vs, mtcars)\nmodels[['Medium model']] &lt;- lm(hp ~ vs + factor(cyl), mtcars)\nmodels[['Large model']] &lt;- lm(hp ~ vs + drat + factor(cyl), mtcars)\nmodelplot(models)\n\n\n\n\n\n\n# add_rows: add an empty reference category\n\nmod &lt;- lm(hp ~ factor(cyl), mtcars)\n\nadd_rows = data.frame(\n  term = \"factory(cyl)4\",\n  model = \"(1)\",\n  estimate = NA)\nattr(add_rows, \"position\") = 3\nmodelplot(mod, add_rows = add_rows)\n\n\n\n\n\n\n# customize your plots with 'ggplot2' functions\nlibrary(ggplot2)\n\nmodelplot(models) +\n  scale_color_brewer(type = 'qual') +\n  theme_classic()\n\n\n\n\n\n\n# pass arguments to 'geom_pointrange' through the ... ellipsis\nmodelplot(mod, color = 'red', size = 1, fatten = .5)\n\n\n\n\n\n\n# add geoms to the background, behind geom_pointrange\nb &lt;- list(geom_vline(xintercept = 0, color = 'orange'),\n  annotate(\"rect\", alpha = .1,\n    xmin = -.5, xmax = .5,\n    ymin = -Inf, ymax = Inf),\n  geom_point(aes(y = term, x = estimate), alpha = .3,\n    size = 10, color = 'red', shape = 'square'))\nmodelplot(mod, background = b)\n\n\n\n\n\n\n# logistic regression example\ndf &lt;- as.data.frame(Titanic)\nmod_titanic &lt;- glm(\n  Survived ~ Class + Sex,\n  family = binomial,\n  weight = Freq,\n  data = df\n)\n\n# displaying odds ratio using a log scale\nmodelplot(mod_titanic, exponentiate = TRUE) +\n  scale_x_log10() +\n  xlab(\"Odds Ratios and 95% confidence intervals\")",
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+    "text": "Counts and Percentages\nThe tables package allows datasummary to use neat tricks to produce frequency tables:\n\nAdd a N to the right-hand side of the equation.\nAdd Percent() to the right-hand side to calculate the percentage of observations in each cell.\nAdd 1 to the left-hand side to include a row with the total number of observations:\n\n\ndatasummary(species * sex + 1 ~ N + Percent(),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_iuhxhtuns5dzwes76shk\n\n\n      \n\nspecies\n                sex\n                N\n                Percent\n              \n\n\nAdelie   \n                  female\n                  73 \n                  21.22 \n                \n\n         \n                  male  \n                  73 \n                  21.22 \n                \n\nChinstrap\n                  female\n                  34 \n                  9.88  \n                \n\n         \n                  male  \n                  34 \n                  9.88  \n                \n\nGentoo   \n                  female\n                  58 \n                  16.86 \n                \n\n         \n                  male  \n                  61 \n                  17.73 \n                \n\n         \n                  All   \n                  344\n                  100.00\n                \n\n\n\n\n    \n\n\nNote that the Percent() function accepts a denom argument to determine if percentages should be calculated row or column-wise, or if they should take into account all cells.",
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-    "text": "Update modelsummary and its dependencies to the latest R-Universe or CRAN versions. The R session needs to be restarted after install.\n\n\n\nupdate_modelsummary(source = \"development\")\n\n\n\n\n\n\n\nsource\n\n\none of two strings: \"development\" or \"cran\"",
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+    "text": "Weighted percentages\nThe Percent() pseudo-function also accepts a fn argument, which must be a function which accepts two vectors: x is the values in the current cell, and y is all the values in the whole dataset. The default fn is:\n\ndatasummary(species * sex + 1 ~ N + Percent(fn = function(x, y) 100 * length(x) / length(y)),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_0mder98ikpie4acsvb3e\n\n\n      \n\nspecies\n                sex\n                N\n                Percent\n              \n\n\nAdelie   \n                  female\n                  73 \n                  21.22 \n                \n\n         \n                  male  \n                  73 \n                  21.22 \n                \n\nChinstrap\n                  female\n                  34 \n                  9.88  \n                \n\n         \n                  male  \n                  34 \n                  9.88  \n                \n\nGentoo   \n                  female\n                  58 \n                  16.86 \n                \n\n         \n                  male  \n                  61 \n                  17.73 \n                \n\n         \n                  All   \n                  344\n                  100.00\n                \n\n\n\n\n    \n\n\nThe code above takes the number of elements in the cell length(x) and divides it by the number of total elements length(y).\nNow, let’s say we want to display percentages weighted by one of the variables of the dataset. This can often be useful with survey weights, for example. Here, we use an arbitrary column of weights called flipper_length_mm:\n\nwtpct &lt;- function(x, y) sum(x, na.rm = TRUE) / sum(y, na.rm = TRUE) * 100\ndatasummary(species * sex + 1 ~ N + flipper_length_mm * Percent(fn = wtpct),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_bk8prpbmxl80j6xwm16l\n\n\n      \n\nspecies\n                sex\n                N\n                Percent\n              \n\n\nAdelie   \n                  female\n                  73 \n                  19.95 \n                \n\n         \n                  male  \n                  73 \n                  20.44 \n                \n\nChinstrap\n                  female\n                  34 \n                  9.49  \n                \n\n         \n                  male  \n                  34 \n                  9.89  \n                \n\nGentoo   \n                  female\n                  58 \n                  17.95 \n                \n\n         \n                  male  \n                  61 \n                  19.67 \n                \n\n         \n                  All   \n                  344\n                  100.00\n                \n\n\n\n\n    \n\n\nIn each cell we now have the sum of weights in that cell, divided by the total sum of weights in the column.",
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-    "text": "Update modelsummary and its dependencies to the latest R-Universe or CRAN versions. The R session needs to be restarted after install.\n\n\n\nupdate_modelsummary(source = \"development\")\n\n\n\n\n\n\n\nsource\n\n\none of two strings: \"development\" or \"cran\"",
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+    "text": "Custom percentages\nHere is another simple illustration of Percent function mechanism in action, where we combine counts and percentages in a simple nice label:\n\ndat &lt;- mtcars\ndat$cyl &lt;- as.factor(dat$cyl)\n\nfn &lt;- function(x, y) {\n    out &lt;- sprintf(\n        \"%s (%.1f%%)\",\n        length(x),\n        length(x) / length(y) * 100)\n}\ndatasummary(\n    cyl ~ Percent(fn = fn),\n    data = dat)\n\n \n\n  \n    \n\ntinytable_01n7279q4m7isxyc9p35\n\n\n      \n\ncyl\n                Percent\n              \n\n\n4\n                  11 (34.4%)\n                \n\n6\n                  7 (21.9%) \n                \n\n8\n                  14 (43.8%)",
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-    "text": "This function was inspired by the excellent skimr package for R. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_skim(\n  data,\n  output = \"default\",\n  type = \"all\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  escape = TRUE,\n  by = NULL,\n  fun_numeric = list(Unique = NUnique, `Missing Pct.` = PercentMissing, Mean = Mean, SD =\n    SD, Min = Min, Median = Median, Max = Max, Histogram = function(x) \"\"),\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\ntype\n\n\nString. Variables to summarize: \"all\", \"numeric\", \"categorical\", \"dataset\"\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\nby\n\n\nCharacter vector of grouping variables to compute statistics over.\n\n\n\n\nfun_numeric\n\n\nNamed list of funtions to apply to each column of data. If fun_numeric includes \"Histogram\" or \"Density\", inline plots are inserted.\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\ndat &lt;- mtcars\ndat$vs &lt;- as.logical(dat$vs)\ndat$cyl &lt;- as.factor(dat$cyl)\ndatasummary_skim(dat)\ndatasummary_skim(dat, type = \"categorical\")",
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+    "text": "Factor\nThe * nesting operator that we used above works automatically for factor, character, and logical variables. Sometimes, it is convenient to use it with other types of variables, such as binary numeric variables. In that case, we can wrap the variable name inside a call to Factor(). This allows us to treat a variable as factor, without having to modify it in the original data. For example, in the mtcars data, there is a binary numeric variable call am. We nest statistics within categories of am by typing:\n\ndatasummary(mpg + hp ~ Factor(am) * (mean + sd),\n            data = mtcars)\n\n \n\n  \n    \n\ntinytable_bda7octedv0whbqafr4j\n\n\n      \n\n\n \n0\n1\n\n\n \n                mean\n                sd\n                mean\n                sd\n              \n\n\n\nmpg\n                  17.15 \n                  3.83 \n                  24.39 \n                  6.17 \n                \n\nhp \n                  160.26\n                  53.91\n                  126.85\n                  84.06",
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-    "text": "This function was inspired by the excellent skimr package for R. See the Details and Examples sections below, and the vignettes on the modelsummary website:\n\n\nhttps://modelsummary.com/\n\n\nhttps://modelsummary.com/articles/datasummary.html\n\n\ndatasummary_skim(\n  data,\n  output = \"default\",\n  type = \"all\",\n  fmt = 1,\n  title = NULL,\n  notes = NULL,\n  align = NULL,\n  escape = TRUE,\n  by = NULL,\n  fun_numeric = list(Unique = NUnique, `Missing Pct.` = PercentMissing, Mean = Mean, SD =\n    SD, Min = Min, Median = Median, Max = Max, Histogram = function(x) \"\"),\n  ...\n)\n\n\n\n\n\ndata\n\n\nA data.frame (or tibble)\n\n\n\n\noutput\n\n\nfilename or object type (character string)\n\n\nSupported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg\n\n\nSupported object types: \"default\", \"html\", \"markdown\", \"latex\", \"latex_tabular\", \"typst\", \"data.frame\", \"tinytable\", \"gt\", \"kableExtra\", \"huxtable\", \"flextable\", \"DT\", \"jupyter\". The \"modelsummary_list\" value produces a lightweight object which can be saved and fed back to the modelsummary function.\n\n\nThe \"default\" output format can be set to \"tinytable\", \"kableExtra\", \"gt\", \"flextable\", \"huxtable\", \"DT\", or \"markdown\"\n\n\nIf the user does not choose a default value, the packages listed above are tried in sequence.\n\n\nSession-specific configuration: options(“modelsummary_factory_default” = “gt”)\n\n\nPersistent configuration: config_modelsummary(output = “markdown”)\n\n\n\n\nWarning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.\n\n\nLaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.\n\n\n\n\n\n\ntype\n\n\nString. Variables to summarize: \"all\", \"numeric\", \"categorical\", \"dataset\"\n\n\n\n\nfmt\n\n\nhow to format numeric values: integer, user-supplied function, or modelsummary function.\n\n\nInteger: Number of decimal digits\n\n\nUser-supplied functions:\n\n\nAny function which accepts a numeric vector and returns a character vector of the same length.\n\n\n\n\nmodelsummary functions:\n\n\nfmt = fmt_significant(2): Two significant digits (at the term-level)\n\n\nfmt = fmt_sprintf(“%.3f”): See ?sprintf\n\n\nfmt = fmt_identity(): unformatted raw values\n\n\n\n\n\n\n\n\ntitle\n\n\nstring\n\n\n\n\nnotes\n\n\nlist or vector of notes to append to the bottom of the table.\n\n\n\n\nalign\n\n\nA string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.\n\n\n\"l\": left-aligned column\n\n\n\"c\": centered column\n\n\n\"r\": right-aligned column\n\n\n\"d\": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.\n\n\n\n\n\n\nescape\n\n\nboolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |&gt; tinytable::format_tt(tab, j=1:5, escape=TRUE)\n\n\n\n\nby\n\n\nCharacter vector of grouping variables to compute statistics over.\n\n\n\n\nfun_numeric\n\n\nNamed list of funtions to apply to each column of data. If fun_numeric includes \"Histogram\" or \"Density\", inline plots are inserted.\n\n\n\n\n…\n\n\nall other arguments are passed through to the table-making functions tinytable::tt, kableExtra::kbl, gt::gt, DT::datatable, etc. depending on the output argument. This allows users to pass arguments directly to datasummary in order to affect the behavior of other functions behind the scenes.\n\n\n\nThe behavior of modelsummary can be modified by setting global options. For example:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\nThe rest of this section describes each of the options above.\n\nThese global option changes the style of the default column headers:\n\n\noptions(modelsummary_model_labels = “roman”)\n\n\noptions(modelsummary_panel_labels = “roman”)\n\n\nThe supported styles are: \"model\", \"panel\", \"arabic\", \"letters\", \"roman\", \"(arabic)\", \"(letters)\", \"(roman)\"\nThe panel-specific option is only used when shape=“rbind”\n\nmodelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:\noptions(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’)\n\nChange the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html\n\n\nmodelsummary_theme_gt\n\n\nmodelsummary_theme_kableExtra\n\n\nmodelsummary_theme_huxtable\n\n\nmodelsummary_theme_flextable\n\n\nmodelsummary_theme_dataframe\n\n\n\nmodelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:\noptions(modelsummary_get = “easystats”)\noptions(modelsummary_get = “broom”)\noptions(modelsummary_get = “all”)\n\nBy default, LaTeX tables enclose all numeric entries in the  command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:\noptions(modelsummary_format_numeric_latex = “plain”)\noptions(modelsummary_format_numeric_latex = “mathmode”)\nA similar option can be used to display numerical entries using MathJax in HTML tables:\noptions(modelsummary_format_numeric_html = “mathjax”)\n\nWhen creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents.\n[3]{}\n\nArel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’\n\n\nlibrary(modelsummary)\n\n\ndat &lt;- mtcars\ndat$vs &lt;- as.logical(dat$vs)\ndat$cyl &lt;- as.factor(dat$cyl)\ndatasummary_skim(dat)\ndatasummary_skim(dat, type = \"categorical\")",
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+    "text": "Arguments: na.rm=TRUE\n\nWe can pass any argument to the summary function by including a call to Arguments(). For instance, there are missing values in the flipper_length_mm variable of the penguins dataset. Therefore, the standard mean function will produce no result, because its default argument is na.rm=FALSE. We can change that by calling:\n\ndatasummary(flipper_length_mm ~ mean + mean*Arguments(na.rm=TRUE),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_xb3z1hpx3lbrxr80f6d0\n\n\n      \n\n \n                mean\n                mean \n              \n\nflipper_length_mm\n                  \n                  200.92\n                \n\n\n\n    \n\n\nNotice that there is an empty cell (NA) under the normal mean function, but that the mean call with Arguments(na.rm=TRUE) produced a numeric result.\nWe can pass the same arguments to multiple functions using the parentheses:\n\ndatasummary(flipper_length_mm ~ (mean + sd) * Arguments(na.rm=TRUE),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_c9pct73bay4ctr12laor\n\n\n      \n\n \n                mean\n                sd\n              \n\nflipper_length_mm\n                  200.92\n                  14.06\n                \n\n\n\n    \n\n\nNote that the shortcut functions that modelsummary supplies use na.rm=TRUE by default, so we can use them directly without arguments, even when there are missing values:\n\ndatasummary(flipper_length_mm ~ Mean + Var + P75 + Range,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_m288rwhi4akdjwnosozq\n\n\n      \n\n \n                Mean\n                Var\n                P75\n                Range\n              \n\nflipper_length_mm\n                  200.92\n                  197.73\n                  213.00\n                  59.00",
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-    "text": "Rounding with decimal digits on a per-term basis in the fmt argument for modelsummary()\n\n\n\nfmt_term(..., default = 3)\n\n\n\n\n\n\n\n…\n\n\nTerm names and fmt value\n\n\n\n\ndefault\n\n\nNumber of decimal digits to keep for unspecified terms",
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+    "text": "Arguments: Weighted Mean\nYou can use the Arguments mechanism to do various things, such as calculating weighted means:\n\nnewdata &lt;- data.frame(\n  x = rnorm(20),\n  w = rnorm(20),\n  y = rnorm(20))\n\ndatasummary(x + y ~ weighted.mean * Arguments(w = w),\n            data = newdata)\n\n \n\n  \n    \n\ntinytable_f4z199extla26itetb57\n\n\n      \n\n \n                weighted.mean\n              \n\n\nx\n                  1.32 \n                \n\ny\n                  -0.24\n                \n\n\n\n\n    \n\n\nWhich produces the same results as:\n\nweighted.mean(newdata$x, newdata$w)\n\n[1] 1.315068\n\nweighted.mean(newdata$y, newdata$w)\n\n[1] -0.236075\n\n\nBut different results from:\n\nmean(newdata$x)\n\n[1] 0.3049217\n\nmean(newdata$y)\n\n[1] -0.2411163",
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-    "text": "Rounding with decimal digits on a per-term basis in the fmt argument for modelsummary()\n\n\n\nfmt_term(..., default = 3)\n\n\n\n\n\n\n\n…\n\n\nTerm names and fmt value\n\n\n\n\ndefault\n\n\nNumber of decimal digits to keep for unspecified terms",
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+    "text": "Logical subsets\nCool stuff is possible with logical subsets:\n\ndatasummary((bill_length_mm &gt; 44.5) + (bill_length_mm &lt;= 44.5) ~ Mean * flipper_length_mm,\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_pyhqf60nxviw80ohrhcv\n\n\n      \n\n\n                \n              \n\n\nbill_length_mm &gt; 44.5 \n                  209.68\n                \n\nbill_length_mm \n                  \n192.45\n                \n\n\n\n\n    \n\n\nSee the tables package documentation for more details and examples.",
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-    "text": "A unified approach to extract results from a wide variety of models. For some models get_gof attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_gof(model, gof_function = NULL, vcov_type = NULL, ...)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\nvcov_type\n\n\nstring vcov type to add at the bottom of the table\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.",
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+    "text": "Missing values\nAt least 3 distinct issues can arise related to missing values.\nFunctions and na.rm\n\nAn empty cell can appear in the table when a statistical function returns NA instead of a numeric value. In those cases, you should:\n\nMake sure that your statistical function (e.g., mean or sd) uses na.rm=TRUE by default\nUse the Arguments strategy to set na.rm=TRUE (see the Arguments section of this vignette).\nUse a convenience function supplied by modelsummary, where na.rm is TRUE by default: Mean, SD, P25, etc.\nEmpty crosstab cells\nAn empty cell can appear in the table when a crosstab is deeply nested, and there are no observations for a given combination of covariates. In those cases, you can use the * DropEmpty pseudo-function. See the “Empty cells” section of this vignette for examples.\n\ndatasummary(island * species * body_mass_g ~ sex * (Mean + SD),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_h82z1swtzcrplc82ub4t\n\n\n      \n\n\n \n \n \nfemale\nmale\n\n\nisland\n                species\n                 \n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nBiscoe   \n                  Adelie   \n                  body_mass_g\n                  3369.32\n                  343.47\n                  4050.00\n                  355.57\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                  4679.74\n                  281.58\n                  5484.84\n                  313.16\n                \n\nDream    \n                  Adelie   \n                  body_mass_g\n                  3344.44\n                  212.06\n                  4045.54\n                  330.55\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                  3527.21\n                  285.33\n                  3938.97\n                  362.14\n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\nTorgersen\n                  Adelie   \n                  body_mass_g\n                  3395.83\n                  259.14\n                  4034.78\n                  372.47\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                         \n                        \n                         \n                        \n                \n\n\n\n\n    \n\ndatasummary(island * species * body_mass_g ~ sex * (Mean + SD) * DropEmpty(),\n            data = penguins)\n\n \n\n  \n    \n\ntinytable_gga58ozg28e7g36rozmd\n\n\n      \n\n\n \n \n \nfemale\nmale\n\n\nisland\n                species\n                 \n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nBiscoe   \n                  Adelie   \n                  body_mass_g\n                  3369.32\n                  343.47\n                  4050.00\n                  355.57\n                \n\n         \n                  Gentoo   \n                  body_mass_g\n                  4679.74\n                  281.58\n                  5484.84\n                  313.16\n                \n\nDream    \n                  Adelie   \n                  body_mass_g\n                  3344.44\n                  212.06\n                  4045.54\n                  330.55\n                \n\n         \n                  Chinstrap\n                  body_mass_g\n                  3527.21\n                  285.33\n                  3938.97\n                  362.14\n                \n\nTorgersen\n                  Adelie   \n                  body_mass_g\n                  3395.83\n                  259.14\n                  4034.78\n                  372.47\n                \n\n\n\n\n    \n\n\nPercent()\nBy default, the Percent() function computes the share of each category with respect to the whole data, including missing observations, but we can work around this with some trickery:jig\n\ndat &lt;- data.frame(z = c(0,0,1,0,NA,0,1))\n\nprop.table(table(dat$z))\n\n\n        0         1 \n0.6666667 0.3333333 \n\ndatasummary(Factor(z) ~ N + Percent(), data = dat)\n\n \n\n  \n    \n\ntinytable_i4taciijcbvi2p1ce0gp\n\n\n      \n\nz\n                N\n                Percent\n              \n\n\n0\n                  4\n                  57.14\n                \n\n1\n                  2\n                  28.57\n                \n\n\n\n\n    \n\n\nTo count percentages without missing values, we can exploit the fact that Percent() accepts a denom argument which can be a logical vector:\n\ndatasummary(Factor(z) ~ N + Percent(denom = !is.na(dat$z)), data = dat)\n\n \n\n  \n    \n\ntinytable_fke4aukdx87ay8s90e40\n\n\n      \n\nz\n                N\n                Percent\n              \n\n\n0\n                  4\n                  66.67\n                \n\n1\n                  2\n                  33.33",
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-    "title": "modelsummary: Data and Model Summaries in R",
-    "section": "",
-    "text": "A unified approach to extract results from a wide variety of models. For some models get_gof attaches useful attributes to the output. You can access this information by calling the attributes function: attributes(get_estimates(model))\n\n\n\nget_gof(model, gof_function = NULL, vcov_type = NULL, ...)\n\n\n\n\n\n\n\nmodel\n\n\na single model object\n\n\n\n\ngof_function\n\n\nfunction which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.\n\n\n\n\nvcov_type\n\n\nstring vcov type to add at the bottom of the table\n\n\n\n\n…\n\n\nall other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.\n\n\nparameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:\n\n\nstandardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.\n\n\n\n\nperformance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:\n\n\nmetrics, estimator, etc.\n\n\n\n\ntinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.",
+    "objectID": "vignettes/datasummary.html#add-rows",
+    "href": "vignettes/datasummary.html#add-rows",
+    "title": "Data Summaries",
+    "section": "Add rows",
+    "text": "Add rows\n\nnew_rows &lt;- data.frame('Does',\n                       2,\n                       'plus',\n                       2,\n                       'equals',\n                       5,\n                       '?')\ndatasummary(flipper_length_mm + body_mass_g ~ species * (Mean + SD),\n            data = penguins,\n            add_rows = new_rows)\n\n \n\n  \n    \n\ntinytable_qs98r1uy2tqq6ao212x1\n\n\n      \n\n\n \nAdelie\nChinstrap\nGentoo\n\n\n \n                Mean\n                SD\n                Mean\n                SD\n                Mean\n                SD\n              \n\n\n\nflipper_length_mm\n                  189.95 \n                  6.54  \n                  195.82 \n                  7.13  \n                  217.19 \n                  6.48  \n                \n\nbody_mass_g      \n                  3700.66\n                  458.57\n                  3733.09\n                  384.34\n                  5076.02\n                  504.12\n                \n\nDoes             \n                  2.00   \n                  plus  \n                  2.00   \n                  equals\n                  5.00   \n                  ?",
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-    "title": "Citation",
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-    "text": "Citation\nTo cite modelsummary in publications use:\n\n  Arel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.”\n  _Journal of Statistical Software_, *103*(1), 1-23.\n  doi:10.18637/jss.v103.i01 &lt;https://doi.org/10.18637/jss.v103.i01&gt;.\n\nA BibTeX entry for LaTeX users is\n\n  @Article{,\n    title = {{modelsummary}: Data and Model Summaries in {R}},\n    author = {Vincent Arel-Bundock},\n    journal = {Journal of Statistical Software},\n    year = {2022},\n    volume = {103},\n    number = {1},\n    pages = {1--23},\n    doi = {10.18637/jss.v103.i01},\n  }",
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+    "href": "vignettes/datasummary.html#add-columns",
+    "title": "Data Summaries",
+    "section": "Add columns",
+    "text": "Add columns\n\nnew_cols &lt;- data.frame('New Stat' = runif(2))\ndatasummary(flipper_length_mm + body_mass_g ~ species * (Mean + SD),\n            data = penguins,\n            add_columns = new_cols)\n\n \n\n  \n    \n\ntinytable_7g2u2olvpiqaemek0hlt\n\n\n      \n\n\n \nAdelie\nChinstrap\nGentoo\n \n\n\n \n                Mean\n                SD\n                Mean\n                SD\n                Mean\n                SD\n                New.Stat\n              \n\n\n\nflipper_length_mm\n                  189.95 \n                  6.54  \n                  195.82 \n                  7.13  \n                  217.19 \n                  6.48  \n                  0.99\n                \n\nbody_mass_g      \n                  3700.66\n                  458.57\n                  3733.09\n                  384.34\n                  5076.02\n                  504.12\n                  0.79",
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inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #2c3e50;--bs-btn-disabled-border-color: #2c3e50}.btn-secondary{--bs-btn-color: #fff;--bs-btn-bg: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #5c636a;--bs-btn-hover-border-color: #565e64;--bs-btn-focus-shadow-rgb: 130, 138, 145;--bs-btn-active-color: #fff;--bs-btn-active-bg: #565e64;--bs-btn-active-border-color: #51585e;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #6c757d;--bs-btn-disabled-border-color: #6c757d}.btn-success{--bs-btn-color: #fff;--bs-btn-bg: #18bc9c;--bs-btn-border-color: #18bc9c;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #14a085;--bs-btn-hover-border-color: #13967d;--bs-btn-focus-shadow-rgb: 59, 198, 171;--bs-btn-active-color: #fff;--bs-btn-active-bg: #13967d;--bs-btn-active-border-color: #128d75;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #18bc9c;--bs-btn-disabled-border-color: #18bc9c}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #3498db;--bs-btn-border-color: #3498db;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c81ba;--bs-btn-hover-border-color: #2a7aaf;--bs-btn-focus-shadow-rgb: 82, 167, 224;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2a7aaf;--bs-btn-active-border-color: #2772a4;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3498db;--bs-btn-disabled-border-color: #3498db}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #f39c12;--bs-btn-border-color: #f39c12;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #cf850f;--bs-btn-hover-border-color: #c27d0e;--bs-btn-focus-shadow-rgb: 245, 171, 54;--bs-btn-active-color: #fff;--bs-btn-active-bg: #c27d0e;--bs-btn-active-border-color: #b6750e;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #f39c12;--bs-btn-disabled-border-color: #f39c12}.btn-danger{--bs-btn-color: #fff;--bs-btn-bg: #e74c3c;--bs-btn-border-color: #e74c3c;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #c44133;--bs-btn-hover-border-color: #b93d30;--bs-btn-focus-shadow-rgb: 235, 103, 89;--bs-btn-active-color: #fff;--bs-btn-active-bg: #b93d30;--bs-btn-active-border-color: #ad392d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #e74c3c;--bs-btn-disabled-border-color: #e74c3c}.btn-light{--bs-btn-color: #000;--bs-btn-bg: #ecf0f1;--bs-btn-border-color: #ecf0f1;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #c9cccd;--bs-btn-hover-border-color: #bdc0c1;--bs-btn-focus-shadow-rgb: 201, 204, 205;--bs-btn-active-color: #000;--bs-btn-active-bg: #bdc0c1;--bs-btn-active-border-color: #b1b4b5;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #000;--bs-btn-disabled-bg: #ecf0f1;--bs-btn-disabled-border-color: #ecf0f1}.btn-dark{--bs-btn-color: #fff;--bs-btn-bg: #7b8a8b;--bs-btn-border-color: #7b8a8b;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #8f9c9c;--bs-btn-hover-border-color: #889697;--bs-btn-focus-shadow-rgb: 143, 156, 156;--bs-btn-active-color: #fff;--bs-btn-active-bg: #95a1a2;--bs-btn-active-border-color: #889697;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #7b8a8b;--bs-btn-disabled-border-color: #7b8a8b}.btn-outline-default{--bs-btn-color: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #6c757d;--bs-btn-hover-border-color: #6c757d;--bs-btn-focus-shadow-rgb: 108, 117, 125;--bs-btn-active-color: #fff;--bs-btn-active-bg: #6c757d;--bs-btn-active-border-color: #6c757d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #6c757d;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-primary{--bs-btn-color: #2c3e50;--bs-btn-border-color: #2c3e50;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c3e50;--bs-btn-hover-border-color: #2c3e50;--bs-btn-focus-shadow-rgb: 44, 62, 80;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2c3e50;--bs-btn-active-border-color: #2c3e50;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2c3e50;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2c3e50;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #6c757d;--bs-btn-hover-border-color: #6c757d;--bs-btn-focus-shadow-rgb: 108, 117, 125;--bs-btn-active-color: #fff;--bs-btn-active-bg: #6c757d;--bs-btn-active-border-color: #6c757d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #6c757d;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-success{--bs-btn-color: #18bc9c;--bs-btn-border-color: #18bc9c;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #18bc9c;--bs-btn-hover-border-color: #18bc9c;--bs-btn-focus-shadow-rgb: 24, 188, 156;--bs-btn-active-color: #fff;--bs-btn-active-bg: #18bc9c;--bs-btn-active-border-color: #18bc9c;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #18bc9c;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #18bc9c;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #3498db;--bs-btn-border-color: #3498db;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #3498db;--bs-btn-hover-border-color: #3498db;--bs-btn-focus-shadow-rgb: 52, 152, 219;--bs-btn-active-color: #fff;--bs-btn-active-bg: #3498db;--bs-btn-active-border-color: #3498db;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #3498db;--bs-btn-disabled-bg: 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#e74c3c;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #e74c3c;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #ecf0f1;--bs-btn-border-color: #ecf0f1;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #ecf0f1;--bs-btn-hover-border-color: #ecf0f1;--bs-btn-focus-shadow-rgb: 236, 240, 241;--bs-btn-active-color: #000;--bs-btn-active-bg: #ecf0f1;--bs-btn-active-border-color: #ecf0f1;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #ecf0f1;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ecf0f1;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #7b8a8b;--bs-btn-border-color: #7b8a8b;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #7b8a8b;--bs-btn-hover-border-color: #7b8a8b;--bs-btn-focus-shadow-rgb: 123, 138, 139;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7b8a8b;--bs-btn-active-border-color: #7b8a8b;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 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6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(34, 34, 34, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(34, 34, 34, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #4b6381;--bs-pagination-focus-bg: #ecf0f1;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ecf0f1;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #222;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(34, 34, 34, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: #222;--bs-list-group-action-active-bg: #ecf0f1;--bs-list-group-disabled-color: rgba(34, 34, 34, 0.75);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(34, 34, 34, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Lato";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(34, 34, 34, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(34, 34, 34, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #4b6381;--bs-pagination-focus-bg: #ecf0f1;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ecf0f1;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #222;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(34, 34, 34, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: #222;--bs-list-group-action-active-bg: #ecf0f1;--bs-list-group-disabled-color: rgba(34, 34, 34, 0.75);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion .accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card 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diff --git a/vignettes/appearance.html b/vignettes/appearance.html
index 8869e4e2..c30df9ea 100644
--- a/vignettes/appearance.html
+++ b/vignettes/appearance.html
@@ -450,19 +450,19 @@ <h1 class="title">Themes and Styles</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_5zmhitdyvh445r7at70z</title>
+<title>tinytable_jyo96m2834xzj6gd3wuw</title>
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@@ -474,7 +474,7 @@ <h1 class="title">Themes and Styles</h1>
       }
     };
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-      <table class="table table-borderless" id="tinytable_5zmhitdyvh445r7at70z" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_jyo96m2834xzj6gd3wuw" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -608,12 +608,12 @@ <h1 class="title">Themes and Styles</h1>
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-        var table = document.getElementById("tinytable_5zmhitdyvh445r7at70z");
+      function styleCell_tinytable_42jn8g55gvdwkyg5k1zt(i, j, css_id) {
+        var table = document.getElementById("tinytable_jyo96m2834xzj6gd3wuw");
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       function insertSpanRow(i, colspan, content) {
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@@ -621,8 +621,8 @@ <h1 class="title">Themes and Styles</h1>
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-      function spanCell_tinytable_lhtc87tql6xim5cuv4ic(i, j, rowspan, colspan) {
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@@ -649,124 +649,124 @@ <h1 class="title">Themes and Styles</h1>
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+window.addEventListener('load', function () { styleCell_tinytable_42jn8g55gvdwkyg5k1zt(4, 1, 'tinytable_css_nx13frcyirjqey5fsn84') })
+window.addEventListener('load', function () { styleCell_tinytable_42jn8g55gvdwkyg5k1zt(5, 1, 'tinytable_css_nx13frcyirjqey5fsn84') })
     </script>
 </div>
 </div>
@@ -786,13 +786,13 @@ <h1 class="title">Themes and Styles</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_wyvy6goajujygdue87mz</title>
+<title>tinytable_b2gtywmo2zsk58ugy5y9</title>
 <style>
-.table td.tinytable_css_vr1t9g80llgcnep2znxs, .table th.tinytable_css_vr1t9g80llgcnep2znxs {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_r002y6tjzmxh5c5r7v9z, .table th.tinytable_css_r002y6tjzmxh5c5r7v9z {    text-align: left; }
-.table td.tinytable_css_8kow7mpugruadm3gij9m, .table th.tinytable_css_8kow7mpugruadm3gij9m {    text-align: right; }
-.table td.tinytable_css_8v8x5fm04l6ydvcb34xp, .table th.tinytable_css_8v8x5fm04l6ydvcb34xp {    text-align: right; }
-.table td.tinytable_css_r7bbvydvmiaee4d6njes, .table th.tinytable_css_r7bbvydvmiaee4d6njes {    text-align: right; }
+.table td.tinytable_css_sgggohi7eyujhsuo7p54, .table th.tinytable_css_sgggohi7eyujhsuo7p54 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_n1a9e49iheehmr91ujrr, .table th.tinytable_css_n1a9e49iheehmr91ujrr {    text-align: left; }
+.table td.tinytable_css_gwmuq7hb3khg1f7gmlcp, .table th.tinytable_css_gwmuq7hb3khg1f7gmlcp {    text-align: right; }
+.table td.tinytable_css_mvm2io7qvo7z4kswudst, .table th.tinytable_css_mvm2io7qvo7z4kswudst {    text-align: right; }
+.table td.tinytable_css_vfpt7v81qiqwlz3p49mz, .table th.tinytable_css_vfpt7v81qiqwlz3p49mz {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -804,7 +804,7 @@ <h1 class="title">Themes and Styles</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_wyvy6goajujygdue87mz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_b2gtywmo2zsk58ugy5y9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Mean</th>
@@ -816,25 +816,25 @@ <h1 class="title">Themes and Styles</h1>
 <td>mpg</td>
                   <td>20.09 </td>
                   <td>6.03 </td>
-                  <td><img src="./tinytable_assets/idx8hlmfe1cmgjvvc62wbj.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idjfprst9k9bsfeou90g32.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>hp </td>
                   <td>146.69</td>
                   <td>68.56</td>
-                  <td><img src="./tinytable_assets/ideupkghvywtquhlcicry2.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/iduav95z03lohvd4pped8f.png" style="height: 1em;"></td>
                 </tr>
 </tbody>
 </table>
 </div>
 
     <script>
-      function styleCell_tinytable_ocrjebpnttvo3a8w9efm(i, j, css_id) {
-        var table = document.getElementById("tinytable_wyvy6goajujygdue87mz");
+      function styleCell_tinytable_jn30kpjf1s7xzvojkaf7(i, j, css_id) {
+        var table = document.getElementById("tinytable_b2gtywmo2zsk58ugy5y9");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_wyvy6goajujygdue87mz');
+        var table = document.getElementById('tinytable_b2gtywmo2zsk58ugy5y9');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -842,8 +842,8 @@ <h1 class="title">Themes and Styles</h1>
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         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_ocrjebpnttvo3a8w9efm(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_wyvy6goajujygdue87mz");
+      function spanCell_tinytable_jn30kpjf1s7xzvojkaf7(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_b2gtywmo2zsk58ugy5y9");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -870,22 +870,22 @@ <h1 class="title">Themes and Styles</h1>
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-window.addEventListener('load', function () { styleCell_tinytable_ocrjebpnttvo3a8w9efm(2, 3, 'tinytable_css_r7bbvydvmiaee4d6njes') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 0, 'tinytable_css_sgggohi7eyujhsuo7p54') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 1, 'tinytable_css_sgggohi7eyujhsuo7p54') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 2, 'tinytable_css_sgggohi7eyujhsuo7p54') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 3, 'tinytable_css_sgggohi7eyujhsuo7p54') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 0, 'tinytable_css_n1a9e49iheehmr91ujrr') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(1, 0, 'tinytable_css_n1a9e49iheehmr91ujrr') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(2, 0, 'tinytable_css_n1a9e49iheehmr91ujrr') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 1, 'tinytable_css_gwmuq7hb3khg1f7gmlcp') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(1, 1, 'tinytable_css_gwmuq7hb3khg1f7gmlcp') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(2, 1, 'tinytable_css_gwmuq7hb3khg1f7gmlcp') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 2, 'tinytable_css_mvm2io7qvo7z4kswudst') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(1, 2, 'tinytable_css_mvm2io7qvo7z4kswudst') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(2, 2, 'tinytable_css_mvm2io7qvo7z4kswudst') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 3, 'tinytable_css_vfpt7v81qiqwlz3p49mz') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(1, 3, 'tinytable_css_vfpt7v81qiqwlz3p49mz') })
+window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(2, 3, 'tinytable_css_vfpt7v81qiqwlz3p49mz') })
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 </div>
@@ -910,16 +910,16 @@ <h1 class="title">Themes and Styles</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_tw9kg7z0oz8pfhl4hbgm</title>
+<title>tinytable_0fygmd91861j6n6jf2dq</title>
 <style>
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-.table td.tinytable_css_d79nxhoh2ijqpneww9ej, .table th.tinytable_css_d79nxhoh2ijqpneww9ej {    text-align: left; }
-.table td.tinytable_css_r9zvxfrcj0fxs17vc8up, .table th.tinytable_css_r9zvxfrcj0fxs17vc8up {    text-align: center; }
-.table td.tinytable_css_ezec7fjejov9oqp7su6y, .table th.tinytable_css_ezec7fjejov9oqp7su6y {    text-align: center; }
-.table td.tinytable_css_z11rok2ah91o54tfxhmj, .table th.tinytable_css_z11rok2ah91o54tfxhmj {    text-align: center; }
-.table td.tinytable_css_ud8zovahjlmzud7zlyoy, .table th.tinytable_css_ud8zovahjlmzud7zlyoy {    text-align: center; }
-.table td.tinytable_css_hkwgyy6vttunjqjf60sj, .table th.tinytable_css_hkwgyy6vttunjqjf60sj {    text-align: center; }
-.table td.tinytable_css_bb7cap2pl2d9pfd7rg9o, .table th.tinytable_css_bb7cap2pl2d9pfd7rg9o {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_umqctmw24ia7h0rucjf4, .table th.tinytable_css_umqctmw24ia7h0rucjf4 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_1j3gpuvql6edik2fxmlx, .table th.tinytable_css_1j3gpuvql6edik2fxmlx {    text-align: left; }
+.table td.tinytable_css_ao8e35k4xunqqsvbcbg4, .table th.tinytable_css_ao8e35k4xunqqsvbcbg4 {    text-align: center; }
+.table td.tinytable_css_ud5195r7mfsogvs1nq80, .table th.tinytable_css_ud5195r7mfsogvs1nq80 {    text-align: center; }
+.table td.tinytable_css_r0xxoijsty3h8yx68fxi, .table th.tinytable_css_r0xxoijsty3h8yx68fxi {    text-align: center; }
+.table td.tinytable_css_lq3x8hv21aklf23awfas, .table th.tinytable_css_lq3x8hv21aklf23awfas {    text-align: center; }
+.table td.tinytable_css_akgtegyivdxvituu771u, .table th.tinytable_css_akgtegyivdxvituu771u {    text-align: center; }
+.table td.tinytable_css_mimyo4yg340zhp26lq9o, .table th.tinytable_css_mimyo4yg340zhp26lq9o {    border-bottom: solid 0.05em black; }
 
 .mytable {
   background-size: cover;
@@ -939,7 +939,7 @@ <h1 class="title">Themes and Styles</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless mytable" id="tinytable_tw9kg7z0oz8pfhl4hbgm" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless mytable" id="tinytable_0fygmd91861j6n6jf2dq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">I</th>
@@ -1066,12 +1066,12 @@ <h1 class="title">Themes and Styles</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_8943g24h0ssreu0hdv80(i, j, css_id) {
-        var table = document.getElementById("tinytable_tw9kg7z0oz8pfhl4hbgm");
+      function styleCell_tinytable_ck79q7vb1gn7lmi82qnk(i, j, css_id) {
+        var table = document.getElementById("tinytable_0fygmd91861j6n6jf2dq");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_tw9kg7z0oz8pfhl4hbgm');
+        var table = document.getElementById('tinytable_0fygmd91861j6n6jf2dq');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1079,8 +1079,8 @@ <h1 class="title">Themes and Styles</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_8943g24h0ssreu0hdv80(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_tw9kg7z0oz8pfhl4hbgm");
+      function spanCell_tinytable_ck79q7vb1gn7lmi82qnk(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_0fygmd91861j6n6jf2dq");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1107,108 +1107,108 @@ <h1 class="title">Themes and Styles</h1>
         targetCell.colSpan = colspan;
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+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(4, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(5, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(6, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(7, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(8, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(9, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(10, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(11, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(12, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(13, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(14, 4, 'tinytable_css_lq3x8hv21aklf23awfas') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(0, 5, 'tinytable_css_akgtegyivdxvituu771u') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(1, 5, 'tinytable_css_akgtegyivdxvituu771u') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(2, 5, 'tinytable_css_akgtegyivdxvituu771u') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(3, 5, 'tinytable_css_akgtegyivdxvituu771u') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(4, 5, 'tinytable_css_akgtegyivdxvituu771u') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(5, 5, 'tinytable_css_akgtegyivdxvituu771u') })
+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(6, 5, 'tinytable_css_akgtegyivdxvituu771u') })
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+window.addEventListener('load', function () { styleCell_tinytable_ck79q7vb1gn7lmi82qnk(6, 5, 'tinytable_css_mimyo4yg340zhp26lq9o') })
     </script>
 </div>
 </div>
@@ -1255,23 +1255,23 @@ <h1 class="title">Themes and Styles</h1>
 <span>    <span class="fu"><a href="https://gt.rstudio.com/reference/tab_style.html">tab_style</a></span><span class="op">(</span>style <span class="op">=</span> <span class="fu"><a href="https://gt.rstudio.com/reference/cell_fill.html">cell_fill</a></span><span class="op">(</span>color <span class="op">=</span> <span class="st">'lightblue'</span><span class="op">)</span>,</span>
 <span>              locations <span class="op">=</span> <span class="fu"><a href="https://gt.rstudio.com/reference/cells_body.html">cells_body</a></span><span class="op">(</span>rows <span class="op">=</span> <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
-<div id="aihtcxnyjp" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#aihtcxnyjp table {
+<div id="boieojxtpv" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#boieojxtpv table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#aihtcxnyjp thead, #aihtcxnyjp tbody, #aihtcxnyjp tfoot, #aihtcxnyjp tr, #aihtcxnyjp td, #aihtcxnyjp th {
+#boieojxtpv thead, #boieojxtpv tbody, #boieojxtpv tfoot, #boieojxtpv tr, #boieojxtpv td, #boieojxtpv th {
   border-style: none;
 }
 
-#aihtcxnyjp p {
+#boieojxtpv p {
   margin: 0;
   padding: 0;
 }
 
-#aihtcxnyjp .gt_table {
+#boieojxtpv .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1297,12 +1297,12 @@ <h1 class="title">Themes and Styles</h1>
   border-left-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_caption {
+#boieojxtpv .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#aihtcxnyjp .gt_title {
+#boieojxtpv .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -1314,7 +1314,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-width: 0;
 }
 
-#aihtcxnyjp .gt_subtitle {
+#boieojxtpv .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1326,7 +1326,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-width: 0;
 }
 
-#aihtcxnyjp .gt_heading {
+#boieojxtpv .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1338,13 +1338,13 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_bottom_border {
+#boieojxtpv .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_col_headings {
+#boieojxtpv .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1359,7 +1359,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_col_heading {
+#boieojxtpv .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1379,7 +1379,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#aihtcxnyjp .gt_column_spanner_outer {
+#boieojxtpv .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1391,15 +1391,15 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 4px;
 }
 
-#aihtcxnyjp .gt_column_spanner_outer:first-child {
+#boieojxtpv .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#aihtcxnyjp .gt_column_spanner_outer:last-child {
+#boieojxtpv .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#aihtcxnyjp .gt_column_spanner {
+#boieojxtpv .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1411,11 +1411,11 @@ <h1 class="title">Themes and Styles</h1>
   width: 100%;
 }
 
-#aihtcxnyjp .gt_spanner_row {
+#boieojxtpv .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#aihtcxnyjp .gt_group_heading {
+#boieojxtpv .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1441,7 +1441,7 @@ <h1 class="title">Themes and Styles</h1>
   text-align: left;
 }
 
-#aihtcxnyjp .gt_empty_group_heading {
+#boieojxtpv .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -1456,15 +1456,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: middle;
 }
 
-#aihtcxnyjp .gt_from_md > :first-child {
+#boieojxtpv .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#aihtcxnyjp .gt_from_md > :last-child {
+#boieojxtpv .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#aihtcxnyjp .gt_row {
+#boieojxtpv .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1483,7 +1483,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#aihtcxnyjp .gt_stub {
+#boieojxtpv .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1496,7 +1496,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#aihtcxnyjp .gt_stub_row_group {
+#boieojxtpv .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1510,15 +1510,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: top;
 }
 
-#aihtcxnyjp .gt_row_group_first td {
+#boieojxtpv .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#aihtcxnyjp .gt_row_group_first th {
+#boieojxtpv .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#aihtcxnyjp .gt_summary_row {
+#boieojxtpv .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1528,16 +1528,16 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#aihtcxnyjp .gt_first_summary_row {
+#boieojxtpv .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_first_summary_row.thick {
+#boieojxtpv .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#aihtcxnyjp .gt_last_summary_row {
+#boieojxtpv .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1547,7 +1547,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_grand_summary_row {
+#boieojxtpv .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1557,7 +1557,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#aihtcxnyjp .gt_first_grand_summary_row {
+#boieojxtpv .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1567,7 +1567,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_last_grand_summary_row_top {
+#boieojxtpv .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1577,11 +1577,11 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_striped {
+#boieojxtpv .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#aihtcxnyjp .gt_table_body {
+#boieojxtpv .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1590,7 +1590,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_footnotes {
+#boieojxtpv .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1604,7 +1604,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_footnote {
+#boieojxtpv .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1613,7 +1613,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#aihtcxnyjp .gt_sourcenotes {
+#boieojxtpv .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1627,7 +1627,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#aihtcxnyjp .gt_sourcenote {
+#boieojxtpv .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1635,63 +1635,63 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#aihtcxnyjp .gt_left {
+#boieojxtpv .gt_left {
   text-align: left;
 }
 
-#aihtcxnyjp .gt_center {
+#boieojxtpv .gt_center {
   text-align: center;
 }
 
-#aihtcxnyjp .gt_right {
+#boieojxtpv .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#aihtcxnyjp .gt_font_normal {
+#boieojxtpv .gt_font_normal {
   font-weight: normal;
 }
 
-#aihtcxnyjp .gt_font_bold {
+#boieojxtpv .gt_font_bold {
   font-weight: bold;
 }
 
-#aihtcxnyjp .gt_font_italic {
+#boieojxtpv .gt_font_italic {
   font-style: italic;
 }
 
-#aihtcxnyjp .gt_super {
+#boieojxtpv .gt_super {
   font-size: 65%;
 }
 
-#aihtcxnyjp .gt_footnote_marks {
+#boieojxtpv .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#aihtcxnyjp .gt_asterisk {
+#boieojxtpv .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#aihtcxnyjp .gt_indent_1 {
+#boieojxtpv .gt_indent_1 {
   text-indent: 5px;
 }
 
-#aihtcxnyjp .gt_indent_2 {
+#boieojxtpv .gt_indent_2 {
   text-indent: 10px;
 }
 
-#aihtcxnyjp .gt_indent_3 {
+#boieojxtpv .gt_indent_3 {
   text-indent: 15px;
 }
 
-#aihtcxnyjp .gt_indent_4 {
+#boieojxtpv .gt_indent_4 {
   text-indent: 20px;
 }
 
-#aihtcxnyjp .gt_indent_5 {
+#boieojxtpv .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -1820,23 +1820,23 @@ <h1 class="title">Themes and Styles</h1>
 <span>    <span class="fu"><a href="https://gt.rstudio.com/reference/text_transform.html">text_transform</a></span><span class="op">(</span>locations <span class="op">=</span> <span class="fu"><a href="https://gt.rstudio.com/reference/cells_body.html">cells_body</a></span><span class="op">(</span>columns <span class="op">=</span> <span class="fl">2</span><span class="op">:</span><span class="fl">6</span>, rows <span class="op">=</span> <span class="fl">1</span><span class="op">)</span>, fn <span class="op">=</span> <span class="va">f</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<div id="rcoqhcxsol" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#rcoqhcxsol table {
+<div id="wuzsczbbqf" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#wuzsczbbqf table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#rcoqhcxsol thead, #rcoqhcxsol tbody, #rcoqhcxsol tfoot, #rcoqhcxsol tr, #rcoqhcxsol td, #rcoqhcxsol th {
+#wuzsczbbqf thead, #wuzsczbbqf tbody, #wuzsczbbqf tfoot, #wuzsczbbqf tr, #wuzsczbbqf td, #wuzsczbbqf th {
   border-style: none;
 }
 
-#rcoqhcxsol p {
+#wuzsczbbqf p {
   margin: 0;
   padding: 0;
 }
 
-#rcoqhcxsol .gt_table {
+#wuzsczbbqf .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1862,12 +1862,12 @@ <h1 class="title">Themes and Styles</h1>
   border-left-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_caption {
+#wuzsczbbqf .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#rcoqhcxsol .gt_title {
+#wuzsczbbqf .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -1879,7 +1879,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-width: 0;
 }
 
-#rcoqhcxsol .gt_subtitle {
+#wuzsczbbqf .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1891,7 +1891,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-width: 0;
 }
 
-#rcoqhcxsol .gt_heading {
+#wuzsczbbqf .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1903,13 +1903,13 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_bottom_border {
+#wuzsczbbqf .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_col_headings {
+#wuzsczbbqf .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1924,7 +1924,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_col_heading {
+#wuzsczbbqf .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1944,7 +1944,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#rcoqhcxsol .gt_column_spanner_outer {
+#wuzsczbbqf .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1956,15 +1956,15 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 4px;
 }
 
-#rcoqhcxsol .gt_column_spanner_outer:first-child {
+#wuzsczbbqf .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#rcoqhcxsol .gt_column_spanner_outer:last-child {
+#wuzsczbbqf .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#rcoqhcxsol .gt_column_spanner {
+#wuzsczbbqf .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1976,11 +1976,11 @@ <h1 class="title">Themes and Styles</h1>
   width: 100%;
 }
 
-#rcoqhcxsol .gt_spanner_row {
+#wuzsczbbqf .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#rcoqhcxsol .gt_group_heading {
+#wuzsczbbqf .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2006,7 +2006,7 @@ <h1 class="title">Themes and Styles</h1>
   text-align: left;
 }
 
-#rcoqhcxsol .gt_empty_group_heading {
+#wuzsczbbqf .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -2021,15 +2021,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: middle;
 }
 
-#rcoqhcxsol .gt_from_md > :first-child {
+#wuzsczbbqf .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#rcoqhcxsol .gt_from_md > :last-child {
+#wuzsczbbqf .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#rcoqhcxsol .gt_row {
+#wuzsczbbqf .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2048,7 +2048,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#rcoqhcxsol .gt_stub {
+#wuzsczbbqf .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2061,7 +2061,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#rcoqhcxsol .gt_stub_row_group {
+#wuzsczbbqf .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2075,15 +2075,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: top;
 }
 
-#rcoqhcxsol .gt_row_group_first td {
+#wuzsczbbqf .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#rcoqhcxsol .gt_row_group_first th {
+#wuzsczbbqf .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#rcoqhcxsol .gt_summary_row {
+#wuzsczbbqf .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2093,16 +2093,16 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#rcoqhcxsol .gt_first_summary_row {
+#wuzsczbbqf .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_first_summary_row.thick {
+#wuzsczbbqf .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#rcoqhcxsol .gt_last_summary_row {
+#wuzsczbbqf .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2112,7 +2112,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_grand_summary_row {
+#wuzsczbbqf .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2122,7 +2122,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#rcoqhcxsol .gt_first_grand_summary_row {
+#wuzsczbbqf .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2132,7 +2132,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_last_grand_summary_row_top {
+#wuzsczbbqf .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2142,11 +2142,11 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_striped {
+#wuzsczbbqf .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#rcoqhcxsol .gt_table_body {
+#wuzsczbbqf .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2155,7 +2155,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_footnotes {
+#wuzsczbbqf .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2169,7 +2169,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_footnote {
+#wuzsczbbqf .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2178,7 +2178,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#rcoqhcxsol .gt_sourcenotes {
+#wuzsczbbqf .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2192,7 +2192,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#rcoqhcxsol .gt_sourcenote {
+#wuzsczbbqf .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -2200,63 +2200,63 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#rcoqhcxsol .gt_left {
+#wuzsczbbqf .gt_left {
   text-align: left;
 }
 
-#rcoqhcxsol .gt_center {
+#wuzsczbbqf .gt_center {
   text-align: center;
 }
 
-#rcoqhcxsol .gt_right {
+#wuzsczbbqf .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#rcoqhcxsol .gt_font_normal {
+#wuzsczbbqf .gt_font_normal {
   font-weight: normal;
 }
 
-#rcoqhcxsol .gt_font_bold {
+#wuzsczbbqf .gt_font_bold {
   font-weight: bold;
 }
 
-#rcoqhcxsol .gt_font_italic {
+#wuzsczbbqf .gt_font_italic {
   font-style: italic;
 }
 
-#rcoqhcxsol .gt_super {
+#wuzsczbbqf .gt_super {
   font-size: 65%;
 }
 
-#rcoqhcxsol .gt_footnote_marks {
+#wuzsczbbqf .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#rcoqhcxsol .gt_asterisk {
+#wuzsczbbqf .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#rcoqhcxsol .gt_indent_1 {
+#wuzsczbbqf .gt_indent_1 {
   text-indent: 5px;
 }
 
-#rcoqhcxsol .gt_indent_2 {
+#wuzsczbbqf .gt_indent_2 {
   text-indent: 10px;
 }
 
-#rcoqhcxsol .gt_indent_3 {
+#wuzsczbbqf .gt_indent_3 {
   text-indent: 15px;
 }
 
-#rcoqhcxsol .gt_indent_4 {
+#wuzsczbbqf .gt_indent_4 {
   text-indent: 20px;
 }
 
-#rcoqhcxsol .gt_indent_5 {
+#wuzsczbbqf .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -2376,23 +2376,23 @@ <h1 class="title">Themes and Styles</h1>
 <span><span class="fu"><a href="https://modelsummary.com/man/modelsummary.html">modelsummary</a></span><span class="op">(</span><span class="va">mod</span>, output <span class="op">=</span> <span class="st">"gt"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<div id="tkfyncgkde" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#tkfyncgkde table {
+<div id="rltfckoqzq" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#rltfckoqzq table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#tkfyncgkde thead, #tkfyncgkde tbody, #tkfyncgkde tfoot, #tkfyncgkde tr, #tkfyncgkde td, #tkfyncgkde th {
+#rltfckoqzq thead, #rltfckoqzq tbody, #rltfckoqzq tfoot, #rltfckoqzq tr, #rltfckoqzq td, #rltfckoqzq th {
   border-style: none;
 }
 
-#tkfyncgkde p {
+#rltfckoqzq p {
   margin: 0;
   padding: 0;
 }
 
-#tkfyncgkde .gt_table {
+#rltfckoqzq .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -2418,12 +2418,12 @@ <h1 class="title">Themes and Styles</h1>
   border-left-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_caption {
+#rltfckoqzq .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#tkfyncgkde .gt_title {
+#rltfckoqzq .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -2435,7 +2435,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-width: 0;
 }
 
-#tkfyncgkde .gt_subtitle {
+#rltfckoqzq .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -2447,7 +2447,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-width: 0;
 }
 
-#tkfyncgkde .gt_heading {
+#rltfckoqzq .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -2459,13 +2459,13 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_bottom_border {
+#rltfckoqzq .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_col_headings {
+#rltfckoqzq .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2480,7 +2480,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_col_heading {
+#rltfckoqzq .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2500,7 +2500,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#tkfyncgkde .gt_column_spanner_outer {
+#rltfckoqzq .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2512,15 +2512,15 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 4px;
 }
 
-#tkfyncgkde .gt_column_spanner_outer:first-child {
+#rltfckoqzq .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#tkfyncgkde .gt_column_spanner_outer:last-child {
+#rltfckoqzq .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#tkfyncgkde .gt_column_spanner {
+#rltfckoqzq .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -2532,11 +2532,11 @@ <h1 class="title">Themes and Styles</h1>
   width: 100%;
 }
 
-#tkfyncgkde .gt_spanner_row {
+#rltfckoqzq .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#tkfyncgkde .gt_group_heading {
+#rltfckoqzq .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2562,7 +2562,7 @@ <h1 class="title">Themes and Styles</h1>
   text-align: left;
 }
 
-#tkfyncgkde .gt_empty_group_heading {
+#rltfckoqzq .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -2577,15 +2577,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: middle;
 }
 
-#tkfyncgkde .gt_from_md > :first-child {
+#rltfckoqzq .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#tkfyncgkde .gt_from_md > :last-child {
+#rltfckoqzq .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#tkfyncgkde .gt_row {
+#rltfckoqzq .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2604,7 +2604,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#tkfyncgkde .gt_stub {
+#rltfckoqzq .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2617,7 +2617,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#tkfyncgkde .gt_stub_row_group {
+#rltfckoqzq .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2631,15 +2631,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: top;
 }
 
-#tkfyncgkde .gt_row_group_first td {
+#rltfckoqzq .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#tkfyncgkde .gt_row_group_first th {
+#rltfckoqzq .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#tkfyncgkde .gt_summary_row {
+#rltfckoqzq .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2649,16 +2649,16 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#tkfyncgkde .gt_first_summary_row {
+#rltfckoqzq .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_first_summary_row.thick {
+#rltfckoqzq .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#tkfyncgkde .gt_last_summary_row {
+#rltfckoqzq .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2668,7 +2668,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_grand_summary_row {
+#rltfckoqzq .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2678,7 +2678,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#tkfyncgkde .gt_first_grand_summary_row {
+#rltfckoqzq .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2688,7 +2688,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_last_grand_summary_row_top {
+#rltfckoqzq .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2698,11 +2698,11 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_striped {
+#rltfckoqzq .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#tkfyncgkde .gt_table_body {
+#rltfckoqzq .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2711,7 +2711,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_footnotes {
+#rltfckoqzq .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2725,7 +2725,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_footnote {
+#rltfckoqzq .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2734,7 +2734,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#tkfyncgkde .gt_sourcenotes {
+#rltfckoqzq .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2748,7 +2748,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#tkfyncgkde .gt_sourcenote {
+#rltfckoqzq .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -2756,63 +2756,63 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#tkfyncgkde .gt_left {
+#rltfckoqzq .gt_left {
   text-align: left;
 }
 
-#tkfyncgkde .gt_center {
+#rltfckoqzq .gt_center {
   text-align: center;
 }
 
-#tkfyncgkde .gt_right {
+#rltfckoqzq .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#tkfyncgkde .gt_font_normal {
+#rltfckoqzq .gt_font_normal {
   font-weight: normal;
 }
 
-#tkfyncgkde .gt_font_bold {
+#rltfckoqzq .gt_font_bold {
   font-weight: bold;
 }
 
-#tkfyncgkde .gt_font_italic {
+#rltfckoqzq .gt_font_italic {
   font-style: italic;
 }
 
-#tkfyncgkde .gt_super {
+#rltfckoqzq .gt_super {
   font-size: 65%;
 }
 
-#tkfyncgkde .gt_footnote_marks {
+#rltfckoqzq .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#tkfyncgkde .gt_asterisk {
+#rltfckoqzq .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#tkfyncgkde .gt_indent_1 {
+#rltfckoqzq .gt_indent_1 {
   text-indent: 5px;
 }
 
-#tkfyncgkde .gt_indent_2 {
+#rltfckoqzq .gt_indent_2 {
   text-indent: 10px;
 }
 
-#tkfyncgkde .gt_indent_3 {
+#rltfckoqzq .gt_indent_3 {
   text-indent: 15px;
 }
 
-#tkfyncgkde .gt_indent_4 {
+#rltfckoqzq .gt_indent_4 {
   text-indent: 20px;
 }
 
-#tkfyncgkde .gt_indent_5 {
+#rltfckoqzq .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -2891,23 +2891,23 @@ <h1 class="title">Themes and Styles</h1>
 <span><span class="fu"><a href="https://modelsummary.com/man/datasummary_crosstab.html">datasummary_crosstab</a></span><span class="op">(</span><span class="va">island</span> <span class="op">~</span> <span class="va">sex</span> <span class="op">*</span> <span class="va">species</span>, output <span class="op">=</span> <span class="st">"gt"</span>, data <span class="op">=</span> <span class="va">penguins</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<div id="plkdiiebsy" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#plkdiiebsy table {
+<div id="sonvwxdofp" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#sonvwxdofp table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#plkdiiebsy thead, #plkdiiebsy tbody, #plkdiiebsy tfoot, #plkdiiebsy tr, #plkdiiebsy td, #plkdiiebsy th {
+#sonvwxdofp thead, #sonvwxdofp tbody, #sonvwxdofp tfoot, #sonvwxdofp tr, #sonvwxdofp td, #sonvwxdofp th {
   border-style: none;
 }
 
-#plkdiiebsy p {
+#sonvwxdofp p {
   margin: 0;
   padding: 0;
 }
 
-#plkdiiebsy .gt_table {
+#sonvwxdofp .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -2933,12 +2933,12 @@ <h1 class="title">Themes and Styles</h1>
   border-left-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_caption {
+#sonvwxdofp .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#plkdiiebsy .gt_title {
+#sonvwxdofp .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -2950,7 +2950,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-width: 0;
 }
 
-#plkdiiebsy .gt_subtitle {
+#sonvwxdofp .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -2962,7 +2962,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-width: 0;
 }
 
-#plkdiiebsy .gt_heading {
+#sonvwxdofp .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -2974,13 +2974,13 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_bottom_border {
+#sonvwxdofp .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_col_headings {
+#sonvwxdofp .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2995,7 +2995,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_col_heading {
+#sonvwxdofp .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3015,7 +3015,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#plkdiiebsy .gt_column_spanner_outer {
+#sonvwxdofp .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3027,15 +3027,15 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 4px;
 }
 
-#plkdiiebsy .gt_column_spanner_outer:first-child {
+#sonvwxdofp .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#plkdiiebsy .gt_column_spanner_outer:last-child {
+#sonvwxdofp .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#plkdiiebsy .gt_column_spanner {
+#sonvwxdofp .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -3047,11 +3047,11 @@ <h1 class="title">Themes and Styles</h1>
   width: 100%;
 }
 
-#plkdiiebsy .gt_spanner_row {
+#sonvwxdofp .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#plkdiiebsy .gt_group_heading {
+#sonvwxdofp .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3077,7 +3077,7 @@ <h1 class="title">Themes and Styles</h1>
   text-align: left;
 }
 
-#plkdiiebsy .gt_empty_group_heading {
+#sonvwxdofp .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -3092,15 +3092,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: middle;
 }
 
-#plkdiiebsy .gt_from_md > :first-child {
+#sonvwxdofp .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#plkdiiebsy .gt_from_md > :last-child {
+#sonvwxdofp .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#plkdiiebsy .gt_row {
+#sonvwxdofp .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3119,7 +3119,7 @@ <h1 class="title">Themes and Styles</h1>
   overflow-x: hidden;
 }
 
-#plkdiiebsy .gt_stub {
+#sonvwxdofp .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3132,7 +3132,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#plkdiiebsy .gt_stub_row_group {
+#sonvwxdofp .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3146,15 +3146,15 @@ <h1 class="title">Themes and Styles</h1>
   vertical-align: top;
 }
 
-#plkdiiebsy .gt_row_group_first td {
+#sonvwxdofp .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#plkdiiebsy .gt_row_group_first th {
+#sonvwxdofp .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#plkdiiebsy .gt_summary_row {
+#sonvwxdofp .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -3164,16 +3164,16 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#plkdiiebsy .gt_first_summary_row {
+#sonvwxdofp .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_first_summary_row.thick {
+#sonvwxdofp .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#plkdiiebsy .gt_last_summary_row {
+#sonvwxdofp .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3183,7 +3183,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_grand_summary_row {
+#sonvwxdofp .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -3193,7 +3193,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#plkdiiebsy .gt_first_grand_summary_row {
+#sonvwxdofp .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3203,7 +3203,7 @@ <h1 class="title">Themes and Styles</h1>
   border-top-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_last_grand_summary_row_top {
+#sonvwxdofp .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3213,11 +3213,11 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_striped {
+#sonvwxdofp .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#plkdiiebsy .gt_table_body {
+#sonvwxdofp .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -3226,7 +3226,7 @@ <h1 class="title">Themes and Styles</h1>
   border-bottom-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_footnotes {
+#sonvwxdofp .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -3240,7 +3240,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_footnote {
+#sonvwxdofp .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -3249,7 +3249,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#plkdiiebsy .gt_sourcenotes {
+#sonvwxdofp .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -3263,7 +3263,7 @@ <h1 class="title">Themes and Styles</h1>
   border-right-color: #D3D3D3;
 }
 
-#plkdiiebsy .gt_sourcenote {
+#sonvwxdofp .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -3271,63 +3271,63 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#plkdiiebsy .gt_left {
+#sonvwxdofp .gt_left {
   text-align: left;
 }
 
-#plkdiiebsy .gt_center {
+#sonvwxdofp .gt_center {
   text-align: center;
 }
 
-#plkdiiebsy .gt_right {
+#sonvwxdofp .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#plkdiiebsy .gt_font_normal {
+#sonvwxdofp .gt_font_normal {
   font-weight: normal;
 }
 
-#plkdiiebsy .gt_font_bold {
+#sonvwxdofp .gt_font_bold {
   font-weight: bold;
 }
 
-#plkdiiebsy .gt_font_italic {
+#sonvwxdofp .gt_font_italic {
   font-style: italic;
 }
 
-#plkdiiebsy .gt_super {
+#sonvwxdofp .gt_super {
   font-size: 65%;
 }
 
-#plkdiiebsy .gt_footnote_marks {
+#sonvwxdofp .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#plkdiiebsy .gt_asterisk {
+#sonvwxdofp .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#plkdiiebsy .gt_indent_1 {
+#sonvwxdofp .gt_indent_1 {
   text-indent: 5px;
 }
 
-#plkdiiebsy .gt_indent_2 {
+#sonvwxdofp .gt_indent_2 {
   text-indent: 10px;
 }
 
-#plkdiiebsy .gt_indent_3 {
+#sonvwxdofp .gt_indent_3 {
   text-indent: 15px;
 }
 
-#plkdiiebsy .gt_indent_4 {
+#sonvwxdofp .gt_indent_4 {
   text-indent: 20px;
 }
 
-#plkdiiebsy .gt_indent_5 {
+#sonvwxdofp .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -3488,8 +3488,8 @@ <h1 class="title">Themes and Styles</h1>
 <span><span class="fu"><a href="https://rdrr.io/r/base/options.html">options</a></span><span class="op">(</span><span class="st">"modelsummary_theme_dataframe"</span> <span class="op">=</span> <span class="va">theme_df</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://modelsummary.com/man/modelsummary.html">modelsummary</a></span><span class="op">(</span><span class="va">mod</span>, output <span class="op">=</span> <span class="st">"dataframe"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
-<div class="datatables html-widget html-fill-item" id="htmlwidget-95a7d98573160adf163b" style="width:100%;height:auto;"></div>
-<script type="application/json" data-for="htmlwidget-95a7d98573160adf163b">{"x":{"filter":"none","vertical":false,"data":[["(Intercept)","(Intercept)","hp","hp","drat","drat","Num.Obs.","R2","R2 Adj.","AIC","BIC","Log.Lik.","F","RMSE"],["10.790","(5.078)","-0.052","(0.009)","4.698","(1.192)","32","0.741","0.723","169.5","175.4","-80.752","41.522","3.02"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>(1)<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":30,"columnDefs":[{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"(1)","targets":1}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[10,25,30,50,100]}},"evals":[],"jsHooks":[]}</script>
+<div class="datatables html-widget html-fill-item" id="htmlwidget-fe9a10a532741576444c" style="width:100%;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-fe9a10a532741576444c">{"x":{"filter":"none","vertical":false,"data":[["(Intercept)","(Intercept)","hp","hp","drat","drat","Num.Obs.","R2","R2 Adj.","AIC","BIC","Log.Lik.","F","RMSE"],["10.790","(5.078)","-0.052","(0.009)","4.698","(1.192)","32","0.741","0.723","169.5","175.4","-80.752","41.522","3.02"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>(1)<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":30,"columnDefs":[{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"(1)","targets":1}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[10,25,30,50,100]}},"evals":[],"jsHooks":[]}</script>
 </div>
 </div>
 <p>Restore default theme:</p>
@@ -3512,12 +3512,12 @@ <h1 class="title">Themes and Styles</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_jp3r8hdo55huh8ooxsli</title>
+<title>tinytable_gsd4n4w1rb783ti9cvgb</title>
 <style>
-.table td.tinytable_css_9qet5wa9lotcu3ynlvxy, .table th.tinytable_css_9qet5wa9lotcu3ynlvxy {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_dvdn2pwufwnwh11sxced, .table th.tinytable_css_dvdn2pwufwnwh11sxced {    text-align: left; }
-.table td.tinytable_css_ddjxwtg5zrvdd5tlmpj7, .table th.tinytable_css_ddjxwtg5zrvdd5tlmpj7 {    text-align: center; }
-.table td.tinytable_css_lxijyq2c0v37sb2jlujn, .table th.tinytable_css_lxijyq2c0v37sb2jlujn {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_azy8vs8o06ve930rabmh, .table th.tinytable_css_azy8vs8o06ve930rabmh {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_aly4vxfv07n55qdh1scz, .table th.tinytable_css_aly4vxfv07n55qdh1scz {    text-align: left; }
+.table td.tinytable_css_ymusypjvmjssc12v5emx, .table th.tinytable_css_ymusypjvmjssc12v5emx {    text-align: center; }
+.table td.tinytable_css_1zobxh9b69l6gy03uyom, .table th.tinytable_css_1zobxh9b69l6gy03uyom {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -3529,7 +3529,7 @@ <h1 class="title">Themes and Styles</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_jp3r8hdo55huh8ooxsli" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_gsd4n4w1rb783ti9cvgb" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(1)</th>
@@ -3596,12 +3596,12 @@ <h1 class="title">Themes and Styles</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_7sd6de420qqt2s3lzlos(i, j, css_id) {
-        var table = document.getElementById("tinytable_jp3r8hdo55huh8ooxsli");
+      function styleCell_tinytable_lhxbtw5d72tivv0mf6bx(i, j, css_id) {
+        var table = document.getElementById("tinytable_gsd4n4w1rb783ti9cvgb");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_jp3r8hdo55huh8ooxsli');
+        var table = document.getElementById('tinytable_gsd4n4w1rb783ti9cvgb');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3609,8 +3609,8 @@ <h1 class="title">Themes and Styles</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_7sd6de420qqt2s3lzlos(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_jp3r8hdo55huh8ooxsli");
+      function spanCell_tinytable_lhxbtw5d72tivv0mf6bx(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_gsd4n4w1rb783ti9cvgb");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3637,40 +3637,40 @@ <h1 class="title">Themes and Styles</h1>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(0, 0, 'tinytable_css_9qet5wa9lotcu3ynlvxy') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(0, 1, 'tinytable_css_9qet5wa9lotcu3ynlvxy') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(0, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(1, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(2, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(3, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(4, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(5, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(6, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(7, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(8, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(9, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(10, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(11, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(12, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(13, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(14, 0, 'tinytable_css_dvdn2pwufwnwh11sxced') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(0, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(1, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(2, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(3, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(4, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(5, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(6, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(7, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
-window.addEventListener('load', function () { styleCell_tinytable_7sd6de420qqt2s3lzlos(8, 1, 'tinytable_css_ddjxwtg5zrvdd5tlmpj7') })
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 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://modelsummary.com/man/datasummary_skim.html">datasummary_skim</a></span><span class="op">(</span><span class="va">dat</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"mpg"</span>, <span class="st">"am"</span>, <span class="st">"drat"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -3680,18 +3680,18 @@ <h1 class="title">Themes and Styles</h1>
   
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@@ -3703,7 +3703,7 @@ <h1 class="title">Themes and Styles</h1>
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@@ -3725,7 +3725,7 @@ <h1 class="title">Themes and Styles</h1>
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 <tr>
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@@ -3736,7 +3736,7 @@ <h1 class="title">Themes and Styles</h1>
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@@ -3747,19 +3747,19 @@ <h1 class="title">Themes and Styles</h1>
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diff --git a/vignettes/datasummary.html b/vignettes/datasummary.html
index 007cae5d..3e337ac9 100644
--- a/vignettes/datasummary.html
+++ b/vignettes/datasummary.html
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                   <td>1.0</td>
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                   <td>231.0</td>
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                   <td>2009.0</td>
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+      <table class="table table-borderless" id="tinytable_lxgp5w5pd65fe0we3zug" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">  </th>
@@ -842,12 +842,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_kdz9bm6gukx9kqcayjuv");
+      function styleCell_tinytable_uovnfej1qykm4n9yxtzk(i, j, css_id) {
+        var table = document.getElementById("tinytable_lxgp5w5pd65fe0we3zug");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_kdz9bm6gukx9kqcayjuv');
+        var table = document.getElementById('tinytable_lxgp5w5pd65fe0we3zug');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -855,8 +855,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_d4kxennyiax1vi9954s7(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_kdz9bm6gukx9kqcayjuv");
+      function spanCell_tinytable_uovnfej1qykm4n9yxtzk(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_lxgp5w5pd65fe0we3zug");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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@@ -883,50 +883,50 @@ <h1 class="title">Data Summaries</h1>
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+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(2, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(3, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(4, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(5, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(6, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(7, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(8, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
+window.addEventListener('load', function () { styleCell_tinytable_uovnfej1qykm4n9yxtzk(9, 3, 'tinytable_css_l81oemfczrniscjjdv5o') })
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 </div>
 </div>
@@ -973,14 +973,14 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_qmon8arg1mbxj2z4wzwe</title>
+<title>tinytable_13ya0c7qn6sewfqutxx9</title>
 <style>
-.table td.tinytable_css_b7mqo2wenj2miwgltjay, .table th.tinytable_css_b7mqo2wenj2miwgltjay {    border-bottom: solid 0.1em #d3d8dc; }
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-.table td.tinytable_css_rl3o5s0lhpv7ek6nvonl, .table th.tinytable_css_rl3o5s0lhpv7ek6nvonl {    text-align: left; }
-.table td.tinytable_css_ntbsmv82pwdetp7phrjn, .table th.tinytable_css_ntbsmv82pwdetp7phrjn {    text-align: right; }
-.table td.tinytable_css_ocshxjpt1kv6186ak34k, .table th.tinytable_css_ocshxjpt1kv6186ak34k {    text-align: right; }
-.table td.tinytable_css_yyzcntf7oxjs9lo6kwsp, .table th.tinytable_css_yyzcntf7oxjs9lo6kwsp {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_ix9jx9179slkbvutxsjf, .table th.tinytable_css_ix9jx9179slkbvutxsjf {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_3tsbsi7fm7oibxbxjs9e, .table th.tinytable_css_3tsbsi7fm7oibxbxjs9e {    text-align: left; }
+.table td.tinytable_css_cscgvpagwgrglr4jjk17, .table th.tinytable_css_cscgvpagwgrglr4jjk17 {    text-align: left; }
+.table td.tinytable_css_qhyvhkrvhs4jk9nv5mcn, .table th.tinytable_css_qhyvhkrvhs4jk9nv5mcn {    text-align: right; }
+.table td.tinytable_css_39jdsosqt7udl90mpq52, .table th.tinytable_css_39jdsosqt7udl90mpq52 {    text-align: right; }
+.table td.tinytable_css_s5tm9xid0f02oc4ar4mj, .table th.tinytable_css_s5tm9xid0f02oc4ar4mj {    border-bottom: solid 0.05em black; }
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 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -992,7 +992,7 @@ <h1 class="title">Data Summaries</h1>
       }
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_qmon8arg1mbxj2z4wzwe" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_13ya0c7qn6sewfqutxx9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">  </th>
@@ -1077,12 +1077,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_qmon8arg1mbxj2z4wzwe");
+      function styleCell_tinytable_9a9k7tg81hy0jau27ro4(i, j, css_id) {
+        var table = document.getElementById("tinytable_13ya0c7qn6sewfqutxx9");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_qmon8arg1mbxj2z4wzwe');
+        var table = document.getElementById('tinytable_13ya0c7qn6sewfqutxx9');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1090,8 +1090,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_oy46djivcnadk6q24k3z(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_qmon8arg1mbxj2z4wzwe");
+      function spanCell_tinytable_9a9k7tg81hy0jau27ro4(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_13ya0c7qn6sewfqutxx9");
         const targetRow = table.rows[i];
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@@ -1118,66 +1118,66 @@ <h1 class="title">Data Summaries</h1>
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+.table td.tinytable_css_4xfa5jzevtqzbsvupnuj, .table th.tinytable_css_4xfa5jzevtqzbsvupnuj {    text-align: right; }
+.table td.tinytable_css_eb0de8toyv871uiofadh, .table th.tinytable_css_eb0de8toyv871uiofadh {    text-align: right; }
+.table td.tinytable_css_dg0yp1ggtqwvofhxw0gq, .table th.tinytable_css_dg0yp1ggtqwvofhxw0gq {    text-align: right; }
+.table td.tinytable_css_euypj6m8crmnrcpgsuqo, .table th.tinytable_css_euypj6m8crmnrcpgsuqo {    text-align: right; }
+.table td.tinytable_css_0x6zbf15bw76awmiujqq, .table th.tinytable_css_0x6zbf15bw76awmiujqq {    text-align: right; }
+.table td.tinytable_css_t4ik8z7u4pmaih8ew4cc, .table th.tinytable_css_t4ik8z7u4pmaih8ew4cc {    text-align: right; }
+.table td.tinytable_css_ysnboigbo9ry5ev2lnel, .table th.tinytable_css_ysnboigbo9ry5ev2lnel {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1217,7 +1217,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_n8hhw7x4tjx6kwhui79c" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_51jj21etu2wk1ftv6e5u" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mpg</th>
@@ -1392,12 +1392,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_r53zumdokrlpjvdv2uuc(i, j, css_id) {
-        var table = document.getElementById("tinytable_n8hhw7x4tjx6kwhui79c");
+      function styleCell_tinytable_t2rzze4kj6hc28gs4o33(i, j, css_id) {
+        var table = document.getElementById("tinytable_51jj21etu2wk1ftv6e5u");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_n8hhw7x4tjx6kwhui79c');
+        var table = document.getElementById('tinytable_51jj21etu2wk1ftv6e5u');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1405,8 +1405,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_r53zumdokrlpjvdv2uuc(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_n8hhw7x4tjx6kwhui79c");
+      function spanCell_tinytable_t2rzze4kj6hc28gs4o33(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_51jj21etu2wk1ftv6e5u");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1433,162 +1433,162 @@ <h1 class="title">Data Summaries</h1>
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         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2325,8 +2325,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_sdpkklhbgh01ufpjvtul(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_68n8j0gpy8tothm6fxml");
+      function spanCell_tinytable_hlb2w584xdmu3avnmze6(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_gc3cbalwvvv9du3ukmqn");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -2353,125 +2353,125 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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+window.addEventListener('load', function () { styleCell_tinytable_abl69kczntn5obq6n9l4(4, 2, 'tinytable_css_58vcm9t59y3qlpq6ra46') })
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+window.addEventListener('load', function () { styleCell_tinytable_abl69kczntn5obq6n9l4(1, 3, 'tinytable_css_bg6onebpjjc9evx6f69m') })
+window.addEventListener('load', function () { styleCell_tinytable_abl69kczntn5obq6n9l4(2, 3, 'tinytable_css_bg6onebpjjc9evx6f69m') })
+window.addEventListener('load', function () { styleCell_tinytable_abl69kczntn5obq6n9l4(3, 3, 'tinytable_css_bg6onebpjjc9evx6f69m') })
+window.addEventListener('load', function () { styleCell_tinytable_abl69kczntn5obq6n9l4(4, 3, 'tinytable_css_bg6onebpjjc9evx6f69m') })
     </script>
 </div>
 </div>
@@ -2622,11 +2622,11 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_8pel0ex1eujh700s2fjj</title>
+<title>tinytable_0oc8o58rle4vir4jlagy</title>
 <style>
-.table td.tinytable_css_njx0hdkchilalqjxdkij, .table th.tinytable_css_njx0hdkchilalqjxdkij {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_u55nwmv8koqks86u0htl, .table th.tinytable_css_u55nwmv8koqks86u0htl {    text-align: left; }
-.table td.tinytable_css_cp5yut0dkfpbjtpapn6g, .table th.tinytable_css_cp5yut0dkfpbjtpapn6g {    text-align: right; }
+.table td.tinytable_css_3bv1anp4qcfasbkd41s7, .table th.tinytable_css_3bv1anp4qcfasbkd41s7 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_ec7ufoqnyk1csxldcmh5, .table th.tinytable_css_ec7ufoqnyk1csxldcmh5 {    text-align: left; }
+.table td.tinytable_css_r5ir3p53gl31p0ogrwe0, .table th.tinytable_css_r5ir3p53gl31p0ogrwe0 {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -2638,7 +2638,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_8pel0ex1eujh700s2fjj" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_0oc8o58rle4vir4jlagy" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Mean</th>
@@ -2651,12 +2651,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_adh820vlneb1g4tmwpfc(i, j, css_id) {
-        var table = document.getElementById("tinytable_8pel0ex1eujh700s2fjj");
+      function styleCell_tinytable_t5xwn7cq1zgupw79qdct(i, j, css_id) {
+        var table = document.getElementById("tinytable_0oc8o58rle4vir4jlagy");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_8pel0ex1eujh700s2fjj');
+        var table = document.getElementById('tinytable_0oc8o58rle4vir4jlagy');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2664,8 +2664,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_adh820vlneb1g4tmwpfc(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_8pel0ex1eujh700s2fjj");
+      function spanCell_tinytable_t5xwn7cq1zgupw79qdct(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_0oc8o58rle4vir4jlagy");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -2692,12 +2692,12 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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+window.addEventListener('load', function () { styleCell_tinytable_t5xwn7cq1zgupw79qdct(0, 0, 'tinytable_css_3bv1anp4qcfasbkd41s7') })
+window.addEventListener('load', function () { styleCell_tinytable_t5xwn7cq1zgupw79qdct(0, 1, 'tinytable_css_3bv1anp4qcfasbkd41s7') })
+window.addEventListener('load', function () { styleCell_tinytable_t5xwn7cq1zgupw79qdct(0, 0, 'tinytable_css_ec7ufoqnyk1csxldcmh5') })
+window.addEventListener('load', function () { styleCell_tinytable_t5xwn7cq1zgupw79qdct(1, 0, 'tinytable_css_ec7ufoqnyk1csxldcmh5') })
+window.addEventListener('load', function () { styleCell_tinytable_t5xwn7cq1zgupw79qdct(0, 1, 'tinytable_css_r5ir3p53gl31p0ogrwe0') })
+window.addEventListener('load', function () { styleCell_tinytable_t5xwn7cq1zgupw79qdct(1, 1, 'tinytable_css_r5ir3p53gl31p0ogrwe0') })
     </script>
 </div>
 </div>
@@ -2711,11 +2711,11 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_k3vshkaus1evpn9bonv5</title>
+<title>tinytable_8e2hlswrnqomu78pmoc4</title>
 <style>
-.table td.tinytable_css_dzryg8za8c8ca8imtli8, .table th.tinytable_css_dzryg8za8c8ca8imtli8 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_5pp8yz05zaknq93040b1, .table th.tinytable_css_5pp8yz05zaknq93040b1 {    text-align: left; }
-.table td.tinytable_css_z6offg8yghki82jmjgza, .table th.tinytable_css_z6offg8yghki82jmjgza {    text-align: right; }
+.table td.tinytable_css_pwo9z0agsjoabkkn5ljw, .table th.tinytable_css_pwo9z0agsjoabkkn5ljw {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_yohtkjvbrte73ndf2vrc, .table th.tinytable_css_yohtkjvbrte73ndf2vrc {    text-align: left; }
+.table td.tinytable_css_yyg83ky78e00d5vkhia6, .table th.tinytable_css_yyg83ky78e00d5vkhia6 {    text-align: right; }
     </style>
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     MathJax = {
@@ -2727,7 +2727,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_k3vshkaus1evpn9bonv5" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_8e2hlswrnqomu78pmoc4" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">flipper_length_mm</th>
@@ -2740,12 +2740,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_1bbu0d4olnqfxjp8acow(i, j, css_id) {
-        var table = document.getElementById("tinytable_k3vshkaus1evpn9bonv5");
+      function styleCell_tinytable_jy1nlvct4wl51jlmige1(i, j, css_id) {
+        var table = document.getElementById("tinytable_8e2hlswrnqomu78pmoc4");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_k3vshkaus1evpn9bonv5');
+        var table = document.getElementById('tinytable_8e2hlswrnqomu78pmoc4');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2753,8 +2753,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_1bbu0d4olnqfxjp8acow(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_k3vshkaus1evpn9bonv5");
+      function spanCell_tinytable_jy1nlvct4wl51jlmige1(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_8e2hlswrnqomu78pmoc4");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -2781,12 +2781,12 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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-window.addEventListener('load', function () { styleCell_tinytable_1bbu0d4olnqfxjp8acow(0, 0, 'tinytable_css_5pp8yz05zaknq93040b1') })
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-window.addEventListener('load', function () { styleCell_tinytable_1bbu0d4olnqfxjp8acow(1, 1, 'tinytable_css_z6offg8yghki82jmjgza') })
+window.addEventListener('load', function () { styleCell_tinytable_jy1nlvct4wl51jlmige1(0, 0, 'tinytable_css_pwo9z0agsjoabkkn5ljw') })
+window.addEventListener('load', function () { styleCell_tinytable_jy1nlvct4wl51jlmige1(0, 1, 'tinytable_css_pwo9z0agsjoabkkn5ljw') })
+window.addEventListener('load', function () { styleCell_tinytable_jy1nlvct4wl51jlmige1(0, 0, 'tinytable_css_yohtkjvbrte73ndf2vrc') })
+window.addEventListener('load', function () { styleCell_tinytable_jy1nlvct4wl51jlmige1(1, 0, 'tinytable_css_yohtkjvbrte73ndf2vrc') })
+window.addEventListener('load', function () { styleCell_tinytable_jy1nlvct4wl51jlmige1(0, 1, 'tinytable_css_yyg83ky78e00d5vkhia6') })
+window.addEventListener('load', function () { styleCell_tinytable_jy1nlvct4wl51jlmige1(1, 1, 'tinytable_css_yyg83ky78e00d5vkhia6') })
     </script>
 </div>
 </div>
@@ -2801,11 +2801,11 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_rwla8m86hmh3wmociyd9</title>
+<title>tinytable_j5v7hmg8vynw9ut2e8lx</title>
 <style>
-.table td.tinytable_css_fkh8eu53lje6atr24gnp, .table th.tinytable_css_fkh8eu53lje6atr24gnp {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_dqtn4521jfjlkjw7a0ni, .table th.tinytable_css_dqtn4521jfjlkjw7a0ni {    text-align: left; }
-.table td.tinytable_css_63c3tkju8x77z3lr35qg, .table th.tinytable_css_63c3tkju8x77z3lr35qg {    text-align: right; }
+.table td.tinytable_css_8nr0ev89e4dh25ch6369, .table th.tinytable_css_8nr0ev89e4dh25ch6369 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_pxeghrxc4hx68w0jnkl6, .table th.tinytable_css_pxeghrxc4hx68w0jnkl6 {    text-align: left; }
+.table td.tinytable_css_buxlvm2lx13hd7tp9krd, .table th.tinytable_css_buxlvm2lx13hd7tp9krd {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -2817,7 +2817,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_rwla8m86hmh3wmociyd9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_j5v7hmg8vynw9ut2e8lx" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">mean</th>
@@ -2830,12 +2830,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_3s9uzg8ejsn2bsybvtdq(i, j, css_id) {
-        var table = document.getElementById("tinytable_rwla8m86hmh3wmociyd9");
+      function styleCell_tinytable_rlr9vwthw697ra83zepe(i, j, css_id) {
+        var table = document.getElementById("tinytable_j5v7hmg8vynw9ut2e8lx");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_rwla8m86hmh3wmociyd9');
+        var table = document.getElementById('tinytable_j5v7hmg8vynw9ut2e8lx');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2843,8 +2843,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_3s9uzg8ejsn2bsybvtdq(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_rwla8m86hmh3wmociyd9");
+      function spanCell_tinytable_rlr9vwthw697ra83zepe(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_j5v7hmg8vynw9ut2e8lx");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -2871,12 +2871,12 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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-window.addEventListener('load', function () { styleCell_tinytable_3s9uzg8ejsn2bsybvtdq(1, 1, 'tinytable_css_63c3tkju8x77z3lr35qg') })
+window.addEventListener('load', function () { styleCell_tinytable_rlr9vwthw697ra83zepe(0, 0, 'tinytable_css_8nr0ev89e4dh25ch6369') })
+window.addEventListener('load', function () { styleCell_tinytable_rlr9vwthw697ra83zepe(0, 1, 'tinytable_css_8nr0ev89e4dh25ch6369') })
+window.addEventListener('load', function () { styleCell_tinytable_rlr9vwthw697ra83zepe(0, 0, 'tinytable_css_pxeghrxc4hx68w0jnkl6') })
+window.addEventListener('load', function () { styleCell_tinytable_rlr9vwthw697ra83zepe(1, 0, 'tinytable_css_pxeghrxc4hx68w0jnkl6') })
+window.addEventListener('load', function () { styleCell_tinytable_rlr9vwthw697ra83zepe(0, 1, 'tinytable_css_buxlvm2lx13hd7tp9krd') })
+window.addEventListener('load', function () { styleCell_tinytable_rlr9vwthw697ra83zepe(1, 1, 'tinytable_css_buxlvm2lx13hd7tp9krd') })
     </script>
 </div>
 </div>
@@ -2893,11 +2893,11 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_jyvtvaehr9vgol4jhh7s</title>
+<title>tinytable_7mdf14epg6tplt7461i1</title>
 <style>
-.table td.tinytable_css_vl27cs3h44j09wk6fayg, .table th.tinytable_css_vl27cs3h44j09wk6fayg {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_169fzmcaok78kd5uiwss, .table th.tinytable_css_169fzmcaok78kd5uiwss {    text-align: left; }
-.table td.tinytable_css_8rji9zfucxmcmyextmbt, .table th.tinytable_css_8rji9zfucxmcmyextmbt {    text-align: right; }
+.table td.tinytable_css_uif4n6h71z1n51vugxix, .table th.tinytable_css_uif4n6h71z1n51vugxix {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_oewzb2wvh0yx8gpp3kxd, .table th.tinytable_css_oewzb2wvh0yx8gpp3kxd {    text-align: left; }
+.table td.tinytable_css_0vsotyqe8801663mcrzv, .table th.tinytable_css_0vsotyqe8801663mcrzv {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -2909,7 +2909,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_jyvtvaehr9vgol4jhh7s" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_7mdf14epg6tplt7461i1" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Range</th>
@@ -2922,12 +2922,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_hk8r7809mge02nahgg51(i, j, css_id) {
-        var table = document.getElementById("tinytable_jyvtvaehr9vgol4jhh7s");
+      function styleCell_tinytable_9gtx5gse5fpgrgq90v23(i, j, css_id) {
+        var table = document.getElementById("tinytable_7mdf14epg6tplt7461i1");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_jyvtvaehr9vgol4jhh7s');
+        var table = document.getElementById('tinytable_7mdf14epg6tplt7461i1');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2935,8 +2935,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_hk8r7809mge02nahgg51(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_jyvtvaehr9vgol4jhh7s");
+      function spanCell_tinytable_9gtx5gse5fpgrgq90v23(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_7mdf14epg6tplt7461i1");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -2963,12 +2963,12 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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-window.addEventListener('load', function () { styleCell_tinytable_hk8r7809mge02nahgg51(0, 1, 'tinytable_css_8rji9zfucxmcmyextmbt') })
-window.addEventListener('load', function () { styleCell_tinytable_hk8r7809mge02nahgg51(1, 1, 'tinytable_css_8rji9zfucxmcmyextmbt') })
+window.addEventListener('load', function () { styleCell_tinytable_9gtx5gse5fpgrgq90v23(0, 0, 'tinytable_css_uif4n6h71z1n51vugxix') })
+window.addEventListener('load', function () { styleCell_tinytable_9gtx5gse5fpgrgq90v23(0, 1, 'tinytable_css_uif4n6h71z1n51vugxix') })
+window.addEventListener('load', function () { styleCell_tinytable_9gtx5gse5fpgrgq90v23(0, 0, 'tinytable_css_oewzb2wvh0yx8gpp3kxd') })
+window.addEventListener('load', function () { styleCell_tinytable_9gtx5gse5fpgrgq90v23(1, 0, 'tinytable_css_oewzb2wvh0yx8gpp3kxd') })
+window.addEventListener('load', function () { styleCell_tinytable_9gtx5gse5fpgrgq90v23(0, 1, 'tinytable_css_0vsotyqe8801663mcrzv') })
+window.addEventListener('load', function () { styleCell_tinytable_9gtx5gse5fpgrgq90v23(1, 1, 'tinytable_css_0vsotyqe8801663mcrzv') })
     </script>
 </div>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">MinMax</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">'['</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">x</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, <span class="st">', '</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">x</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, <span class="st">']'</span><span class="op">)</span></span>
@@ -2981,11 +2981,11 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_u3p4jhx097tdtetdzvmi</title>
+<title>tinytable_078za3ozts6f5b69kczq</title>
 <style>
-.table td.tinytable_css_x48xfns552v88huiqylt, .table th.tinytable_css_x48xfns552v88huiqylt {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_3ldbm2igq359l75gj0ky, .table th.tinytable_css_3ldbm2igq359l75gj0ky {    text-align: left; }
-.table td.tinytable_css_oblpd952454yv9wiswn1, .table th.tinytable_css_oblpd952454yv9wiswn1 {    text-align: right; }
+.table td.tinytable_css_gxruu1lkdfqwbdwv650r, .table th.tinytable_css_gxruu1lkdfqwbdwv650r {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_t2p9zypln5ebt4h5qzwm, .table th.tinytable_css_t2p9zypln5ebt4h5qzwm {    text-align: left; }
+.table td.tinytable_css_gpe3gvsjvfks0mbgfkfw, .table th.tinytable_css_gpe3gvsjvfks0mbgfkfw {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -2997,7 +2997,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_u3p4jhx097tdtetdzvmi" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_078za3ozts6f5b69kczq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">MinMax</th>
@@ -3010,12 +3010,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_s3bg269f119yeczb2dnh(i, j, css_id) {
-        var table = document.getElementById("tinytable_u3p4jhx097tdtetdzvmi");
+      function styleCell_tinytable_hulgmqdke868qep8umqx(i, j, css_id) {
+        var table = document.getElementById("tinytable_078za3ozts6f5b69kczq");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_u3p4jhx097tdtetdzvmi');
+        var table = document.getElementById('tinytable_078za3ozts6f5b69kczq');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3023,8 +3023,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_s3bg269f119yeczb2dnh(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_u3p4jhx097tdtetdzvmi");
+      function spanCell_tinytable_hulgmqdke868qep8umqx(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_078za3ozts6f5b69kczq");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3051,12 +3051,12 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_s3bg269f119yeczb2dnh(0, 0, 'tinytable_css_x48xfns552v88huiqylt') })
-window.addEventListener('load', function () { styleCell_tinytable_s3bg269f119yeczb2dnh(0, 1, 'tinytable_css_x48xfns552v88huiqylt') })
-window.addEventListener('load', function () { styleCell_tinytable_s3bg269f119yeczb2dnh(0, 0, 'tinytable_css_3ldbm2igq359l75gj0ky') })
-window.addEventListener('load', function () { styleCell_tinytable_s3bg269f119yeczb2dnh(1, 0, 'tinytable_css_3ldbm2igq359l75gj0ky') })
-window.addEventListener('load', function () { styleCell_tinytable_s3bg269f119yeczb2dnh(0, 1, 'tinytable_css_oblpd952454yv9wiswn1') })
-window.addEventListener('load', function () { styleCell_tinytable_s3bg269f119yeczb2dnh(1, 1, 'tinytable_css_oblpd952454yv9wiswn1') })
+window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(0, 0, 'tinytable_css_gxruu1lkdfqwbdwv650r') })
+window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(0, 1, 'tinytable_css_gxruu1lkdfqwbdwv650r') })
+window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(0, 0, 'tinytable_css_t2p9zypln5ebt4h5qzwm') })
+window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(1, 0, 'tinytable_css_t2p9zypln5ebt4h5qzwm') })
+window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(0, 1, 'tinytable_css_gpe3gvsjvfks0mbgfkfw') })
+window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(1, 1, 'tinytable_css_gpe3gvsjvfks0mbgfkfw') })
     </script>
 </div>
 </div>
@@ -3072,12 +3072,12 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_a7tgtk5vm1vbmj83atj4</title>
+<title>tinytable_4ndr0ikwtgv4ym7nlxgg</title>
 <style>
-.table td.tinytable_css_gwj51c2m0s9nm5avlvy9, .table th.tinytable_css_gwj51c2m0s9nm5avlvy9 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_feogifetz05cvh7vofm6, .table th.tinytable_css_feogifetz05cvh7vofm6 {    text-align: left; }
-.table td.tinytable_css_ek7zhcczqig1y6wi4l9g, .table th.tinytable_css_ek7zhcczqig1y6wi4l9g {    text-align: right; }
-.table td.tinytable_css_7jqcsh998iq27rvkgwoo, .table th.tinytable_css_7jqcsh998iq27rvkgwoo {    text-align: right; }
+.table td.tinytable_css_mmznkg3vhsmr4d9vl83x, .table th.tinytable_css_mmznkg3vhsmr4d9vl83x {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_0qg3nl4hxy60bs8zzog8, .table th.tinytable_css_0qg3nl4hxy60bs8zzog8 {    text-align: left; }
+.table td.tinytable_css_lj21sd1xrf3zkwmbqanf, .table th.tinytable_css_lj21sd1xrf3zkwmbqanf {    text-align: right; }
+.table td.tinytable_css_4or0y0risatk3rbwefma, .table th.tinytable_css_4or0y0risatk3rbwefma {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -3089,7 +3089,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_a7tgtk5vm1vbmj83atj4" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_4ndr0ikwtgv4ym7nlxgg" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Mean</th>
@@ -3111,12 +3111,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_k6wilf7536nkclmb1x0y(i, j, css_id) {
-        var table = document.getElementById("tinytable_a7tgtk5vm1vbmj83atj4");
+      function styleCell_tinytable_0y7l5ifk2m1qf876e8wa(i, j, css_id) {
+        var table = document.getElementById("tinytable_4ndr0ikwtgv4ym7nlxgg");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_a7tgtk5vm1vbmj83atj4');
+        var table = document.getElementById('tinytable_4ndr0ikwtgv4ym7nlxgg');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3124,8 +3124,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_k6wilf7536nkclmb1x0y(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_a7tgtk5vm1vbmj83atj4");
+      function spanCell_tinytable_0y7l5ifk2m1qf876e8wa(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_4ndr0ikwtgv4ym7nlxgg");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3152,18 +3152,18 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_k6wilf7536nkclmb1x0y(0, 0, 'tinytable_css_gwj51c2m0s9nm5avlvy9') })
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-window.addEventListener('load', function () { styleCell_tinytable_k6wilf7536nkclmb1x0y(2, 0, 'tinytable_css_feogifetz05cvh7vofm6') })
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-window.addEventListener('load', function () { styleCell_tinytable_k6wilf7536nkclmb1x0y(1, 2, 'tinytable_css_7jqcsh998iq27rvkgwoo') })
-window.addEventListener('load', function () { styleCell_tinytable_k6wilf7536nkclmb1x0y(2, 2, 'tinytable_css_7jqcsh998iq27rvkgwoo') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(0, 0, 'tinytable_css_mmznkg3vhsmr4d9vl83x') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(0, 1, 'tinytable_css_mmznkg3vhsmr4d9vl83x') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(0, 2, 'tinytable_css_mmznkg3vhsmr4d9vl83x') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(0, 0, 'tinytable_css_0qg3nl4hxy60bs8zzog8') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(1, 0, 'tinytable_css_0qg3nl4hxy60bs8zzog8') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(2, 0, 'tinytable_css_0qg3nl4hxy60bs8zzog8') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(0, 1, 'tinytable_css_lj21sd1xrf3zkwmbqanf') })
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+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(2, 1, 'tinytable_css_lj21sd1xrf3zkwmbqanf') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(0, 2, 'tinytable_css_4or0y0risatk3rbwefma') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(1, 2, 'tinytable_css_4or0y0risatk3rbwefma') })
+window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(2, 2, 'tinytable_css_4or0y0risatk3rbwefma') })
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@@ -3177,12 +3177,12 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_qhimj363vv9biddepqfy</title>
+<title>tinytable_0lrl0y7y9a6aetp3h30z</title>
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-.table td.tinytable_css_mfqlccklsaj4idxfohcf, .table th.tinytable_css_mfqlccklsaj4idxfohcf {    text-align: right; }
+.table td.tinytable_css_q3rtufixkp8q22sp7ug0, .table th.tinytable_css_q3rtufixkp8q22sp7ug0 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_c2rvo1iuyyajk28gj5sn, .table th.tinytable_css_c2rvo1iuyyajk28gj5sn {    text-align: left; }
+.table td.tinytable_css_ggdpf2b5bygpn0xd9sl6, .table th.tinytable_css_ggdpf2b5bygpn0xd9sl6 {    text-align: right; }
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     MathJax = {
@@ -3194,7 +3194,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_qhimj363vv9biddepqfy" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_0lrl0y7y9a6aetp3h30z" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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                 <th scope="col">Mean</th>
@@ -3236,12 +3236,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
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-        var table = document.getElementById("tinytable_qhimj363vv9biddepqfy");
+      function styleCell_tinytable_frm4y5zsd9o8o1z2v0bk(i, j, css_id) {
+        var table = document.getElementById("tinytable_0lrl0y7y9a6aetp3h30z");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_qhimj363vv9biddepqfy');
+        var table = document.getElementById('tinytable_0lrl0y7y9a6aetp3h30z');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3249,8 +3249,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_fisvpdm5ga7l2e0c6qmr(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_qhimj363vv9biddepqfy");
+      function spanCell_tinytable_frm4y5zsd9o8o1z2v0bk(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_0lrl0y7y9a6aetp3h30z");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3277,30 +3277,30 @@ <h1 class="title">Data Summaries</h1>
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+window.addEventListener('load', function () { styleCell_tinytable_frm4y5zsd9o8o1z2v0bk(1, 2, 'tinytable_css_abxpxcck87m3pahyrvrm') })
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+window.addEventListener('load', function () { styleCell_tinytable_frm4y5zsd9o8o1z2v0bk(3, 2, 'tinytable_css_abxpxcck87m3pahyrvrm') })
+window.addEventListener('load', function () { styleCell_tinytable_frm4y5zsd9o8o1z2v0bk(4, 2, 'tinytable_css_abxpxcck87m3pahyrvrm') })
+window.addEventListener('load', function () { styleCell_tinytable_frm4y5zsd9o8o1z2v0bk(5, 2, 'tinytable_css_abxpxcck87m3pahyrvrm') })
+window.addEventListener('load', function () { styleCell_tinytable_frm4y5zsd9o8o1z2v0bk(6, 2, 'tinytable_css_abxpxcck87m3pahyrvrm') })
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@@ -3317,12 +3317,12 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_1r9ymtuhq8t5wqhjkihm</title>
+<title>tinytable_gmpomhntrat7k9d0i4e3</title>
 <style>
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-.table td.tinytable_css_xtasagoclhdwq89pfe3e, .table th.tinytable_css_xtasagoclhdwq89pfe3e {    text-align: right; }
-.table td.tinytable_css_anrxmnhpt8kiqk5rtr4j, .table th.tinytable_css_anrxmnhpt8kiqk5rtr4j {    text-align: right; }
+.table td.tinytable_css_ev10canmdvj7vel0d7zo, .table th.tinytable_css_ev10canmdvj7vel0d7zo {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_ctjniku04ape7emtymzo, .table th.tinytable_css_ctjniku04ape7emtymzo {    text-align: left; }
+.table td.tinytable_css_kj2irloeuzfm9kujg31t, .table th.tinytable_css_kj2irloeuzfm9kujg31t {    text-align: right; }
+.table td.tinytable_css_smq6a9wrcifnttwx3eqv, .table th.tinytable_css_smq6a9wrcifnttwx3eqv {    text-align: right; }
     </style>
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     MathJax = {
@@ -3334,7 +3334,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_1r9ymtuhq8t5wqhjkihm" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_gmpomhntrat7k9d0i4e3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">female</th>
@@ -3356,12 +3356,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_nhbj3q03m1t4g8m847s9(i, j, css_id) {
-        var table = document.getElementById("tinytable_1r9ymtuhq8t5wqhjkihm");
+      function styleCell_tinytable_f4g2og9yt13q2je89g3y(i, j, css_id) {
+        var table = document.getElementById("tinytable_gmpomhntrat7k9d0i4e3");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_1r9ymtuhq8t5wqhjkihm');
+        var table = document.getElementById('tinytable_gmpomhntrat7k9d0i4e3');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3369,8 +3369,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_nhbj3q03m1t4g8m847s9(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_1r9ymtuhq8t5wqhjkihm");
+      function spanCell_tinytable_f4g2og9yt13q2je89g3y(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_gmpomhntrat7k9d0i4e3");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3397,18 +3397,18 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
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+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(0, 0, 'tinytable_css_ev10canmdvj7vel0d7zo') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(0, 1, 'tinytable_css_ev10canmdvj7vel0d7zo') })
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+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(0, 0, 'tinytable_css_ctjniku04ape7emtymzo') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(1, 0, 'tinytable_css_ctjniku04ape7emtymzo') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(2, 0, 'tinytable_css_ctjniku04ape7emtymzo') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(0, 1, 'tinytable_css_kj2irloeuzfm9kujg31t') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(1, 1, 'tinytable_css_kj2irloeuzfm9kujg31t') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(2, 1, 'tinytable_css_kj2irloeuzfm9kujg31t') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(0, 2, 'tinytable_css_smq6a9wrcifnttwx3eqv') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(1, 2, 'tinytable_css_smq6a9wrcifnttwx3eqv') })
+window.addEventListener('load', function () { styleCell_tinytable_f4g2og9yt13q2je89g3y(2, 2, 'tinytable_css_smq6a9wrcifnttwx3eqv') })
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@@ -3422,17 +3422,17 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
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+<title>tinytable_61gfvsq6e63e6kvnf1gv</title>
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@@ -3444,7 +3444,7 @@ <h1 class="title">Data Summaries</h1>
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-      <table class="table table-borderless" id="tinytable_obmklq0itbezch9njcgh" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_61gfvsq6e63e6kvnf1gv" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
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@@ -3470,12 +3470,12 @@ <h1 class="title">Data Summaries</h1>
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+      function styleCell_tinytable_ajrwd10hhiysowhud2ga(i, j, css_id) {
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 </div>
 </div>
@@ -3556,13 +3556,13 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_sor9t2xlfjxtsj45gmat</title>
+<title>tinytable_5g2gknzl8ajp7jr2aw6y</title>
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-.table td.tinytable_css_2kgi7an6pil03lfc5uos, .table th.tinytable_css_2kgi7an6pil03lfc5uos {    text-align: right; }
+.table td.tinytable_css_2xk5oac404rm7pmi4xx5, .table th.tinytable_css_2xk5oac404rm7pmi4xx5 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_4u4sy1l7h00063ef8dl0, .table th.tinytable_css_4u4sy1l7h00063ef8dl0 {    text-align: left; }
+.table td.tinytable_css_c5m433wykg0wnssky483, .table th.tinytable_css_c5m433wykg0wnssky483 {    text-align: left; }
+.table td.tinytable_css_ilcm3lnibi5uayemyi0c, .table th.tinytable_css_ilcm3lnibi5uayemyi0c {    text-align: right; }
+.table td.tinytable_css_9z2j0qquq30d5xqd4veu, .table th.tinytable_css_9z2j0qquq30d5xqd4veu {    text-align: right; }
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@@ -3574,7 +3574,7 @@ <h1 class="title">Data Summaries</h1>
       }
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-      <table class="table table-borderless" id="tinytable_sor9t2xlfjxtsj45gmat" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_5g2gknzl8ajp7jr2aw6y" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">sex</th>
                 <th scope="col"> </th>
@@ -3611,12 +3611,12 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_sor9t2xlfjxtsj45gmat");
+      function styleCell_tinytable_lmuv3ecibr0lbnvao3wt(i, j, css_id) {
+        var table = document.getElementById("tinytable_5g2gknzl8ajp7jr2aw6y");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_sor9t2xlfjxtsj45gmat');
+        var table = document.getElementById('tinytable_5g2gknzl8ajp7jr2aw6y');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3624,8 +3624,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_jia8pkfpm5f5xfyr3shq(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_sor9t2xlfjxtsj45gmat");
+      function spanCell_tinytable_lmuv3ecibr0lbnvao3wt(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_5g2gknzl8ajp7jr2aw6y");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3652,30 +3652,30 @@ <h1 class="title">Data Summaries</h1>
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@@ -4097,12 +4097,12 @@ <h1 class="title">Data Summaries</h1>
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+.table td.tinytable_css_mjh6d79csbm7o0pzmgzh, .table th.tinytable_css_mjh6d79csbm7o0pzmgzh {    text-align: right; }
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@@ -4339,7 +4339,7 @@ <h1 class="title">Data Summaries</h1>
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-      <table class="table table-borderless" id="tinytable_4h1bhvxkrwhahymhzla9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_4lg5ajybsnt7mc21r5tt" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
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 <th scope="col" align="center" colspan="1"> </th>
@@ -4381,12 +4381,12 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_4h1bhvxkrwhahymhzla9");
+      function styleCell_tinytable_ewsbqedjqx5jiqvda85t(i, j, css_id) {
+        var table = document.getElementById("tinytable_4lg5ajybsnt7mc21r5tt");
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       function insertSpanRow(i, colspan, content) {
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+        var table = document.getElementById('tinytable_4lg5ajybsnt7mc21r5tt');
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@@ -4487,19 +4487,19 @@ <h1 class="title">Data Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_prmzsvq6vsc6j93abpup" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -4553,12 +4553,12 @@ <h1 class="title">Data Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_shg3lq1fzassz2vtqz4c" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">sex</th>
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@@ -4720,12 +4720,12 @@ <h1 class="title">Data Summaries</h1>
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+      function styleCell_tinytable_d6q3fvpcyw9t7c1alsv2(i, j, css_id) {
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 </div>
 <div class="sourceCode" id="cb30"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://modelsummary.com/man/datasummary.html">datasummary</a></span><span class="op">(</span><span class="va">sex</span> <span class="op">*</span> <span class="op">(</span><span class="va">flipper_length_mm</span> <span class="op">+</span> <span class="va">bill_length_mm</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://dmurdoch.github.io/tables/reference/Heading.html">Heading</a></span><span class="op">(</span><span class="st">"Body mass (g)"</span>, nearData<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">*</span> <span class="va">body_mass_g</span> <span class="op">~</span> <span class="va">Mean</span> <span class="op">+</span> <span class="va">SD</span>,</span>
@@ -4799,13 +4799,13 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_7g4uyfmhkfin69idswko</title>
+<title>tinytable_1jwng5p5tqqkn755em2n</title>
 <style>
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+.table td.tinytable_css_7t2txy07hcl7sxitwfkn, .table th.tinytable_css_7t2txy07hcl7sxitwfkn {    text-align: left; }
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+.table td.tinytable_css_uqr59y206oo509w2eyp6, .table th.tinytable_css_uqr59y206oo509w2eyp6 {    text-align: right; }
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     MathJax = {
@@ -4817,7 +4817,7 @@ <h1 class="title">Data Summaries</h1>
       }
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_7g4uyfmhkfin69idswko" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_1jwng5p5tqqkn755em2n" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">sex</th>
                 <th scope="col"> </th>
@@ -4860,12 +4860,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-      function styleCell_tinytable_x6jbdxubi1henwkrzm63(i, j, css_id) {
-        var table = document.getElementById("tinytable_7g4uyfmhkfin69idswko");
+      function styleCell_tinytable_vwg4vnao7nsnivcnw0el(i, j, css_id) {
+        var table = document.getElementById("tinytable_1jwng5p5tqqkn755em2n");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_7g4uyfmhkfin69idswko');
+        var table = document.getElementById('tinytable_1jwng5p5tqqkn755em2n');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -4873,8 +4873,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
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-      function spanCell_tinytable_x6jbdxubi1henwkrzm63(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_7g4uyfmhkfin69idswko");
+      function spanCell_tinytable_vwg4vnao7nsnivcnw0el(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_1jwng5p5tqqkn755em2n");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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@@ -4901,34 +4901,34 @@ <h1 class="title">Data Summaries</h1>
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+.table td.tinytable_css_s6u1eki1u8ztl020m1nd, .table th.tinytable_css_s6u1eki1u8ztl020m1nd {    text-align: left; }
+.table td.tinytable_css_6t9x5lj27jogejw7qi3u, .table th.tinytable_css_6t9x5lj27jogejw7qi3u {    text-align: left; }
+.table td.tinytable_css_nkai44r7irs7p6uc00y2, .table th.tinytable_css_nkai44r7irs7p6uc00y2 {    text-align: right; }
+.table td.tinytable_css_kzhwt5nu46wkizwicxey, .table th.tinytable_css_kzhwt5nu46wkizwicxey {    text-align: right; }
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     MathJax = {
@@ -5131,7 +5131,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_205a3bxbkahnluvbhizw" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_0mder98ikpie4acsvb3e" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">species</th>
                 <th scope="col">sex</th>
@@ -5186,12 +5186,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-      function styleCell_tinytable_pkins849cttfwhz7on71(i, j, css_id) {
-        var table = document.getElementById("tinytable_205a3bxbkahnluvbhizw");
+      function styleCell_tinytable_kp5kvztenb0hiwkxz892(i, j, css_id) {
+        var table = document.getElementById("tinytable_0mder98ikpie4acsvb3e");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_205a3bxbkahnluvbhizw');
+        var table = document.getElementById('tinytable_0mder98ikpie4acsvb3e');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -5199,8 +5199,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_pkins849cttfwhz7on71(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_205a3bxbkahnluvbhizw");
+      function spanCell_tinytable_kp5kvztenb0hiwkxz892(i, j, rowspan, colspan) {
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         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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@@ -5227,42 +5227,42 @@ <h1 class="title">Data Summaries</h1>
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+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(0, 3, 'tinytable_css_jxwp48749futvcj8eaxz') })
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+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(5, 2, 'tinytable_css_nkai44r7irs7p6uc00y2') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(6, 2, 'tinytable_css_nkai44r7irs7p6uc00y2') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(7, 2, 'tinytable_css_nkai44r7irs7p6uc00y2') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(0, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(1, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(2, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(3, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(4, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(5, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(6, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
+window.addEventListener('load', function () { styleCell_tinytable_kp5kvztenb0hiwkxz892(7, 3, 'tinytable_css_kzhwt5nu46wkizwicxey') })
     </script>
 </div>
 </div>
@@ -5278,13 +5278,13 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_t8lb898vqn7z0edqtq1e</title>
+<title>tinytable_bk8prpbmxl80j6xwm16l</title>
 <style>
-.table td.tinytable_css_kbyxbfl3rbwy1d7hiqu8, .table th.tinytable_css_kbyxbfl3rbwy1d7hiqu8 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_b766grs6jddfmobchdfr, .table th.tinytable_css_b766grs6jddfmobchdfr {    text-align: left; }
-.table td.tinytable_css_59hxumcfmy6no11ryq9h, .table th.tinytable_css_59hxumcfmy6no11ryq9h {    text-align: left; }
-.table td.tinytable_css_rdc8sqkcupwijcszip8u, .table th.tinytable_css_rdc8sqkcupwijcszip8u {    text-align: right; }
-.table td.tinytable_css_r5r1fsnupaxq6so8pcod, .table th.tinytable_css_r5r1fsnupaxq6so8pcod {    text-align: right; }
+.table td.tinytable_css_1xzflrm8vcaywwegnaai, .table th.tinytable_css_1xzflrm8vcaywwegnaai {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_q8woqnn6mwp9hvqy4k3o, .table th.tinytable_css_q8woqnn6mwp9hvqy4k3o {    text-align: left; }
+.table td.tinytable_css_2mndhznxkp1c3ujtqtf5, .table th.tinytable_css_2mndhznxkp1c3ujtqtf5 {    text-align: left; }
+.table td.tinytable_css_83adxr3eq2aeke8np61c, .table th.tinytable_css_83adxr3eq2aeke8np61c {    text-align: right; }
+.table td.tinytable_css_cx8egm1y0sj7s1luvffr, .table th.tinytable_css_cx8egm1y0sj7s1luvffr {    text-align: right; }
     </style>
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     MathJax = {
@@ -5296,7 +5296,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_t8lb898vqn7z0edqtq1e" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_bk8prpbmxl80j6xwm16l" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">species</th>
                 <th scope="col">sex</th>
@@ -5351,12 +5351,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_o6mi8ojhb7qu5srvu7g7(i, j, css_id) {
-        var table = document.getElementById("tinytable_t8lb898vqn7z0edqtq1e");
+      function styleCell_tinytable_ugpuje4q1v2lsy5ie9f6(i, j, css_id) {
+        var table = document.getElementById("tinytable_bk8prpbmxl80j6xwm16l");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_t8lb898vqn7z0edqtq1e');
+        var table = document.getElementById('tinytable_bk8prpbmxl80j6xwm16l');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -5364,8 +5364,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_o6mi8ojhb7qu5srvu7g7(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_t8lb898vqn7z0edqtq1e");
+      function spanCell_tinytable_ugpuje4q1v2lsy5ie9f6(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_bk8prpbmxl80j6xwm16l");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -5392,42 +5392,42 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
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+      <table class="table table-borderless" id="tinytable_bda7octedv0whbqafr4j" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -5614,12 +5614,12 @@ <h1 class="title">Data Summaries</h1>
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@@ -5708,12 +5708,12 @@ <h1 class="title">Data Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_xb3z1hpx3lbrxr80f6d0" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -5740,12 +5740,12 @@ <h1 class="title">Data Summaries</h1>
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+      function styleCell_tinytable_ha9wsfb2s3ykkotlcxqf(i, j, css_id) {
+        var table = document.getElementById("tinytable_xb3z1hpx3lbrxr80f6d0");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_d4f7ey98nxsmi6a67is0');
+        var table = document.getElementById('tinytable_xb3z1hpx3lbrxr80f6d0');
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-        var table = document.getElementById("tinytable_d4f7ey98nxsmi6a67is0");
+      function spanCell_tinytable_ha9wsfb2s3ykkotlcxqf(i, j, rowspan, colspan) {
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@@ -5804,12 +5804,12 @@ <h1 class="title">Data Summaries</h1>
   
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     MathJax = {
@@ -5821,7 +5821,7 @@ <h1 class="title">Data Summaries</h1>
       }
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     </script><div class="container">
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+      <table class="table table-borderless" id="tinytable_c9pct73bay4ctr12laor" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -5836,12 +5836,12 @@ <h1 class="title">Data Summaries</h1>
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@@ -6037,23 +6037,23 @@ <h1 class="title">Data Summaries</h1>
 <tbody>
 <tr>
 <td>x</td>
-                  <td>-15.63</td>
+                  <td>1.32 </td>
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@@ -6104,22 +6104,22 @@ <h1 class="title">Data Summaries</h1>
 <div class="cell">
 <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/weighted.mean.html">weighted.mean</a></span><span class="op">(</span><span class="va">newdata</span><span class="op">$</span><span class="va">x</span>, <span class="va">newdata</span><span class="op">$</span><span class="va">w</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] -15.62678</code></pre>
+<pre><code>[1] 1.315068</code></pre>
 </div>
 <div class="sourceCode" id="cb42"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/weighted.mean.html">weighted.mean</a></span><span class="op">(</span><span class="va">newdata</span><span class="op">$</span><span class="va">y</span>, <span class="va">newdata</span><span class="op">$</span><span class="va">w</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] -11.1353</code></pre>
+<pre><code>[1] -0.236075</code></pre>
 </div>
 </div>
 <p>But different results from:</p>
 <div class="cell">
 <div class="sourceCode" id="cb44"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">newdata</span><span class="op">$</span><span class="va">x</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] -0.2762772</code></pre>
+<pre><code>[1] 0.3049217</code></pre>
 </div>
 <div class="sourceCode" id="cb46"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">newdata</span><span class="op">$</span><span class="va">y</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 0.1409996</code></pre>
+<pre><code>[1] -0.2411163</code></pre>
 </div>
 </div>
 </section><section id="logical-subsets" class="level2"><h2 class="anchored" data-anchor-id="logical-subsets">Logical subsets</h2>
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+      <table class="table table-borderless" id="tinytable_h82z1swtzcrplc82ub4t" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -6872,7 +6872,7 @@ <h1 class="title">Data Summaries</h1>
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@@ -6894,12 +6894,12 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_xz1uq0nd4uarcyh7o2n2");
+      function styleCell_tinytable_0jwu8tjjmmyg769v5afw(i, j, css_id) {
+        var table = document.getElementById("tinytable_fke4aukdx87ay8s90e40");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_xz1uq0nd4uarcyh7o2n2');
+        var table = document.getElementById('tinytable_fke4aukdx87ay8s90e40');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -6907,8 +6907,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_cm4wiz3fqaa2z5nfxupr(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_xz1uq0nd4uarcyh7o2n2");
+      function spanCell_tinytable_0jwu8tjjmmyg769v5afw(i, j, rowspan, colspan) {
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         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -6935,18 +6935,18 @@ <h1 class="title">Data Summaries</h1>
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+window.addEventListener('load', function () { styleCell_tinytable_0jwu8tjjmmyg769v5afw(0, 1, 'tinytable_css_9qw9m74swrv9em2kjbl7') })
+window.addEventListener('load', function () { styleCell_tinytable_0jwu8tjjmmyg769v5afw(1, 1, 'tinytable_css_9qw9m74swrv9em2kjbl7') })
+window.addEventListener('load', function () { styleCell_tinytable_0jwu8tjjmmyg769v5afw(2, 1, 'tinytable_css_9qw9m74swrv9em2kjbl7') })
+window.addEventListener('load', function () { styleCell_tinytable_0jwu8tjjmmyg769v5afw(0, 2, 'tinytable_css_snjqyuyk7n2fk8rt7qoq') })
+window.addEventListener('load', function () { styleCell_tinytable_0jwu8tjjmmyg769v5afw(1, 2, 'tinytable_css_snjqyuyk7n2fk8rt7qoq') })
+window.addEventListener('load', function () { styleCell_tinytable_0jwu8tjjmmyg769v5afw(2, 2, 'tinytable_css_snjqyuyk7n2fk8rt7qoq') })
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 </div>
 </div>
@@ -6968,19 +6968,19 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_o4d4rgd32ctt4yallwk1</title>
+<title>tinytable_qs98r1uy2tqq6ao212x1</title>
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-.table td.tinytable_css_lnb1ojoe2j0e8q179poa, .table th.tinytable_css_lnb1ojoe2j0e8q179poa {    text-align: center; }
+.table td.tinytable_css_e9z36iirkkge1nyj172k, .table th.tinytable_css_e9z36iirkkge1nyj172k {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_6r1kzxz2wyq0bj0hqm4e, .table th.tinytable_css_6r1kzxz2wyq0bj0hqm4e {    text-align: center; }
+.table td.tinytable_css_gwgyo2c89odp5wwgbj15, .table th.tinytable_css_gwgyo2c89odp5wwgbj15 {    border-bottom: solid 0.05em #d3d8dc; }
+.table td.tinytable_css_emt4m04lyfawz82bybqi, .table th.tinytable_css_emt4m04lyfawz82bybqi {    text-align: left; }
+.table td.tinytable_css_8mxyn5wjjdctqbcex82v, .table th.tinytable_css_8mxyn5wjjdctqbcex82v {    text-align: right; }
+.table td.tinytable_css_qoupx0llkzu4wyfrpejz, .table th.tinytable_css_qoupx0llkzu4wyfrpejz {    text-align: right; }
+.table td.tinytable_css_pfmw1ngejts9gqurlwyd, .table th.tinytable_css_pfmw1ngejts9gqurlwyd {    text-align: right; }
+.table td.tinytable_css_w6hyeigns0ap99p57kfy, .table th.tinytable_css_w6hyeigns0ap99p57kfy {    text-align: right; }
+.table td.tinytable_css_6ptpvqitoxxfjqxzlv7x, .table th.tinytable_css_6ptpvqitoxxfjqxzlv7x {    text-align: right; }
+.table td.tinytable_css_w0sazim53gierxqglyby, .table th.tinytable_css_w0sazim53gierxqglyby {    text-align: right; }
+.table td.tinytable_css_a1qz868xhfxbr4nk73to, .table th.tinytable_css_a1qz868xhfxbr4nk73to {    text-align: center; }
     </style>
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     MathJax = {
@@ -6992,7 +6992,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_o4d4rgd32ctt4yallwk1" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_qs98r1uy2tqq6ao212x1" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -7043,12 +7043,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_knazfpx1n23r168nodud(i, j, css_id) {
-        var table = document.getElementById("tinytable_o4d4rgd32ctt4yallwk1");
+      function styleCell_tinytable_gamap21ubwp1o9yxwu67(i, j, css_id) {
+        var table = document.getElementById("tinytable_qs98r1uy2tqq6ao212x1");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_o4d4rgd32ctt4yallwk1');
+        var table = document.getElementById('tinytable_qs98r1uy2tqq6ao212x1');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -7056,8 +7056,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_knazfpx1n23r168nodud(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_o4d4rgd32ctt4yallwk1");
+      function spanCell_tinytable_gamap21ubwp1o9yxwu67(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_qs98r1uy2tqq6ao212x1");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -7084,65 +7084,65 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
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@@ -7222,19 +7222,19 @@ <h1 class="title">Data Summaries</h1>
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@@ -7469,12 +7469,12 @@ <h1 class="title">Data Summaries</h1>
   
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 <th scope="col"> </th>
                 <th scope="col">Mean</th>
@@ -7501,12 +7501,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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+window.addEventListener('load', function () { styleCell_tinytable_sm6gyupz9wy4fng64cg8(1, 0, 'tinytable_css_qv7mfah7gld48298yu4i') })
+window.addEventListener('load', function () { styleCell_tinytable_sm6gyupz9wy4fng64cg8(0, 1, 'tinytable_css_ecdowqxe6grhxpnoek40') })
+window.addEventListener('load', function () { styleCell_tinytable_sm6gyupz9wy4fng64cg8(1, 1, 'tinytable_css_ecdowqxe6grhxpnoek40') })
+window.addEventListener('load', function () { styleCell_tinytable_sm6gyupz9wy4fng64cg8(0, 2, 'tinytable_css_k8c7jfjj20g6lbe8z8iu') })
+window.addEventListener('load', function () { styleCell_tinytable_sm6gyupz9wy4fng64cg8(1, 2, 'tinytable_css_k8c7jfjj20g6lbe8z8iu') })
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@@ -7565,11 +7565,11 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_lbr3nwxfbla5wkv8r4u1</title>
+<title>tinytable_m7m8ed84j2a9tptbeswu</title>
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+.table td.tinytable_css_hawu4o3rppccaq3o69ot, .table th.tinytable_css_hawu4o3rppccaq3o69ot {    text-align: left; }
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     MathJax = {
@@ -7581,7 +7581,7 @@ <h1 class="title">Data Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_m7m8ed84j2a9tptbeswu" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">X</th>
                 <th scope="col">N</th>
@@ -7589,27 +7589,27 @@ <h1 class="title">Data Summaries</h1>
 <tbody>
 <tr>
 <td>a</td>
-                  <td>332,891</td>
+                  <td>332,919</td>
                 </tr>
 <tr>
 <td>b</td>
-                  <td>334,296</td>
+                  <td>333,564</td>
                 </tr>
 <tr>
 <td>c</td>
-                  <td>332,813</td>
+                  <td>333,517</td>
                 </tr>
 </tbody>
 </table>
 </div>
 
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-        var table = document.getElementById("tinytable_lbr3nwxfbla5wkv8r4u1");
+      function styleCell_tinytable_qs9oqnd5vvhse4r7vhlx(i, j, css_id) {
+        var table = document.getElementById("tinytable_m7m8ed84j2a9tptbeswu");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_lbr3nwxfbla5wkv8r4u1');
+        var table = document.getElementById('tinytable_m7m8ed84j2a9tptbeswu');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -7617,8 +7617,8 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_lbr3nwxfbla5wkv8r4u1");
+      function spanCell_tinytable_qs9oqnd5vvhse4r7vhlx(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_m7m8ed84j2a9tptbeswu");
         const targetRow = table.rows[i];
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@@ -7645,16 +7645,16 @@ <h1 class="title">Data Summaries</h1>
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+window.addEventListener('load', function () { styleCell_tinytable_qs9oqnd5vvhse4r7vhlx(3, 0, 'tinytable_css_hawu4o3rppccaq3o69ot') })
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+window.addEventListener('load', function () { styleCell_tinytable_qs9oqnd5vvhse4r7vhlx(3, 1, 'tinytable_css_qnyx5e3eoo7xm5gyf10m') })
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 </div>
 </div>
@@ -7671,12 +7671,12 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_gasx6b1ph0nk51kgbhj8</title>
+<title>tinytable_993pqqsi04yuatltlpsh</title>
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+.table td.tinytable_css_lbl4ml1wu076moisnai9, .table th.tinytable_css_lbl4ml1wu076moisnai9 {    text-align: left; }
+.table td.tinytable_css_w25iz0stb8hdcu1mte15, .table th.tinytable_css_w25iz0stb8hdcu1mte15 {    text-align: right; }
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     MathJax = {
@@ -7688,7 +7688,7 @@ <h1 class="title">Data Summaries</h1>
       }
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_gasx6b1ph0nk51kgbhj8" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_993pqqsi04yuatltlpsh" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 </thead><caption>Statistics about the famous Palmer Penguins.</caption>
               <tbody><tr>
@@ -7734,12 +7734,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_gasx6b1ph0nk51kgbhj8");
+      function styleCell_tinytable_s8sfpr8m48c6cj28lfm9(i, j, css_id) {
+        var table = document.getElementById("tinytable_993pqqsi04yuatltlpsh");
         table.rows[i].cells[j].classList.add(css_id);
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_gasx6b1ph0nk51kgbhj8');
+        var table = document.getElementById('tinytable_993pqqsi04yuatltlpsh');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -7747,8 +7747,8 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_gasx6b1ph0nk51kgbhj8");
+      function spanCell_tinytable_s8sfpr8m48c6cj28lfm9(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_993pqqsi04yuatltlpsh");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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@@ -7775,30 +7775,30 @@ <h1 class="title">Data Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_2o2w3a9lvp81qs0jlu6b" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -7933,14 +7933,14 @@ <h1 class="title">Data Summaries</h1>
   
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-      <table class="table table-borderless" id="tinytable_zhgt0bijy34a2ewudfrk" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_1n7zmk8yj3ove8ladax8" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -8022,12 +8022,12 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_zhgt0bijy34a2ewudfrk");
+      function styleCell_tinytable_uwuw86fvne1racxq1s9m(i, j, css_id) {
+        var table = document.getElementById("tinytable_1n7zmk8yj3ove8ladax8");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_zhgt0bijy34a2ewudfrk');
+        var table = document.getElementById('tinytable_1n7zmk8yj3ove8ladax8');
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         var newCell = newRow.insertCell(0);
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+      function spanCell_tinytable_uwuw86fvne1racxq1s9m(i, j, rowspan, colspan) {
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 <div class="sourceCode" id="cb66"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://modelsummary.com/man/datasummary_crosstab.html">datasummary_crosstab</a></span><span class="op">(</span><span class="va">cyl_na</span> <span class="op">~</span> <span class="va">vs</span>, data <span class="op">=</span> <span class="va">mycars</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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     </script>
 </div>
 </div>
diff --git a/vignettes/get_started.html b/vignettes/get_started.html
index c59c47d8..a68dce7c 100644
--- a/vignettes/get_started.html
+++ b/vignettes/get_started.html
@@ -414,10 +414,10 @@ <h1 class="title">Getting started</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_38zsoedied9ecmg1qdo8</title>
+<title>tinytable_5s5s0ho4i0d11mopqcm0</title>
 <style>
-.table td.tinytable_css_fhz3tbkn60jcqrocuv64, .table th.tinytable_css_fhz3tbkn60jcqrocuv64 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_s8wazsyh6mxjr7xch654, .table th.tinytable_css_s8wazsyh6mxjr7xch654 {    border-top: solid 0.1em black; }
+.table td.tinytable_css_ddylb9f5iwswcznu19v7, .table th.tinytable_css_ddylb9f5iwswcznu19v7 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_br6e7iid1gvysw8iiaw3, .table th.tinytable_css_br6e7iid1gvysw8iiaw3 {    border-top: solid 0.1em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -429,7 +429,7 @@ <h1 class="title">Getting started</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_38zsoedied9ecmg1qdo8" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_5s5s0ho4i0d11mopqcm0" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Unique</th>
@@ -451,7 +451,7 @@ <h1 class="title">Getting started</h1>
                   <td>1246.0</td>
                   <td>5020.0</td>
                   <td>37015.0</td>
-                  <td><img src="./tinytable_assets/idutiv3gjakzd66m2got4j.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idhb8vbjg7cxgojustlo00.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>Literacy</td>
@@ -462,7 +462,7 @@ <h1 class="title">Getting started</h1>
                   <td>12.0</td>
                   <td>38.0</td>
                   <td>74.0</td>
-                  <td><img src="./tinytable_assets/id1s9kdkvr81olp49ry2vx.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idorwn5dl4dwudwz3kb7dm.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>Commerce</td>
@@ -473,7 +473,7 @@ <h1 class="title">Getting started</h1>
                   <td>1.0</td>
                   <td>42.5</td>
                   <td>86.0</td>
-                  <td><img src="./tinytable_assets/idksfpm9k6stvwlexk662j.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idx8yn8l8vz8kjq5yxmqzz.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>Crime_pers</td>
@@ -484,7 +484,7 @@ <h1 class="title">Getting started</h1>
                   <td>2199.0</td>
                   <td>18748.5</td>
                   <td>37014.0</td>
-                  <td><img src="./tinytable_assets/idsskw4rqnq3j1zccf45n6.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idvfj107zcc181ae9yhm6c.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>Crime_prop</td>
@@ -495,7 +495,7 @@ <h1 class="title">Getting started</h1>
                   <td>1368.0</td>
                   <td>7595.0</td>
                   <td>20235.0</td>
-                  <td><img src="./tinytable_assets/id7gf5g9egiaxtwa2ni79a.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idixxqslczpg9thoz97s4n.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>Clergy</td>
@@ -506,7 +506,7 @@ <h1 class="title">Getting started</h1>
                   <td>1.0</td>
                   <td>43.5</td>
                   <td>86.0</td>
-                  <td><img src="./tinytable_assets/iddyaihsexyzz3u4j496xt.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/id3tsb3ss7el2y7v3o6n6m.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>Small</td>
@@ -546,12 +546,12 @@ <h1 class="title">Getting started</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_r0m2hgsgmmtnzf0ym6gw(i, j, css_id) {
-        var table = document.getElementById("tinytable_38zsoedied9ecmg1qdo8");
+      function styleCell_tinytable_s59lhv5bxhaqj816axkr(i, j, css_id) {
+        var table = document.getElementById("tinytable_5s5s0ho4i0d11mopqcm0");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_38zsoedied9ecmg1qdo8');
+        var table = document.getElementById('tinytable_5s5s0ho4i0d11mopqcm0');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -559,8 +559,8 @@ <h1 class="title">Getting started</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_r0m2hgsgmmtnzf0ym6gw(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_38zsoedied9ecmg1qdo8");
+      function spanCell_tinytable_s59lhv5bxhaqj816axkr(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_5s5s0ho4i0d11mopqcm0");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -587,24 +587,24 @@ <h1 class="title">Getting started</h1>
         targetCell.colSpan = colspan;
       }
 
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-window.addEventListener('load', function () { styleCell_tinytable_r0m2hgsgmmtnzf0ym6gw(7, 0, 'tinytable_css_s8wazsyh6mxjr7xch654') })
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+window.addEventListener('load', function () { styleCell_tinytable_s59lhv5bxhaqj816axkr(7, 3, 'tinytable_css_br6e7iid1gvysw8iiaw3') })
+window.addEventListener('load', function () { styleCell_tinytable_s59lhv5bxhaqj816axkr(7, 4, 'tinytable_css_br6e7iid1gvysw8iiaw3') })
+window.addEventListener('load', function () { styleCell_tinytable_s59lhv5bxhaqj816axkr(7, 5, 'tinytable_css_br6e7iid1gvysw8iiaw3') })
+window.addEventListener('load', function () { styleCell_tinytable_s59lhv5bxhaqj816axkr(7, 6, 'tinytable_css_br6e7iid1gvysw8iiaw3') })
+window.addEventListener('load', function () { styleCell_tinytable_s59lhv5bxhaqj816axkr(7, 7, 'tinytable_css_br6e7iid1gvysw8iiaw3') })
+window.addEventListener('load', function () { styleCell_tinytable_s59lhv5bxhaqj816axkr(7, 8, 'tinytable_css_br6e7iid1gvysw8iiaw3') })
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 </div>
 </div>
@@ -617,19 +617,19 @@ <h1 class="title">Getting started</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_384z899wjzagdbs0nw2y</title>
+<title>tinytable_l9y4jxu9shckkefbll5k</title>
 <style>
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-.table td.tinytable_css_r0mhng0twu1gd3r1ovm4, .table th.tinytable_css_r0mhng0twu1gd3r1ovm4 {    text-align: center; }
+.table td.tinytable_css_wjm6by0do1rl3n7otj1b, .table th.tinytable_css_wjm6by0do1rl3n7otj1b {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_13q3dyms88rk339lxfg0, .table th.tinytable_css_13q3dyms88rk339lxfg0 {    text-align: center; }
+.table td.tinytable_css_df9l4rmpqnx9ys4ufs3u, .table th.tinytable_css_df9l4rmpqnx9ys4ufs3u {    border-bottom: solid 0.05em #d3d8dc; }
+.table td.tinytable_css_g5j6s7bf9a13cyufp3f3, .table th.tinytable_css_g5j6s7bf9a13cyufp3f3 {    text-align: left; }
+.table td.tinytable_css_m2ubfir4n3q5odgkvmkk, .table th.tinytable_css_m2ubfir4n3q5odgkvmkk {    text-align: right; }
+.table td.tinytable_css_9n2xhinloxz23gslycy8, .table th.tinytable_css_9n2xhinloxz23gslycy8 {    text-align: right; }
+.table td.tinytable_css_qevil37jilp5m53osgai, .table th.tinytable_css_qevil37jilp5m53osgai {    text-align: right; }
+.table td.tinytable_css_pr9p5i8qo6cb8dsr0l7g, .table th.tinytable_css_pr9p5i8qo6cb8dsr0l7g {    text-align: right; }
+.table td.tinytable_css_9ssivulgtormqz3xz1b1, .table th.tinytable_css_9ssivulgtormqz3xz1b1 {    text-align: right; }
+.table td.tinytable_css_jlumx0po4nd06sn83dr9, .table th.tinytable_css_jlumx0po4nd06sn83dr9 {    text-align: right; }
+.table td.tinytable_css_fdfsaexcxf0jtyoe5i14, .table th.tinytable_css_fdfsaexcxf0jtyoe5i14 {    text-align: center; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -641,7 +641,7 @@ <h1 class="title">Getting started</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_384z899wjzagdbs0nw2y" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_l9y4jxu9shckkefbll5k" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -720,12 +720,12 @@ <h1 class="title">Getting started</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_6o0lxcsailgz86zqflq8(i, j, css_id) {
-        var table = document.getElementById("tinytable_384z899wjzagdbs0nw2y");
+      function styleCell_tinytable_aytqd2l1z2ncifcp4z1r(i, j, css_id) {
+        var table = document.getElementById("tinytable_l9y4jxu9shckkefbll5k");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_384z899wjzagdbs0nw2y');
+        var table = document.getElementById('tinytable_l9y4jxu9shckkefbll5k');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -733,8 +733,8 @@ <h1 class="title">Getting started</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_6o0lxcsailgz86zqflq8(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_384z899wjzagdbs0nw2y");
+      function spanCell_tinytable_aytqd2l1z2ncifcp4z1r(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_l9y4jxu9shckkefbll5k");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -761,85 +761,85 @@ <h1 class="title">Getting started</h1>
         targetCell.colSpan = colspan;
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+window.addEventListener('load', function () { styleCell_tinytable_aytqd2l1z2ncifcp4z1r(0, 1, 'tinytable_css_fdfsaexcxf0jtyoe5i14') })
+window.addEventListener('load', function () { styleCell_tinytable_aytqd2l1z2ncifcp4z1r(0, 2, 'tinytable_css_fdfsaexcxf0jtyoe5i14') })
+window.addEventListener('load', function () { styleCell_tinytable_aytqd2l1z2ncifcp4z1r(0, 3, 'tinytable_css_fdfsaexcxf0jtyoe5i14') })
+window.addEventListener('load', function () { styleCell_tinytable_aytqd2l1z2ncifcp4z1r(0, 4, 'tinytable_css_fdfsaexcxf0jtyoe5i14') })
+window.addEventListener('load', function () { styleCell_tinytable_aytqd2l1z2ncifcp4z1r(0, 5, 'tinytable_css_fdfsaexcxf0jtyoe5i14') })
+window.addEventListener('load', function () { styleCell_tinytable_aytqd2l1z2ncifcp4z1r(0, 6, 'tinytable_css_fdfsaexcxf0jtyoe5i14') })
     </script>
 </div>
 </div>
@@ -852,16 +852,16 @@ <h1 class="title">Getting started</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_r92eaqgo94qsihqb2nz2</title>
+<title>tinytable_8zpjnjnivhvk23crvyu3</title>
 <style>
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-.table td.tinytable_css_twtrwthywh9cjqdd2a9z, .table th.tinytable_css_twtrwthywh9cjqdd2a9z {    text-align: left; }
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-.table td.tinytable_css_262k2pj6iwvuzrew5hf1, .table th.tinytable_css_262k2pj6iwvuzrew5hf1 {    text-align: right; }
-.table td.tinytable_css_k2yvawi58n1arfn40zkt, .table th.tinytable_css_k2yvawi58n1arfn40zkt {    text-align: right; }
-.table td.tinytable_css_cahzzm9rwx135edxyrfs, .table th.tinytable_css_cahzzm9rwx135edxyrfs {    text-align: right; }
-.table td.tinytable_css_des4oz4mgawefxgflfp7, .table th.tinytable_css_des4oz4mgawefxgflfp7 {    text-align: right; }
+.table td.tinytable_css_tvb7ps8zphvkov6yznpg, .table th.tinytable_css_tvb7ps8zphvkov6yznpg {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_eonzg6jhzfkrnzwar991, .table th.tinytable_css_eonzg6jhzfkrnzwar991 {    text-align: left; }
+.table td.tinytable_css_mk6wuu1u70be3j1pt4we, .table th.tinytable_css_mk6wuu1u70be3j1pt4we {    text-align: right; }
+.table td.tinytable_css_atdcv6tothxn8uck9404, .table th.tinytable_css_atdcv6tothxn8uck9404 {    text-align: right; }
+.table td.tinytable_css_mo9g03ly5yc0357cp41b, .table th.tinytable_css_mo9g03ly5yc0357cp41b {    text-align: right; }
+.table td.tinytable_css_cxm2cvuuvn1d9l01j8j0, .table th.tinytable_css_cxm2cvuuvn1d9l01j8j0 {    text-align: right; }
+.table td.tinytable_css_3ag62hg920b5j5i0wnos, .table th.tinytable_css_3ag62hg920b5j5i0wnos {    text-align: right; }
+.table td.tinytable_css_qzr1zc7exycsjd78o93n, .table th.tinytable_css_qzr1zc7exycsjd78o93n {    text-align: right; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -873,7 +873,7 @@ <h1 class="title">Getting started</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_r92eaqgo94qsihqb2nz2" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_8zpjnjnivhvk23crvyu3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Donations</th>
@@ -943,12 +943,12 @@ <h1 class="title">Getting started</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_36jqpm3d9sy7x8t5sbe7(i, j, css_id) {
-        var table = document.getElementById("tinytable_r92eaqgo94qsihqb2nz2");
+      function styleCell_tinytable_zz73box012pzt4hwcknv(i, j, css_id) {
+        var table = document.getElementById("tinytable_8zpjnjnivhvk23crvyu3");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_r92eaqgo94qsihqb2nz2');
+        var table = document.getElementById('tinytable_8zpjnjnivhvk23crvyu3');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -956,8 +956,8 @@ <h1 class="title">Getting started</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_36jqpm3d9sy7x8t5sbe7(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_r92eaqgo94qsihqb2nz2");
+      function spanCell_tinytable_zz73box012pzt4hwcknv(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_8zpjnjnivhvk23crvyu3");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -984,62 +984,62 @@ <h1 class="title">Getting started</h1>
         targetCell.colSpan = colspan;
       }
 
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+window.addEventListener('load', function () { styleCell_tinytable_zz73box012pzt4hwcknv(2, 6, 'tinytable_css_qzr1zc7exycsjd78o93n') })
+window.addEventListener('load', function () { styleCell_tinytable_zz73box012pzt4hwcknv(3, 6, 'tinytable_css_qzr1zc7exycsjd78o93n') })
+window.addEventListener('load', function () { styleCell_tinytable_zz73box012pzt4hwcknv(4, 6, 'tinytable_css_qzr1zc7exycsjd78o93n') })
+window.addEventListener('load', function () { styleCell_tinytable_zz73box012pzt4hwcknv(5, 6, 'tinytable_css_qzr1zc7exycsjd78o93n') })
+window.addEventListener('load', function () { styleCell_tinytable_zz73box012pzt4hwcknv(6, 6, 'tinytable_css_qzr1zc7exycsjd78o93n') })
     </script>
 </div>
 </div>
@@ -1052,17 +1052,17 @@ <h1 class="title">Getting started</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_79m5i46umqjzdlj0tv90</title>
+<title>tinytable_h3xk51iw9talhwi0q5um</title>
 <style>
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-.table td.tinytable_css_i9q5hfdvi8rovob0epri, .table th.tinytable_css_i9q5hfdvi8rovob0epri {    text-align: center; }
-.table td.tinytable_css_m3v3nrgwqbj2tbjsg7ih, .table th.tinytable_css_m3v3nrgwqbj2tbjsg7ih {    border-bottom: solid 0.05em #d3d8dc; }
-.table td.tinytable_css_7r1mh2y2t7pspk3s0oby, .table th.tinytable_css_7r1mh2y2t7pspk3s0oby {    text-align: left; }
-.table td.tinytable_css_ax6whkvtqia3l1r9bkst, .table th.tinytable_css_ax6whkvtqia3l1r9bkst {    text-align: right; }
-.table td.tinytable_css_cvub8qenssphyqyjy2th, .table th.tinytable_css_cvub8qenssphyqyjy2th {    text-align: right; }
-.table td.tinytable_css_2yj2evrvcyvn5nccc3zb, .table th.tinytable_css_2yj2evrvcyvn5nccc3zb {    text-align: right; }
-.table td.tinytable_css_s2nqsfnjaev5vf6afci6, .table th.tinytable_css_s2nqsfnjaev5vf6afci6 {    text-align: right; }
-.table td.tinytable_css_zdd477n8wrwiwx2lnpbm, .table th.tinytable_css_zdd477n8wrwiwx2lnpbm {    text-align: center; }
+.table td.tinytable_css_o2wxw0vcm0ntnlt9k0ww, .table th.tinytable_css_o2wxw0vcm0ntnlt9k0ww {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_el09xoxy0rwdpwkp3iap, .table th.tinytable_css_el09xoxy0rwdpwkp3iap {    text-align: center; }
+.table td.tinytable_css_67jw2ykjc855dr2euef9, .table th.tinytable_css_67jw2ykjc855dr2euef9 {    border-bottom: solid 0.05em #d3d8dc; }
+.table td.tinytable_css_bg0ws4egm460svwn1hkp, .table th.tinytable_css_bg0ws4egm460svwn1hkp {    text-align: left; }
+.table td.tinytable_css_gfwkbahs9vnjrrrt7sqt, .table th.tinytable_css_gfwkbahs9vnjrrrt7sqt {    text-align: right; }
+.table td.tinytable_css_f84vw98o91b2ab1fysmh, .table th.tinytable_css_f84vw98o91b2ab1fysmh {    text-align: right; }
+.table td.tinytable_css_ervegw9s9tnnkp886va9, .table th.tinytable_css_ervegw9s9tnnkp886va9 {    text-align: right; }
+.table td.tinytable_css_7c99nw1rlshrnu2d1f5p, .table th.tinytable_css_7c99nw1rlshrnu2d1f5p {    text-align: right; }
+.table td.tinytable_css_iocq30pnf2wzaubdfo2i, .table th.tinytable_css_iocq30pnf2wzaubdfo2i {    text-align: center; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1074,7 +1074,7 @@ <h1 class="title">Getting started</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_79m5i46umqjzdlj0tv90" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_h3xk51iw9talhwi0q5um" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -1109,12 +1109,12 @@ <h1 class="title">Getting started</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_sq7aelu1wbe03lnliz4x(i, j, css_id) {
-        var table = document.getElementById("tinytable_79m5i46umqjzdlj0tv90");
+      function styleCell_tinytable_zipr10i9rfp2genf6xpt(i, j, css_id) {
+        var table = document.getElementById("tinytable_h3xk51iw9talhwi0q5um");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_79m5i46umqjzdlj0tv90');
+        var table = document.getElementById('tinytable_h3xk51iw9talhwi0q5um');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1122,8 +1122,8 @@ <h1 class="title">Getting started</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_sq7aelu1wbe03lnliz4x(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_79m5i46umqjzdlj0tv90");
+      function spanCell_tinytable_zipr10i9rfp2genf6xpt(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_h3xk51iw9talhwi0q5um");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1150,43 +1150,43 @@ <h1 class="title">Getting started</h1>
         targetCell.colSpan = colspan;
       }
 
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+window.addEventListener('load', function () { styleCell_tinytable_zipr10i9rfp2genf6xpt(0, 3, 'tinytable_css_ervegw9s9tnnkp886va9') })
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+window.addEventListener('load', function () { styleCell_tinytable_zipr10i9rfp2genf6xpt(3, 4, 'tinytable_css_7c99nw1rlshrnu2d1f5p') })
+window.addEventListener('load', function () { styleCell_tinytable_zipr10i9rfp2genf6xpt(0, 0, 'tinytable_css_iocq30pnf2wzaubdfo2i') })
+window.addEventListener('load', function () { styleCell_tinytable_zipr10i9rfp2genf6xpt(0, 1, 'tinytable_css_iocq30pnf2wzaubdfo2i') })
+window.addEventListener('load', function () { styleCell_tinytable_zipr10i9rfp2genf6xpt(0, 2, 'tinytable_css_iocq30pnf2wzaubdfo2i') })
+window.addEventListener('load', function () { styleCell_tinytable_zipr10i9rfp2genf6xpt(0, 3, 'tinytable_css_iocq30pnf2wzaubdfo2i') })
+window.addEventListener('load', function () { styleCell_tinytable_zipr10i9rfp2genf6xpt(0, 4, 'tinytable_css_iocq30pnf2wzaubdfo2i') })
     </script>
 </div>
 </div>
@@ -1202,12 +1202,12 @@ <h1 class="title">Getting started</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_266fvgi9qes3d7bgbf5m</title>
+<title>tinytable_fa6zc8q2mjc0ixwi1rfq</title>
 <style>
-.table td.tinytable_css_nkg41y4dj7knppv8kct9, .table th.tinytable_css_nkg41y4dj7knppv8kct9 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_oyb0inb75irf4ycmr56r, .table th.tinytable_css_oyb0inb75irf4ycmr56r {    text-align: left; }
-.table td.tinytable_css_n59wy4ftlrs672908ppy, .table th.tinytable_css_n59wy4ftlrs672908ppy {    text-align: center; }
-.table td.tinytable_css_cxv103g9lbsjokt2pcc4, .table th.tinytable_css_cxv103g9lbsjokt2pcc4 {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_p8m7vwxd9u83kwfie0c1, .table th.tinytable_css_p8m7vwxd9u83kwfie0c1 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_m6jcdbksqjmpyx2ar8o9, .table th.tinytable_css_m6jcdbksqjmpyx2ar8o9 {    text-align: left; }
+.table td.tinytable_css_hdw0ijy59ycdyhk1d1o5, .table th.tinytable_css_hdw0ijy59ycdyhk1d1o5 {    text-align: center; }
+.table td.tinytable_css_7b9xp0hisw6kzi1pfwjx, .table th.tinytable_css_7b9xp0hisw6kzi1pfwjx {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1219,7 +1219,7 @@ <h1 class="title">Getting started</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_266fvgi9qes3d7bgbf5m" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_fa6zc8q2mjc0ixwi1rfq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(1)</th>
@@ -1278,12 +1278,12 @@ <h1 class="title">Getting started</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_c1zwnku7aqudfbx9an1i(i, j, css_id) {
-        var table = document.getElementById("tinytable_266fvgi9qes3d7bgbf5m");
+      function styleCell_tinytable_xfk6nvwvkm63632uq4to(i, j, css_id) {
+        var table = document.getElementById("tinytable_fa6zc8q2mjc0ixwi1rfq");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_266fvgi9qes3d7bgbf5m');
+        var table = document.getElementById('tinytable_fa6zc8q2mjc0ixwi1rfq');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1291,8 +1291,8 @@ <h1 class="title">Getting started</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_c1zwnku7aqudfbx9an1i(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_266fvgi9qes3d7bgbf5m");
+      function spanCell_tinytable_xfk6nvwvkm63632uq4to(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_fa6zc8q2mjc0ixwi1rfq");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1319,36 +1319,36 @@ <h1 class="title">Getting started</h1>
         targetCell.colSpan = colspan;
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-window.addEventListener('load', function () { styleCell_tinytable_c1zwnku7aqudfbx9an1i(4, 1, 'tinytable_css_cxv103g9lbsjokt2pcc4') })
+window.addEventListener('load', function () { styleCell_tinytable_xfk6nvwvkm63632uq4to(0, 0, 'tinytable_css_p8m7vwxd9u83kwfie0c1') })
+window.addEventListener('load', function () { styleCell_tinytable_xfk6nvwvkm63632uq4to(0, 1, 'tinytable_css_p8m7vwxd9u83kwfie0c1') })
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+window.addEventListener('load', function () { styleCell_tinytable_xfk6nvwvkm63632uq4to(1, 0, 'tinytable_css_m6jcdbksqjmpyx2ar8o9') })
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+window.addEventListener('load', function () { styleCell_tinytable_xfk6nvwvkm63632uq4to(3, 0, 'tinytable_css_m6jcdbksqjmpyx2ar8o9') })
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@@ -1372,18 +1372,18 @@ <h1 class="title">Getting started</h1>
   
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+<title>tinytable_quyfgid783myjatx00rr</title>
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@@ -1395,7 +1395,7 @@ <h1 class="title">Getting started</h1>
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-      <table class="table table-borderless" id="tinytable_v03brg46tmdcfyzugmzq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_quyfgid783myjatx00rr" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
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@@ -1498,12 +1498,12 @@ <h1 class="title">Getting started</h1>
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-        var table = document.getElementById("tinytable_v03brg46tmdcfyzugmzq");
+      function styleCell_tinytable_k8ky3kcfvfs0fztuzpqi(i, j, css_id) {
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       function insertSpanRow(i, colspan, content) {
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+        var table = document.getElementById('tinytable_quyfgid783myjatx00rr');
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+      function spanCell_tinytable_k8ky3kcfvfs0fztuzpqi(i, j, rowspan, colspan) {
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     </script>
 </div>
 </div>
diff --git a/vignettes/modelplot.html b/vignettes/modelplot.html
index f50033d0..a0bb0848 100644
--- a/vignettes/modelplot.html
+++ b/vignettes/modelplot.html
@@ -489,14 +489,14 @@ <h1 class="title">Model Plots</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_nl1be27a7ezoa76bm7y3</title>
+<title>tinytable_q482m33kcr59753r4d4r</title>
 <style>
-.table td.tinytable_css_zenhfc8n7p41vmtawrcx, .table th.tinytable_css_zenhfc8n7p41vmtawrcx {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_6e7u932c728thklg090x, .table th.tinytable_css_6e7u932c728thklg090x {    text-align: left; }
-.table td.tinytable_css_663ial2665ju512kqib7, .table th.tinytable_css_663ial2665ju512kqib7 {    text-align: center; }
-.table td.tinytable_css_bwnsbgu30qrpu7mfziap, .table th.tinytable_css_bwnsbgu30qrpu7mfziap {    text-align: center; }
-.table td.tinytable_css_ppe5ajrgpblot6nn8zcg, .table th.tinytable_css_ppe5ajrgpblot6nn8zcg {    text-align: center; }
-.table td.tinytable_css_oy02x15hajimg2mpdw3m, .table th.tinytable_css_oy02x15hajimg2mpdw3m {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_utaijeoe7rq6nzpvqvtp, .table th.tinytable_css_utaijeoe7rq6nzpvqvtp {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_jawvq1xbp6oz6po9fj27, .table th.tinytable_css_jawvq1xbp6oz6po9fj27 {    text-align: left; }
+.table td.tinytable_css_mlpyov9uachthxcawco9, .table th.tinytable_css_mlpyov9uachthxcawco9 {    text-align: center; }
+.table td.tinytable_css_pn73fdzsj7xx4q5qro14, .table th.tinytable_css_pn73fdzsj7xx4q5qro14 {    text-align: center; }
+.table td.tinytable_css_0dpvkwcoiicaxrcjiig4, .table th.tinytable_css_0dpvkwcoiicaxrcjiig4 {    text-align: center; }
+.table td.tinytable_css_zvfjk0amdc693oku42oc, .table th.tinytable_css_zvfjk0amdc693oku42oc {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -508,7 +508,7 @@ <h1 class="title">Model Plots</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_nl1be27a7ezoa76bm7y3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_q482m33kcr59753r4d4r" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Small model</th>
@@ -629,12 +629,12 @@ <h1 class="title">Model Plots</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_hjwfj14n2o3c2st6dm2t(i, j, css_id) {
-        var table = document.getElementById("tinytable_nl1be27a7ezoa76bm7y3");
+      function styleCell_tinytable_dxbrybcrudycrqmzkrpw(i, j, css_id) {
+        var table = document.getElementById("tinytable_q482m33kcr59753r4d4r");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_nl1be27a7ezoa76bm7y3');
+        var table = document.getElementById('tinytable_q482m33kcr59753r4d4r');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -642,8 +642,8 @@ <h1 class="title">Model Plots</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_hjwfj14n2o3c2st6dm2t(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_nl1be27a7ezoa76bm7y3");
+      function spanCell_tinytable_dxbrybcrudycrqmzkrpw(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_q482m33kcr59753r4d4r");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -670,90 +670,90 @@ <h1 class="title">Model Plots</h1>
         targetCell.colSpan = colspan;
       }
 
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diff --git a/vignettes/modelsummary.html b/vignettes/modelsummary.html
index ed9dae83..63adbd5d 100644
--- a/vignettes/modelsummary.html
+++ b/vignettes/modelsummary.html
@@ -482,14 +482,14 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_6at2ztg025oh1ikjxsul</title>
+<title>tinytable_99h4l9rdsyut9f7et9pf</title>
 <style>
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-.table td.tinytable_css_6pxz5ymvad5wt7aio25b, .table th.tinytable_css_6pxz5ymvad5wt7aio25b {    text-align: left; }
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-.table td.tinytable_css_wdxt9xwupilwla99tkyq, .table th.tinytable_css_wdxt9xwupilwla99tkyq {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_b36ibd37pxics19k0ng6, .table th.tinytable_css_b36ibd37pxics19k0ng6 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_xqg49oh0fnqrw1ngk9e7, .table th.tinytable_css_xqg49oh0fnqrw1ngk9e7 {    text-align: left; }
+.table td.tinytable_css_6np4xqtwwu2sbk5j0fzk, .table th.tinytable_css_6np4xqtwwu2sbk5j0fzk {    text-align: center; }
+.table td.tinytable_css_k1kkzxcoxw64bad3z0qn, .table th.tinytable_css_k1kkzxcoxw64bad3z0qn {    text-align: center; }
+.table td.tinytable_css_hgdcm179n6ovd0cj8teh, .table th.tinytable_css_hgdcm179n6ovd0cj8teh {    text-align: center; }
+.table td.tinytable_css_4o06rzpjhow75dvbp1k1, .table th.tinytable_css_4o06rzpjhow75dvbp1k1 {    border-bottom: solid 0.05em black; }
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     MathJax = {
@@ -501,7 +501,7 @@ <h1 class="title">Model Summaries</h1>
       }
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_6at2ztg025oh1ikjxsul" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_99h4l9rdsyut9f7et9pf" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">OLS 1</th>
@@ -610,12 +610,12 @@ <h1 class="title">Model Summaries</h1>
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-      function styleCell_tinytable_i5lyupfkxyyikknffjma(i, j, css_id) {
-        var table = document.getElementById("tinytable_6at2ztg025oh1ikjxsul");
+      function styleCell_tinytable_tpwtrzymi1uq7fn6h4k7(i, j, css_id) {
+        var table = document.getElementById("tinytable_99h4l9rdsyut9f7et9pf");
         table.rows[i].cells[j].classList.add(css_id);
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_6at2ztg025oh1ikjxsul');
+        var table = document.getElementById('tinytable_99h4l9rdsyut9f7et9pf');
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@@ -623,8 +623,8 @@ <h1 class="title">Model Summaries</h1>
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@@ -651,82 +651,82 @@ <h1 class="title">Model Summaries</h1>
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@@ -820,11 +820,11 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+<title>tinytable_y4zw86ddjiphaoex6w02</title>
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+      <table class="table table-borderless" id="tinytable_y4zw86ddjiphaoex6w02" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -863,12 +863,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_qq5mvitonyz9cqxyzhg7(i, j, css_id) {
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@@ -942,14 +942,14 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_fbmg0bgkky4jtpu3f3ah" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -1154,12 +1154,12 @@ <h1 class="title">Model Summaries</h1>
   
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+<title>tinytable_dxxyqjn6le9wlx5095rz</title>
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-      <table class="table table-borderless" id="tinytable_do5htssh66pi8vdpb8qu" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_dxxyqjn6le9wlx5095rz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -1222,12 +1222,12 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_do5htssh66pi8vdpb8qu");
+      function styleCell_tinytable_wyzmhtegtf8chvs34nux(i, j, css_id) {
+        var table = document.getElementById("tinytable_dxxyqjn6le9wlx5095rz");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_do5htssh66pi8vdpb8qu');
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-      function spanCell_tinytable_khuewh7mdhouyyhb03ve(i, j, rowspan, colspan) {
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+      function spanCell_tinytable_wyzmhtegtf8chvs34nux(i, j, rowspan, colspan) {
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@@ -1308,14 +1308,14 @@ <h1 class="title">Model Summaries</h1>
   
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+.table td.tinytable_css_s634cqblw0wybvgca2xn, .table th.tinytable_css_s634cqblw0wybvgca2xn {    text-align: left; }
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+.table td.tinytable_css_70y693vhehltm1b8wgc3, .table th.tinytable_css_70y693vhehltm1b8wgc3 {    text-align: center; }
+.table td.tinytable_css_ygxknb5g29bhd10t7n3q, .table th.tinytable_css_ygxknb5g29bhd10t7n3q {    text-align: center; }
+.table td.tinytable_css_zyaphs1g59yyhkiodcwt, .table th.tinytable_css_zyaphs1g59yyhkiodcwt {    border-bottom: solid 0.05em black; }
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@@ -1327,7 +1327,7 @@ <h1 class="title">Model Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_2us8mde8hmx62g7kpd5f" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -1406,12 +1406,12 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_iy6a1fexwg5cza4klk1t");
+      function styleCell_tinytable_1gujjgutv5nsimojh9vr(i, j, css_id) {
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         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_iy6a1fexwg5cza4klk1t');
+        var table = document.getElementById('tinytable_2us8mde8hmx62g7kpd5f');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
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@@ -1419,8 +1419,8 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_iy6a1fexwg5cza4klk1t");
+      function spanCell_tinytable_1gujjgutv5nsimojh9vr(i, j, rowspan, colspan) {
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@@ -1447,62 +1447,62 @@ <h1 class="title">Model Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_3rc1m6zq2wh2e3e4zwax" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -1653,12 +1653,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_bik3eb8af53zca4665lj(i, j, css_id) {
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+        var table = document.getElementById('tinytable_wcbil11z8fusk24kgxkq');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2416,8 +2416,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_hyn7o5r7dyw3aqdwdc17(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_mfh2mzvo98wh1rkxj7qk");
+      function spanCell_tinytable_4vt2oa69w3eaxex3ap6f(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_wcbil11z8fusk24kgxkq");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -2444,30 +2444,30 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(2, 2, 'tinytable_css_cq5o5kgkfa8u6d5crwq0') })
+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(3, 2, 'tinytable_css_cq5o5kgkfa8u6d5crwq0') })
+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(4, 2, 'tinytable_css_cq5o5kgkfa8u6d5crwq0') })
+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(0, 3, 'tinytable_css_uh4s4neph0uk45wa52ws') })
+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(1, 3, 'tinytable_css_uh4s4neph0uk45wa52ws') })
+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(2, 3, 'tinytable_css_uh4s4neph0uk45wa52ws') })
+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(3, 3, 'tinytable_css_uh4s4neph0uk45wa52ws') })
+window.addEventListener('load', function () { styleCell_tinytable_4vt2oa69w3eaxex3ap6f(4, 3, 'tinytable_css_uh4s4neph0uk45wa52ws') })
     </script>
 </div>
 </div>
@@ -2519,13 +2519,13 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_zcj25mneuln2xfpqlb4i</title>
+<title>tinytable_cffkbcjlluhfm08hj8ua</title>
 <style>
-.table td.tinytable_css_wil2k7m23hq6wetwl589, .table th.tinytable_css_wil2k7m23hq6wetwl589 {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_hxcv0tn4w62ebps3ddah, .table th.tinytable_css_hxcv0tn4w62ebps3ddah {    text-align: left; }
-.table td.tinytable_css_cc9fjts0pfkytnjf92rl, .table th.tinytable_css_cc9fjts0pfkytnjf92rl {    text-align: center; }
-.table td.tinytable_css_pe3wk2wkewb0nbjez9il, .table th.tinytable_css_pe3wk2wkewb0nbjez9il {    text-align: center; }
-.table td.tinytable_css_6la6hfetgqgqxnxryfin, .table th.tinytable_css_6la6hfetgqgqxnxryfin {    text-align: center; }
+.table td.tinytable_css_w2krj35q0to2p6lu8i3i, .table th.tinytable_css_w2krj35q0to2p6lu8i3i {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_ot6qkbqf3rxxurvvu86u, .table th.tinytable_css_ot6qkbqf3rxxurvvu86u {    text-align: left; }
+.table td.tinytable_css_egv3pdvanu7oqlvrtync, .table th.tinytable_css_egv3pdvanu7oqlvrtync {    text-align: center; }
+.table td.tinytable_css_83oxusywlpst2y62l529, .table th.tinytable_css_83oxusywlpst2y62l529 {    text-align: center; }
+.table td.tinytable_css_f022n27zdw8nh1qq46ew, .table th.tinytable_css_f022n27zdw8nh1qq46ew {    text-align: center; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -2537,7 +2537,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_zcj25mneuln2xfpqlb4i" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_cffkbcjlluhfm08hj8ua" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">OLS 1</th>
@@ -2598,12 +2598,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_zbwjnie7lhuym8y05cz1(i, j, css_id) {
-        var table = document.getElementById("tinytable_zcj25mneuln2xfpqlb4i");
+      function styleCell_tinytable_7dxl4gy9ebfib6c1xgfo(i, j, css_id) {
+        var table = document.getElementById("tinytable_cffkbcjlluhfm08hj8ua");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_zcj25mneuln2xfpqlb4i');
+        var table = document.getElementById('tinytable_cffkbcjlluhfm08hj8ua');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2611,8 +2611,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_zbwjnie7lhuym8y05cz1(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_zcj25mneuln2xfpqlb4i");
+      function spanCell_tinytable_7dxl4gy9ebfib6c1xgfo(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_cffkbcjlluhfm08hj8ua");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -2639,46 +2639,46 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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@@ -2693,13 +2693,13 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_jrgcs1byppk7fgap0vpy" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -2825,13 +2825,13 @@ <h1 class="title">Model Summaries</h1>
   
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@@ -2843,7 +2843,7 @@ <h1 class="title">Model Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_cpvgfqjkp7tgmet3t0gs" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -2880,12 +2880,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_crt0f5yogxcf0dgxq1re(i, j, css_id) {
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-        var table = document.getElementById('tinytable_hfojfzew5iwz9q51v9qc');
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+      function spanCell_tinytable_crt0f5yogxcf0dgxq1re(i, j, rowspan, colspan) {
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                 <th scope="col">OLS 1</th>
@@ -3132,12 +3132,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
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-      function styleCell_tinytable_5al43e6vhfp1a6kzhw96(i, j, css_id) {
-        var table = document.getElementById("tinytable_d2xpkhzz4p81ququv7il");
+      function styleCell_tinytable_hc2kwqcw2evkd25641tw(i, j, css_id) {
+        var table = document.getElementById("tinytable_tyw740p8uhqyomtirxcz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_d2xpkhzz4p81ququv7il');
+        var table = document.getElementById('tinytable_tyw740p8uhqyomtirxcz');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3145,8 +3145,8 @@ <h1 class="title">Model Summaries</h1>
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         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_5al43e6vhfp1a6kzhw96(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_d2xpkhzz4p81ququv7il");
+      function spanCell_tinytable_hc2kwqcw2evkd25641tw(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_tyw740p8uhqyomtirxcz");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3173,22 +3173,22 @@ <h1 class="title">Model Summaries</h1>
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+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(0, 0, 'tinytable_css_6x4lqug8oki7xva6jvp5') })
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+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(1, 0, 'tinytable_css_vcwjvb70ond9cq0q8be3') })
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+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(2, 1, 'tinytable_css_a5g58323ydfd1ic2r9az') })
+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(0, 2, 'tinytable_css_7naki3621imrciw811tc') })
+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(1, 2, 'tinytable_css_7naki3621imrciw811tc') })
+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(2, 2, 'tinytable_css_7naki3621imrciw811tc') })
+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(0, 3, 'tinytable_css_bxu8ldzp69trdld902mh') })
+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(1, 3, 'tinytable_css_bxu8ldzp69trdld902mh') })
+window.addEventListener('load', function () { styleCell_tinytable_hc2kwqcw2evkd25641tw(2, 3, 'tinytable_css_bxu8ldzp69trdld902mh') })
     </script>
 </div>
 </div>
@@ -3201,13 +3201,13 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_1q9c1vrll79608zvnftp</title>
+<title>tinytable_k87n3hf4xcuoi14wofjs</title>
 <style>
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-.table td.tinytable_css_a8thtlckrx5vqkofz925, .table th.tinytable_css_a8thtlckrx5vqkofz925 {    text-align: left; }
-.table td.tinytable_css_z5na3hpkf59h9wp9u0m9, .table th.tinytable_css_z5na3hpkf59h9wp9u0m9 {    text-align: center; }
-.table td.tinytable_css_62qh840awua4hkurl00f, .table th.tinytable_css_62qh840awua4hkurl00f {    text-align: center; }
-.table td.tinytable_css_thrz3i3y33ov719hi0hq, .table th.tinytable_css_thrz3i3y33ov719hi0hq {    text-align: center; }
+.table td.tinytable_css_yfnpsq2r42otpg4ve0nb, .table th.tinytable_css_yfnpsq2r42otpg4ve0nb {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_xxxu232l9orawdnwh0ny, .table th.tinytable_css_xxxu232l9orawdnwh0ny {    text-align: left; }
+.table td.tinytable_css_6x11cy5pwqjjzebg3ckx, .table th.tinytable_css_6x11cy5pwqjjzebg3ckx {    text-align: center; }
+.table td.tinytable_css_sr711cow34u2ks9vvp48, .table th.tinytable_css_sr711cow34u2ks9vvp48 {    text-align: center; }
+.table td.tinytable_css_2s1ou2yoj5u3xiagckow, .table th.tinytable_css_2s1ou2yoj5u3xiagckow {    text-align: center; }
     </style>
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     MathJax = {
@@ -3219,7 +3219,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_1q9c1vrll79608zvnftp" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_k87n3hf4xcuoi14wofjs" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">OLS 1</th>
@@ -3256,12 +3256,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_nlsfshhsucudhfyf67sm(i, j, css_id) {
-        var table = document.getElementById("tinytable_1q9c1vrll79608zvnftp");
+      function styleCell_tinytable_gv68iyyolbi01g8i5b5x(i, j, css_id) {
+        var table = document.getElementById("tinytable_k87n3hf4xcuoi14wofjs");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_1q9c1vrll79608zvnftp');
+        var table = document.getElementById('tinytable_k87n3hf4xcuoi14wofjs');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3269,8 +3269,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_nlsfshhsucudhfyf67sm(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_1q9c1vrll79608zvnftp");
+      function spanCell_tinytable_gv68iyyolbi01g8i5b5x(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_k87n3hf4xcuoi14wofjs");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3297,30 +3297,30 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
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+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(4, 1, 'tinytable_css_6x11cy5pwqjjzebg3ckx') })
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+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(3, 2, 'tinytable_css_sr711cow34u2ks9vvp48') })
+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(4, 2, 'tinytable_css_sr711cow34u2ks9vvp48') })
+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(0, 3, 'tinytable_css_2s1ou2yoj5u3xiagckow') })
+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(1, 3, 'tinytable_css_2s1ou2yoj5u3xiagckow') })
+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(2, 3, 'tinytable_css_2s1ou2yoj5u3xiagckow') })
+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(3, 3, 'tinytable_css_2s1ou2yoj5u3xiagckow') })
+window.addEventListener('load', function () { styleCell_tinytable_gv68iyyolbi01g8i5b5x(4, 3, 'tinytable_css_2s1ou2yoj5u3xiagckow') })
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@@ -3333,13 +3333,13 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_adzi5h8lssjzmsbhp30a</title>
+<title>tinytable_ohscx6fjfyu6kxwz9r55</title>
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+.table td.tinytable_css_1ej8egr3l05n4g62efvu, .table th.tinytable_css_1ej8egr3l05n4g62efvu {    text-align: center; }
+.table td.tinytable_css_l7k8unqopy6qpiy3aiiv, .table th.tinytable_css_l7k8unqopy6qpiy3aiiv {    text-align: center; }
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     MathJax = {
@@ -3351,7 +3351,7 @@ <h1 class="title">Model Summaries</h1>
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_adzi5h8lssjzmsbhp30a" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ohscx6fjfyu6kxwz9r55" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">OLS 1</th>
@@ -3400,12 +3400,12 @@ <h1 class="title">Model Summaries</h1>
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-      function styleCell_tinytable_sxc9c7ecwf5pxwa38dti(i, j, css_id) {
-        var table = document.getElementById("tinytable_adzi5h8lssjzmsbhp30a");
+      function styleCell_tinytable_7rgyzpwika0g4qqzrg6r(i, j, css_id) {
+        var table = document.getElementById("tinytable_ohscx6fjfyu6kxwz9r55");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_adzi5h8lssjzmsbhp30a');
+        var table = document.getElementById('tinytable_ohscx6fjfyu6kxwz9r55');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3413,8 +3413,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_sxc9c7ecwf5pxwa38dti(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_adzi5h8lssjzmsbhp30a");
+      function spanCell_tinytable_7rgyzpwika0g4qqzrg6r(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ohscx6fjfyu6kxwz9r55");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3441,38 +3441,38 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(0, 1, 'tinytable_css_3nifkpgi8tzkeg7teaaf') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(0, 2, 'tinytable_css_3nifkpgi8tzkeg7teaaf') })
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+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(6, 0, 'tinytable_css_wi9trrks06r5ov2y9gqu') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(0, 1, 'tinytable_css_1ej8egr3l05n4g62efvu') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(1, 1, 'tinytable_css_1ej8egr3l05n4g62efvu') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(2, 1, 'tinytable_css_1ej8egr3l05n4g62efvu') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(3, 1, 'tinytable_css_1ej8egr3l05n4g62efvu') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(4, 1, 'tinytable_css_1ej8egr3l05n4g62efvu') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(5, 1, 'tinytable_css_1ej8egr3l05n4g62efvu') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(6, 1, 'tinytable_css_1ej8egr3l05n4g62efvu') })
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+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(1, 2, 'tinytable_css_l7k8unqopy6qpiy3aiiv') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(2, 2, 'tinytable_css_l7k8unqopy6qpiy3aiiv') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(3, 2, 'tinytable_css_l7k8unqopy6qpiy3aiiv') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(4, 2, 'tinytable_css_l7k8unqopy6qpiy3aiiv') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(5, 2, 'tinytable_css_l7k8unqopy6qpiy3aiiv') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(6, 2, 'tinytable_css_l7k8unqopy6qpiy3aiiv') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(0, 3, 'tinytable_css_ivd32klzu8u3y4g9w35j') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(1, 3, 'tinytable_css_ivd32klzu8u3y4g9w35j') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(2, 3, 'tinytable_css_ivd32klzu8u3y4g9w35j') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(3, 3, 'tinytable_css_ivd32klzu8u3y4g9w35j') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(4, 3, 'tinytable_css_ivd32klzu8u3y4g9w35j') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(5, 3, 'tinytable_css_ivd32klzu8u3y4g9w35j') })
+window.addEventListener('load', function () { styleCell_tinytable_7rgyzpwika0g4qqzrg6r(6, 3, 'tinytable_css_ivd32klzu8u3y4g9w35j') })
     </script>
 </div>
 </div>
@@ -3490,13 +3490,13 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_web0qpmhdfdk99hivh87</title>
+<title>tinytable_k698if5lz3ipydsdyooo</title>
 <style>
-.table td.tinytable_css_dcgqqwft7fx7h8op8uvz, .table th.tinytable_css_dcgqqwft7fx7h8op8uvz {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_f4aw74cw14sjeh6iv1ut, .table th.tinytable_css_f4aw74cw14sjeh6iv1ut {    text-align: left; }
-.table td.tinytable_css_jrx2jtikwn73lbww4syh, .table th.tinytable_css_jrx2jtikwn73lbww4syh {    text-align: center; }
-.table td.tinytable_css_y88i5e2ywbzkhm4pq25t, .table th.tinytable_css_y88i5e2ywbzkhm4pq25t {    text-align: center; }
-.table td.tinytable_css_4ick0mc36az4stkcwfpn, .table th.tinytable_css_4ick0mc36az4stkcwfpn {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_e95udvrlfe5wny84t41u, .table th.tinytable_css_e95udvrlfe5wny84t41u {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_i5rsom987gsu2msmjwky, .table th.tinytable_css_i5rsom987gsu2msmjwky {    text-align: left; }
+.table td.tinytable_css_m1afw65rbxnujqs9s02g, .table th.tinytable_css_m1afw65rbxnujqs9s02g {    text-align: center; }
+.table td.tinytable_css_ambs4xffghtdsjj9bntl, .table th.tinytable_css_ambs4xffghtdsjj9bntl {    text-align: center; }
+.table td.tinytable_css_g1a6vrr6se09nzb8hq32, .table th.tinytable_css_g1a6vrr6se09nzb8hq32 {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -3508,7 +3508,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_web0qpmhdfdk99hivh87" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_k698if5lz3ipydsdyooo" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(1)</th>
@@ -3580,12 +3580,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_kf4yjd4592mqiuh17bex(i, j, css_id) {
-        var table = document.getElementById("tinytable_web0qpmhdfdk99hivh87");
+      function styleCell_tinytable_eln2ukuv4aq7q3oitbeg(i, j, css_id) {
+        var table = document.getElementById("tinytable_k698if5lz3ipydsdyooo");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_web0qpmhdfdk99hivh87');
+        var table = document.getElementById('tinytable_k698if5lz3ipydsdyooo');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3593,8 +3593,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_kf4yjd4592mqiuh17bex(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_web0qpmhdfdk99hivh87");
+      function spanCell_tinytable_eln2ukuv4aq7q3oitbeg(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_k698if5lz3ipydsdyooo");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -3621,51 +3621,51 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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@@ -4796,14 +4796,14 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_l1bk8vi8xpz2bl9irwmz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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+      <table class="table table-borderless" id="tinytable_0fvgw29rsgoi2rzet0c8" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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+.table td.tinytable_css_0mg3kfjh7zn9vr6lh3if, .table th.tinytable_css_0mg3kfjh7zn9vr6lh3if {    text-align: center; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -5568,7 +5568,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_c6tuxvc6ncnmmettxgiu" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_6wq9xs0pmvbqoj2c9917" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -5618,12 +5618,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_6y4fkr9bn39o5u4etccc(i, j, css_id) {
-        var table = document.getElementById("tinytable_c6tuxvc6ncnmmettxgiu");
+      function styleCell_tinytable_1th7y2e9zjgc4t7xijyt(i, j, css_id) {
+        var table = document.getElementById("tinytable_6wq9xs0pmvbqoj2c9917");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_c6tuxvc6ncnmmettxgiu');
+        var table = document.getElementById('tinytable_6wq9xs0pmvbqoj2c9917');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -5631,8 +5631,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_6y4fkr9bn39o5u4etccc(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_c6tuxvc6ncnmmettxgiu");
+      function spanCell_tinytable_1th7y2e9zjgc4t7xijyt(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_6wq9xs0pmvbqoj2c9917");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -5659,64 +5659,64 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
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@@ -6038,12 +6038,12 @@ <h1 class="title">Model Summaries</h1>
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@@ -6143,13 +6143,13 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_glywgw6ov80uyl6a7ck3</title>
+<title>tinytable_6jv7kmo5efo0m52s8kuh</title>
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-      <table class="table table-borderless" id="tinytable_glywgw6ov80uyl6a7ck3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_6jv7kmo5efo0m52s8kuh" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -6233,12 +6233,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_lw3swor87dzy9c27g9q2(i, j, css_id) {
+        var table = document.getElementById("tinytable_6jv7kmo5efo0m52s8kuh");
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       function insertSpanRow(i, colspan, content) {
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+        var table = document.getElementById('tinytable_6jv7kmo5efo0m52s8kuh');
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+      function spanCell_tinytable_lw3swor87dzy9c27g9q2(i, j, rowspan, colspan) {
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@@ -6330,12 +6330,12 @@ <h1 class="title">Model Summaries</h1>
   
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@@ -6507,14 +6507,14 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_y983v9oe0oiaouncc3kt" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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-      function insertSpanRow_05x5bccm3a7y79evbp60(i, colspan, content) {
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+      function insertSpanRow_2osjgfcda8ulmsroi4uu(i, colspan, content) {
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+.table td.tinytable_css_zh4rfp2sw24xxkrr5gig, .table th.tinytable_css_zh4rfp2sw24xxkrr5gig {    text-align: left; }
+.table td.tinytable_css_c3v7xcfpnsm3y7099vjr, .table th.tinytable_css_c3v7xcfpnsm3y7099vjr {    text-align: center; }
+.table td.tinytable_css_2cqee0teuac985o20vtl, .table th.tinytable_css_2cqee0teuac985o20vtl {    text-align: center; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -6767,7 +6767,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_rmzveenlqp1so6t23e8g" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_mh0rh4x36zji6idjis27" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(I)</th>
@@ -6849,12 +6849,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_rmzveenlqp1so6t23e8g");
+      function styleCell_tinytable_c5ohesk90quwmm5kocu8(i, j, css_id) {
+        var table = document.getElementById("tinytable_mh0rh4x36zji6idjis27");
         table.rows[i].cells[j].classList.add(css_id);
       }
-      function insertSpanRow_kcvuro961wix2f7a7ant(i, colspan, content) {
-        var table = document.getElementById('tinytable_rmzveenlqp1so6t23e8g');
+      function insertSpanRow_tonolendb7ipzi3l1gga(i, colspan, content) {
+        var table = document.getElementById('tinytable_mh0rh4x36zji6idjis27');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -6862,8 +6862,8 @@ <h1 class="title">Model Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_so0a5dei0n7oe2989ba7(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_rmzveenlqp1so6t23e8g");
+      function spanCell_tinytable_c5ohesk90quwmm5kocu8(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_mh0rh4x36zji6idjis27");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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@@ -6890,79 +6890,79 @@ <h1 class="title">Model Summaries</h1>
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@@ -7357,8 +7357,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_usor386t8xhdp6x2wt8z(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_pd7bbcw5lf60sd2yj61u");
+      function spanCell_tinytable_nwgp1gs71fh9jfrbctsa(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_glh2tfj40vrzbc6l8hzb");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -7385,58 +7385,58 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
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@@ -7466,15 +7466,15 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_pmlwp438dzqjq74rpai3</title>
+<title>tinytable_rx0se4nak144lxodtco6</title>
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+      <table class="table table-borderless" id="tinytable_rx0se4nak144lxodtco6" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -7563,12 +7563,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_k8gwnjtwbizqlciz71sq(i, j, css_id) {
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         newCell.setAttribute("colspan", colspan);
@@ -7576,8 +7576,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_7g4lhoc0b7ib5vhm6mfe(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_pmlwp438dzqjq74rpai3");
+      function spanCell_tinytable_k8gwnjtwbizqlciz71sq(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_rx0se4nak144lxodtco6");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -7604,78 +7604,78 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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 <div class="sourceCode" id="cb61"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/rm.html">rm</a></span><span class="op">(</span><span class="st">"glance_custom.fixest"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -8531,12 +8531,12 @@ <h1 class="title">Model Summaries</h1>
   
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+<title>tinytable_ilm86p0fvjfgpjpfr2ms</title>
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-      <table class="table table-borderless" id="tinytable_x005147564gngte9a1nj" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ilm86p0fvjfgpjpfr2ms" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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+      function styleCell_tinytable_lpixpy5wyjkosdr9fd3r(i, j, css_id) {
+        var table = document.getElementById("tinytable_ilm86p0fvjfgpjpfr2ms");
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@@ -8739,12 +8739,12 @@ <h1 class="title">Model Summaries</h1>
   
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+<title>tinytable_ydt55vodj93gkxcu138j</title>
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+      <table class="table table-borderless" id="tinytable_ydt55vodj93gkxcu138j" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -8826,12 +8826,12 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_tfglwcdv5d1l4lf1ngu7");
+      function styleCell_tinytable_l11r7lj6zkeeyij8km3b(i, j, css_id) {
+        var table = document.getElementById("tinytable_ydt55vodj93gkxcu138j");
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       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_tfglwcdv5d1l4lf1ngu7');
+        var table = document.getElementById('tinytable_ydt55vodj93gkxcu138j');
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+      function spanCell_tinytable_l11r7lj6zkeeyij8km3b(i, j, rowspan, colspan) {
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@@ -9706,13 +9706,13 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_gnxqelpt3e58rikaxvvx" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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                 <th scope="col">(1)</th>
@@ -9733,33 +9733,33 @@ <h1 class="title">Model Summaries</h1>
 <tbody>
 <tr>
 <td>(Intercept)</td>
-                  <td>326.027 </td>
-                  <td>283.140 </td>
+                  <td>326.012 </td>
+                  <td>281.967 </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(31.202)</td>
-                  <td>(42.615)</td>
+                  <td>(30.638)</td>
+                  <td>(41.371)</td>
                 </tr>
 <tr>
 <td>mpg        </td>
-                  <td>-8.946  </td>
-                  <td>-10.079 </td>
+                  <td>-8.966  </td>
+                  <td>-10.205 </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(1.376) </td>
-                  <td>(2.341) </td>
+                  <td>(1.390) </td>
+                  <td>(2.339) </td>
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 <tr>
 <td>drat       </td>
                   <td>        </td>
-                  <td>18.241  </td>
+                  <td>19.036  </td>
                 </tr>
 <tr>
 <td>           </td>
                   <td>        </td>
-                  <td>(20.931)</td>
+                  <td>(20.568)</td>
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@@ -9806,12 +9806,12 @@ <h1 class="title">Model Summaries</h1>
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@@ -9933,12 +9933,12 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_vnnj050bm86wrx2jlr2i" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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                 <th scope="col">(1)</th>
@@ -9958,87 +9958,87 @@ <h1 class="title">Model Summaries</h1>
 <tbody>
 <tr>
 <td>fit_x_endo_1   </td>
-                  <td>0.646           </td>
+                  <td>-0.174           </td>
                 </tr>
 <tr>
 <td>               </td>
-                  <td>(0.246)         </td>
+                  <td>(4.108)          </td>
                 </tr>
 <tr>
 <td>fit_x_endo_2   </td>
-                  <td>0.444           </td>
+                  <td>3.060            </td>
                 </tr>
 <tr>
 <td>               </td>
-                  <td>(0.566)         </td>
+                  <td>(11.055)         </td>
                 </tr>
 <tr>
 <td>x1             </td>
-                  <td>0.461           </td>
+                  <td>0.329            </td>
                 </tr>
 <tr>
 <td>               </td>
-                  <td>(0.269)         </td>
+                  <td>(0.508)          </td>
                 </tr>
 <tr>
 <td>Num.Obs.       </td>
-                  <td>150             </td>
+                  <td>150              </td>
                 </tr>
 <tr>
 <td>R2             </td>
-                  <td>0.813           </td>
+                  <td>-2.741           </td>
                 </tr>
 <tr>
 <td>R2 Adj.        </td>
-                  <td>0.807           </td>
+                  <td>-2.871           </td>
                 </tr>
 <tr>
 <td>R2 Within      </td>
-                  <td>0.510           </td>
+                  <td>-8.812           </td>
                 </tr>
 <tr>
 <td>R2 Within Adj. </td>
-                  <td>0.500           </td>
+                  <td>-9.017           </td>
                 </tr>
 <tr>
 <td>AIC            </td>
-                  <td>128.5           </td>
+                  <td>578.0            </td>
                 </tr>
 <tr>
 <td>BIC            </td>
-                  <td>146.6           </td>
+                  <td>596.1            </td>
                 </tr>
 <tr>
 <td>RMSE           </td>
-                  <td>0.36            </td>
+                  <td>1.60             </td>
                 </tr>
 <tr>
 <td>Std.Errors     </td>
-                  <td>by: fe          </td>
+                  <td>by: fe           </td>
                 </tr>
 <tr>
 <td>FE: fe         </td>
-                  <td>X               </td>
+                  <td>X                </td>
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 <tr>
 <td>Wald (x_endo_1)</td>
-                  <td>46.9104378163272</td>
+                  <td>50.8615423860559 </td>
                 </tr>
 <tr>
 <td>Wald (x_endo_2)</td>
-                  <td>4.41706222570233</td>
+                  <td>0.581592469806361</td>
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@@ -10194,43 +10194,43 @@ <h1 class="title">Model Summaries</h1>
 <tbody>
 <tr>
 <td>(Intercept)</td>
-                  <td>77.468  </td>
-                  <td>68.041  </td>
-                  <td>79.707  </td>
+                  <td>81.719  </td>
+                  <td>75.699  </td>
+                  <td>79.645  </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(13.883)</td>
-                  <td>(17.248)</td>
-                  <td>(12.214)</td>
+                  <td>(14.584)</td>
+                  <td>(13.342)</td>
+                  <td>(13.468)</td>
                 </tr>
 <tr>
 <td>Literacy   </td>
-                  <td>-0.513  </td>
-                  <td>-0.379  </td>
-                  <td>-0.526  </td>
+                  <td>-0.638  </td>
+                  <td>-0.499  </td>
+                  <td>-0.541  </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(0.210) </td>
-                  <td>(0.257) </td>
-                  <td>(0.183) </td>
+                  <td>(0.234) </td>
+                  <td>(0.205) </td>
+                  <td>(0.216) </td>
                 </tr>
 <tr>
 <td>Commerce   </td>
-                  <td>-0.320  </td>
-                  <td>-0.229  </td>
-                  <td>-0.338  </td>
+                  <td>-0.305  </td>
+                  <td>-0.283  </td>
+                  <td>-0.331  </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(0.154) </td>
-                  <td>(0.186) </td>
-                  <td>(0.134) </td>
+                  <td>(0.168) </td>
+                  <td>(0.145) </td>
+                  <td>(0.148) </td>
                 </tr>
 <tr>
 <td>Num.Obs.   </td>
-                  <td>61      </td>
+                  <td>59      </td>
                   <td>86      </td>
                   <td>86      </td>
                 </tr>
@@ -10242,37 +10242,43 @@ <h1 class="title">Model Summaries</h1>
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 <tr>
 <td>R2         </td>
-                  <td>0.100   </td>
-                  <td>0.054   </td>
-                  <td>0.105   </td>
+                  <td>0.121   </td>
+                  <td>0.078   </td>
+                  <td>0.102   </td>
                 </tr>
 <tr>
 <td>R2 Adj.    </td>
-                  <td>0.069   </td>
-                  <td>        </td>
-                  <td>0.083   </td>
+                  <td>0.089   </td>
+                  <td>0.055   </td>
+                  <td>0.079   </td>
                 </tr>
 <tr>
 <td>AIC        </td>
-                  <td>563.7   </td>
+                  <td>557.0   </td>
                   <td>        </td>
                   <td>        </td>
                 </tr>
 <tr>
 <td>BIC        </td>
-                  <td>572.2   </td>
+                  <td>565.3   </td>
                   <td>        </td>
                   <td>        </td>
                 </tr>
 <tr>
 <td>Log.Lik.   </td>
-                  <td>-277.862</td>
+                  <td>-274.488</td>
+                  <td>        </td>
+                  <td>        </td>
+                </tr>
+<tr>
+<td>F          </td>
+                  <td>3.843   </td>
                   <td>        </td>
                   <td>        </td>
                 </tr>
 <tr>
 <td>RMSE       </td>
-                  <td>23.02   </td>
+                  <td>25.37   </td>
                   <td>        </td>
                   <td>        </td>
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@@ -10281,12 +10287,12 @@ <h1 class="title">Model Summaries</h1>
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_bzjss5rake3s1sfgl1k5');
+        var table = document.getElementById('tinytable_3a5oeypthhhlna2s2bdl');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -10294,8 +10300,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_ui9zhavyzwuzxa45jsg8(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_bzjss5rake3s1sfgl1k5");
+      function spanCell_tinytable_xzv6ut5k65hmqzdtg9bu(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_3a5oeypthhhlna2s2bdl");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -10322,74 +10328,78 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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 <div class="cell-output cell-output-stdout">
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+<pre><code>parameters: 0.026 sec elapsed</code></pre>
 </div>
 <div class="sourceCode" id="cb102"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/tictoc/man/tic.html">tic</a></span><span class="op">(</span><span class="st">"performance"</span><span class="op">)</span></span>
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 <span><span class="fu"><a href="https://rdrr.io/pkg/tictoc/man/tic.html">toc</a></span><span class="op">(</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
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+<pre><code>performance: 0.014 sec elapsed</code></pre>
 </div>
 </div>
 <p>In my experience, the main bottleneck tends to be computing goodness-of-fit statistics. The <code>performance</code> extractor allows users to specify a <code>metrics</code> argument to select a subset of GOF to include. Using this can speedup things considerably.</p>
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 <div class="cell-output cell-output-stdout">
 <pre><code>         term      estimate         mad conf.level     conf.low    conf.high prior.distribution prior.location prior.scale group std.error statistic p.value
-1 (Intercept) -2.2318051666 0.571312754       0.95 -3.398159779 -1.036557136             normal        0.40625  1.24747729              NA        NA      NA
-2          hp  0.0006961822 0.001081759       0.95 -0.001400122  0.002867875             normal        0.00000  0.01819465              NA        NA      NA
-3        drat  0.7056622409 0.131337768       0.95  0.425382271  0.970069039             normal        0.00000  2.33313429              NA        NA      NA</code></pre>
+1 (Intercept) -2.2246277000 0.599969846       0.95 -3.433826756 -1.055312644             normal        0.40625  1.24747729              NA        NA      NA
+2          hp  0.0006919802 0.001109785       0.95 -0.001499742  0.002809575             normal        0.00000  0.01819465              NA        NA      NA
+3        drat  0.7030982373 0.139586860       0.95  0.434089902  0.984676752             normal        0.00000  2.33313429              NA        NA      NA</code></pre>
 </div>
 </div>
 <p>This shows that there is no <code>std.error</code> column, but that there is a <code>mad</code> statistic (mean absolute deviation). So we can do:</p>
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@@ -10895,9 +10905,9 @@ <h1 class="title">Model Summaries</h1>
 <div class="sourceCode" id="cb119"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://modelsummary.com/man/get_estimates.html">get_estimates</a></span><span class="op">(</span><span class="va">mod</span>, centrality <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>         term      estimate     std.dev conf.level     conf.low    conf.high prior.distribution prior.location prior.scale group std.error statistic p.value
-1 (Intercept) -2.2253997084 0.593524210       0.95 -3.398159779 -1.036557136             normal        0.40625  1.24747729              NA        NA      NA
-2          hp  0.0006951465 0.001091155       0.95 -0.001400122  0.002867875             normal        0.00000  0.01819465              NA        NA      NA
-3        drat  0.7034512498 0.138457886       0.95  0.425382271  0.970069039             normal        0.00000  2.33313429              NA        NA      NA</code></pre>
+1 (Intercept) -2.2256516592 0.604144261       0.95 -3.433826756 -1.055312644             normal        0.40625  1.24747729              NA        NA      NA
+2          hp  0.0006990604 0.001121095       0.95 -0.001499742  0.002809575             normal        0.00000  0.01819465              NA        NA      NA
+3        drat  0.7031661013 0.140682478       0.95  0.434089902  0.984676752             normal        0.00000  2.33313429              NA        NA      NA</code></pre>
 </div>
 <div class="sourceCode" id="cb121"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://modelsummary.com/man/modelsummary.html">modelsummary</a></span><span class="op">(</span><span class="va">mod</span>, statistic <span class="op">=</span> <span class="st">"std.dev"</span>, centrality <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -10931,11 +10941,11 @@ <h1 class="title">Model Summaries</h1>
 <tbody>
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 <td>(Intercept)</td>
-                  <td>-2.225 </td>
+                  <td>-2.226 </td>
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 <tr>
 <td>           </td>
-                  <td>(0.594)</td>
+                  <td>(0.604)</td>
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 <tr>
 <td>           </td>
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+                  <td>(0.141)</td>
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 <tr>
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 <tr>
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 <tr>
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 <tr>
 <td>WAIC       </td>
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+                  <td>30.1   </td>
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@@ -11084,10 +11094,10 @@ <h1 class="title">Model Summaries</h1>
 <div class="cell">
 <div class="sourceCode" id="cb122"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://modelsummary.com/man/get_estimates.html">get_estimates</a></span><span class="op">(</span><span class="va">mod</span>, test <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"pd"</span>, <span class="st">"rope"</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>         term      estimate         mad conf.level     conf.low    conf.high      pd rope.percentage prior.distribution prior.location prior.scale group std.error statistic p.value
-1 (Intercept) -2.2318051666 0.571312754       0.95 -3.398159779 -1.036557136 0.99925               0             normal        0.40625  1.24747729              NA        NA      NA
-2          hp  0.0006961822 0.001081759       0.95 -0.001400122  0.002867875 0.74000               1             normal        0.00000  0.01819465              NA        NA      NA
-3        drat  0.7056622409 0.131337768       0.95  0.425382271  0.970069039 1.00000               0             normal        0.00000  2.33313429              NA        NA      NA</code></pre>
+<pre><code>         term      estimate         mad conf.level     conf.low    conf.high     pd rope.percentage prior.distribution prior.location prior.scale group std.error statistic p.value
+1 (Intercept) -2.2246277000 0.599969846       0.95 -3.433826756 -1.055312644 0.9995               0             normal        0.40625  1.24747729              NA        NA      NA
+2          hp  0.0006919802 0.001109785       0.95 -0.001499742  0.002809575 0.7330               1             normal        0.00000  0.01819465              NA        NA      NA
+3        drat  0.7030982373 0.139586860       0.95  0.434089902  0.984676752 1.0000               0             normal        0.00000  2.33313429              NA        NA      NA</code></pre>
 </div>
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diff --git a/vignettes/modelsummary_extension.html b/vignettes/modelsummary_extension.html
index 1187b158..79c479ff 100644
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-        var table = document.getElementById("tinytable_f7k6a6z0fmzcdwvwlapd");
+      function spanCell_tinytable_w2ao64vjccgz2tyb7ye7(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_jksl040r3pxtt6k384tx");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -524,28 +524,28 @@ <h1 class="title">Extension and Customization</h1>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(0, 0, 'tinytable_css_fw0kt6315mr6ufedxqnt') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(0, 1, 'tinytable_css_fw0kt6315mr6ufedxqnt') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(0, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(1, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(2, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(3, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(4, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(5, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(6, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(7, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(8, 0, 'tinytable_css_vy8kltgoxzz67ekmtwkj') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(0, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(1, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(2, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(3, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(4, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(5, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(6, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(7, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(8, 1, 'tinytable_css_iiovkj4plwd3ntcmahyh') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(6, 0, 'tinytable_css_v16j1psb2wf0vp3vhxoy') })
-window.addEventListener('load', function () { styleCell_tinytable_ihiagan98txc7zhne72v(6, 1, 'tinytable_css_v16j1psb2wf0vp3vhxoy') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(0, 0, 'tinytable_css_ly32qidz9kvbthhratqo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(0, 1, 'tinytable_css_ly32qidz9kvbthhratqo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(0, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(1, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(2, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(3, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(4, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(5, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(6, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(7, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(8, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(0, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(1, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(2, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(3, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(4, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(5, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(6, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(7, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(8, 1, 'tinytable_css_7vq9q1ft10bc9skh6bzo') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(6, 0, 'tinytable_css_z0c1amkiefrm8r5rl382') })
+window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(6, 1, 'tinytable_css_z0c1amkiefrm8r5rl382') })
     </script>
 </div>
 </div>
@@ -631,12 +631,12 @@ <h1 class="title">Extension and Customization</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_f0vx584sr6fschoqglq0</title>
+<title>tinytable_5twluwvpajnufzz0zw3n</title>
 <style>
-.table td.tinytable_css_epk4ds7c1nyr69vvcshe, .table th.tinytable_css_epk4ds7c1nyr69vvcshe {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_2upo1bd203wbuvg35cah, .table th.tinytable_css_2upo1bd203wbuvg35cah {    text-align: left; }
-.table td.tinytable_css_sj503mdiyb7r6atos788, .table th.tinytable_css_sj503mdiyb7r6atos788 {    text-align: center; }
-.table td.tinytable_css_ncju1sz76rrkhzvstxcw, .table th.tinytable_css_ncju1sz76rrkhzvstxcw {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_cxy3ib2c7geuyzydemah, .table th.tinytable_css_cxy3ib2c7geuyzydemah {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_haue84cjt4ne791tysm4, .table th.tinytable_css_haue84cjt4ne791tysm4 {    text-align: left; }
+.table td.tinytable_css_i02nd6mecwoxosrjbepd, .table th.tinytable_css_i02nd6mecwoxosrjbepd {    text-align: center; }
+.table td.tinytable_css_p2pu2xltefeeam9ksre5, .table th.tinytable_css_p2pu2xltefeeam9ksre5 {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -648,7 +648,7 @@ <h1 class="title">Extension and Customization</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_f0vx584sr6fschoqglq0" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_5twluwvpajnufzz0zw3n" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(1)</th>
@@ -708,12 +708,12 @@ <h1 class="title">Extension and Customization</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_bp8a575xkuvwalgn3q0t(i, j, css_id) {
-        var table = document.getElementById("tinytable_f0vx584sr6fschoqglq0");
+      function styleCell_tinytable_qs19tv3lwc61ckh7se0f(i, j, css_id) {
+        var table = document.getElementById("tinytable_5twluwvpajnufzz0zw3n");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_f0vx584sr6fschoqglq0');
+        var table = document.getElementById('tinytable_5twluwvpajnufzz0zw3n');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -721,8 +721,8 @@ <h1 class="title">Extension and Customization</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_bp8a575xkuvwalgn3q0t(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_f0vx584sr6fschoqglq0");
+      function spanCell_tinytable_qs19tv3lwc61ckh7se0f(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_5twluwvpajnufzz0zw3n");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -749,36 +749,36 @@ <h1 class="title">Extension and Customization</h1>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(0, 0, 'tinytable_css_epk4ds7c1nyr69vvcshe') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(0, 1, 'tinytable_css_epk4ds7c1nyr69vvcshe') })
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-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(2, 0, 'tinytable_css_2upo1bd203wbuvg35cah') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(3, 0, 'tinytable_css_2upo1bd203wbuvg35cah') })
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-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(5, 0, 'tinytable_css_2upo1bd203wbuvg35cah') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(6, 0, 'tinytable_css_2upo1bd203wbuvg35cah') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(7, 0, 'tinytable_css_2upo1bd203wbuvg35cah') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(8, 0, 'tinytable_css_2upo1bd203wbuvg35cah') })
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-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(12, 0, 'tinytable_css_2upo1bd203wbuvg35cah') })
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-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(1, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(2, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(3, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(4, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(5, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
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-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(7, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(8, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(9, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(10, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(11, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(12, 1, 'tinytable_css_sj503mdiyb7r6atos788') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(8, 0, 'tinytable_css_ncju1sz76rrkhzvstxcw') })
-window.addEventListener('load', function () { styleCell_tinytable_bp8a575xkuvwalgn3q0t(8, 1, 'tinytable_css_ncju1sz76rrkhzvstxcw') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(0, 0, 'tinytable_css_cxy3ib2c7geuyzydemah') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(0, 1, 'tinytable_css_cxy3ib2c7geuyzydemah') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(0, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(1, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(2, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(3, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(4, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(5, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(6, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(7, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(8, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(9, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(10, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(11, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(12, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(0, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(1, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(2, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(3, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(4, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(5, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(6, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(7, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(8, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(9, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(10, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(11, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(12, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(8, 0, 'tinytable_css_p2pu2xltefeeam9ksre5') })
+window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(8, 1, 'tinytable_css_p2pu2xltefeeam9ksre5') })
     </script>
 </div>
 </div>
@@ -827,13 +827,13 @@ <h1 class="title">Extension and Customization</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_hwtou6jdyzx35b3mgv7n</title>
+<title>tinytable_m5fjx7sp2ezy631papdo</title>
 <style>
-.table td.tinytable_css_k70lijxca8mx2jnnicms, .table th.tinytable_css_k70lijxca8mx2jnnicms {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_pw73jknv53fwozal33xj, .table th.tinytable_css_pw73jknv53fwozal33xj {    text-align: left; }
-.table td.tinytable_css_drre946z8eh3h55ae7tm, .table th.tinytable_css_drre946z8eh3h55ae7tm {    text-align: center; }
-.table td.tinytable_css_zf3ccyq31vssivftdnz2, .table th.tinytable_css_zf3ccyq31vssivftdnz2 {    text-align: center; }
-.table td.tinytable_css_gffm88z6h665y61w5hzp, .table th.tinytable_css_gffm88z6h665y61w5hzp {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_v7syctqna80nkn8t7zd4, .table th.tinytable_css_v7syctqna80nkn8t7zd4 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_id3jk4hiqbea1ludd3vq, .table th.tinytable_css_id3jk4hiqbea1ludd3vq {    text-align: left; }
+.table td.tinytable_css_c7hauzomlojb6ry0isv7, .table th.tinytable_css_c7hauzomlojb6ry0isv7 {    text-align: center; }
+.table td.tinytable_css_ncny549r8hc03m0rgx9f, .table th.tinytable_css_ncny549r8hc03m0rgx9f {    text-align: center; }
+.table td.tinytable_css_0rsybudliyukztlq9c9e, .table th.tinytable_css_0rsybudliyukztlq9c9e {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -845,7 +845,7 @@ <h1 class="title">Extension and Customization</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_hwtou6jdyzx35b3mgv7n" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_m5fjx7sp2ezy631papdo" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(1)</th>
@@ -942,12 +942,12 @@ <h1 class="title">Extension and Customization</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_q6033hbnn7q6fb8o9pk2(i, j, css_id) {
-        var table = document.getElementById("tinytable_hwtou6jdyzx35b3mgv7n");
+      function styleCell_tinytable_x952bwvicpt0gmg3srz3(i, j, css_id) {
+        var table = document.getElementById("tinytable_m5fjx7sp2ezy631papdo");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_hwtou6jdyzx35b3mgv7n');
+        var table = document.getElementById('tinytable_m5fjx7sp2ezy631papdo');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -955,8 +955,8 @@ <h1 class="title">Extension and Customization</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_q6033hbnn7q6fb8o9pk2(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_hwtou6jdyzx35b3mgv7n");
+      function spanCell_tinytable_x952bwvicpt0gmg3srz3(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_m5fjx7sp2ezy631papdo");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -983,66 +983,66 @@ <h1 class="title">Extension and Customization</h1>
         targetCell.colSpan = colspan;
       }
 
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+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(13, 2, 'tinytable_css_ncny549r8hc03m0rgx9f') })
+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(14, 2, 'tinytable_css_ncny549r8hc03m0rgx9f') })
+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(15, 2, 'tinytable_css_ncny549r8hc03m0rgx9f') })
+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(16, 2, 'tinytable_css_ncny549r8hc03m0rgx9f') })
+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(17, 2, 'tinytable_css_ncny549r8hc03m0rgx9f') })
+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(8, 0, 'tinytable_css_0rsybudliyukztlq9c9e') })
+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(8, 1, 'tinytable_css_0rsybudliyukztlq9c9e') })
+window.addEventListener('load', function () { styleCell_tinytable_x952bwvicpt0gmg3srz3(8, 2, 'tinytable_css_0rsybudliyukztlq9c9e') })
     </script>
 </div>
 </div>
@@ -1080,12 +1080,12 @@ <h1 class="title">Extension and Customization</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_rhkg49z730j7lvo6r1y4</title>
+<title>tinytable_h4l0flseibxj7c69crx7</title>
 <style>
-.table td.tinytable_css_66qn3h5pwb5nnvbgemxn, .table th.tinytable_css_66qn3h5pwb5nnvbgemxn {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_bl3o9ag6xexddk8nepp2, .table th.tinytable_css_bl3o9ag6xexddk8nepp2 {    text-align: left; }
-.table td.tinytable_css_ga57npuchyh5dh4lne4u, .table th.tinytable_css_ga57npuchyh5dh4lne4u {    text-align: center; }
-.table td.tinytable_css_vus097n2u8bkgj98i9ud, .table th.tinytable_css_vus097n2u8bkgj98i9ud {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_uo4q58yez6v1qdl37h72, .table th.tinytable_css_uo4q58yez6v1qdl37h72 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_3of3inl20cf1f1hmk28p, .table th.tinytable_css_3of3inl20cf1f1hmk28p {    text-align: left; }
+.table td.tinytable_css_t8g88wgllp46k9gw5juk, .table th.tinytable_css_t8g88wgllp46k9gw5juk {    text-align: center; }
+.table td.tinytable_css_ej85sgaibe270gjwk04f, .table th.tinytable_css_ej85sgaibe270gjwk04f {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1097,7 +1097,7 @@ <h1 class="title">Extension and Customization</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_rhkg49z730j7lvo6r1y4" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_h4l0flseibxj7c69crx7" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">(1)</th>
@@ -1140,12 +1140,12 @@ <h1 class="title">Extension and Customization</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_cw30d2bu2gd8mtltkaff(i, j, css_id) {
-        var table = document.getElementById("tinytable_rhkg49z730j7lvo6r1y4");
+      function styleCell_tinytable_tsl87hzyeieeglj3k2wi(i, j, css_id) {
+        var table = document.getElementById("tinytable_h4l0flseibxj7c69crx7");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_rhkg49z730j7lvo6r1y4');
+        var table = document.getElementById('tinytable_h4l0flseibxj7c69crx7');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1153,8 +1153,8 @@ <h1 class="title">Extension and Customization</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_cw30d2bu2gd8mtltkaff(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_rhkg49z730j7lvo6r1y4");
+      function spanCell_tinytable_tsl87hzyeieeglj3k2wi(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_h4l0flseibxj7c69crx7");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1181,28 +1181,28 @@ <h1 class="title">Extension and Customization</h1>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(0, 0, 'tinytable_css_66qn3h5pwb5nnvbgemxn') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(0, 1, 'tinytable_css_66qn3h5pwb5nnvbgemxn') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(0, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(1, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(2, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(3, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(4, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(5, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(6, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(7, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(8, 0, 'tinytable_css_bl3o9ag6xexddk8nepp2') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(0, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(1, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(2, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(3, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(4, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(5, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(6, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(7, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(8, 1, 'tinytable_css_ga57npuchyh5dh4lne4u') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(6, 0, 'tinytable_css_vus097n2u8bkgj98i9ud') })
-window.addEventListener('load', function () { styleCell_tinytable_cw30d2bu2gd8mtltkaff(6, 1, 'tinytable_css_vus097n2u8bkgj98i9ud') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(0, 0, 'tinytable_css_uo4q58yez6v1qdl37h72') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(0, 1, 'tinytable_css_uo4q58yez6v1qdl37h72') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(0, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(1, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(2, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(3, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(4, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(5, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(6, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(7, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(8, 0, 'tinytable_css_3of3inl20cf1f1hmk28p') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(0, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(1, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(2, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(3, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(4, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(5, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(6, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(7, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(8, 1, 'tinytable_css_t8g88wgllp46k9gw5juk') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(6, 0, 'tinytable_css_ej85sgaibe270gjwk04f') })
+window.addEventListener('load', function () { styleCell_tinytable_tsl87hzyeieeglj3k2wi(6, 1, 'tinytable_css_ej85sgaibe270gjwk04f') })
     </script>
 </div>
 </div>
@@ -1250,13 +1250,13 @@ <h1 class="title">Extension and Customization</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_4ybcmel83wjzwixxyrgk</title>
+<title>tinytable_utn1rzbcwfztkd0j2ikt</title>
 <style>
-.table td.tinytable_css_ww73jw6hn8ybh7mhw8hb, .table th.tinytable_css_ww73jw6hn8ybh7mhw8hb {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_sh4yrpzvpvfif4d4f3mt, .table th.tinytable_css_sh4yrpzvpvfif4d4f3mt {    text-align: left; }
-.table td.tinytable_css_tu2h14hx9jwwzcwpzjj8, .table th.tinytable_css_tu2h14hx9jwwzcwpzjj8 {    text-align: center; }
-.table td.tinytable_css_cwes7drqkw7c9tm77vge, .table th.tinytable_css_cwes7drqkw7c9tm77vge {    text-align: center; }
-.table td.tinytable_css_kcu23c34brj18gsriy6z, .table th.tinytable_css_kcu23c34brj18gsriy6z {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_xm53okcmtpcglphv9jfs, .table th.tinytable_css_xm53okcmtpcglphv9jfs {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_1h4fysyl4hbrvbd5xfme, .table th.tinytable_css_1h4fysyl4hbrvbd5xfme {    text-align: left; }
+.table td.tinytable_css_36vmwc99ooutbq1glkjr, .table th.tinytable_css_36vmwc99ooutbq1glkjr {    text-align: center; }
+.table td.tinytable_css_pecgmn5oexmufb42ikhb, .table th.tinytable_css_pecgmn5oexmufb42ikhb {    text-align: center; }
+.table td.tinytable_css_otgkpla5yzf58q90nvwe, .table th.tinytable_css_otgkpla5yzf58q90nvwe {    border-bottom: solid 0.05em black; }
     </style>
 <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script><script id="MathJax-script" async="" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script><script>
     MathJax = {
@@ -1268,7 +1268,7 @@ <h1 class="title">Extension and Customization</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_4ybcmel83wjzwixxyrgk" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_utn1rzbcwfztkd0j2ikt" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col"> </th>
                 <th scope="col">Wald</th>
@@ -1336,12 +1336,12 @@ <h1 class="title">Extension and Customization</h1>
 </div>
 
     <script>
-      function styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(i, j, css_id) {
-        var table = document.getElementById("tinytable_4ybcmel83wjzwixxyrgk");
+      function styleCell_tinytable_xl3hbcrqmn71j60wq8cz(i, j, css_id) {
+        var table = document.getElementById("tinytable_utn1rzbcwfztkd0j2ikt");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_4ybcmel83wjzwixxyrgk');
+        var table = document.getElementById('tinytable_utn1rzbcwfztkd0j2ikt');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1349,8 +1349,8 @@ <h1 class="title">Extension and Customization</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_j3s3ds3rg4q2ws1uebbb(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_4ybcmel83wjzwixxyrgk");
+      function spanCell_tinytable_xl3hbcrqmn71j60wq8cz(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_utn1rzbcwfztkd0j2ikt");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1377,48 +1377,48 @@ <h1 class="title">Extension and Customization</h1>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(0, 0, 'tinytable_css_ww73jw6hn8ybh7mhw8hb') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(0, 1, 'tinytable_css_ww73jw6hn8ybh7mhw8hb') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(0, 2, 'tinytable_css_ww73jw6hn8ybh7mhw8hb') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(0, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(1, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(2, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(3, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(4, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(5, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(6, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(7, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(8, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(9, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(10, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(11, 0, 'tinytable_css_sh4yrpzvpvfif4d4f3mt') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(0, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(1, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(2, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(3, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(4, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(5, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(6, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(7, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(8, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(9, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(10, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
-window.addEventListener('load', function () { styleCell_tinytable_j3s3ds3rg4q2ws1uebbb(11, 1, 'tinytable_css_tu2h14hx9jwwzcwpzjj8') })
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