From 5e81ab00717ecdec4ec9d7b320db40e0f934d8ba Mon Sep 17 00:00:00 2001
From: vincentarelbundock <vincentarelbundock@users.noreply.github.com>
Date: Thu, 16 May 2024 03:07:51 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20vincenta?=
 =?UTF-8?q?relbundock/modelsummary@b40bd237775f09dcfacb592f07b9777eb9757d5?=
 =?UTF-8?q?7=20=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 man/coef_rename.html                          |   98 +-
 man/datasummary.html                          |  642 +-
 man/datasummary_balance.html                  |  250 +-
 man/datasummary_correlation.html              |  358 +-
 man/datasummary_correlation_format.html       |   64 +-
 man/datasummary_crosstab.html                 |  544 +-
 man/dvnames.html                              |  252 +-
 search.json                                   |   84 +-
 vignettes/appearance.html                     | 1214 ++--
 vignettes/datasummary.html                    | 4846 ++++++-------
 vignettes/get_started.html                    |  808 +--
 vignettes/modelplot.html                      |  194 +-
 vignettes/modelsummary.html                   | 6330 ++++++++---------
 vignettes/modelsummary_extension.html         |  466 +-
 .../id0514m942vvnijlnihcrn.png                |  Bin 0 -> 1295 bytes
 .../id51top1lgh29wn36x1i3q.png                |  Bin 0 -> 1433 bytes
 .../id7gq3p7xrmxzgz3czxz5e.png                |  Bin 0 -> 1728 bytes
 .../idbvvzhsj7tpyk2th6f6xm.png                |  Bin 0 -> 2316 bytes
 .../idelrgotvcsxg0cj5ewz79.png                |  Bin 0 -> 12872 bytes
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 .../idi8gnz0s7w0v95g9caeh2.png                |  Bin 0 -> 2344 bytes
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 .../iduaergfij55nl3egazed4.png                |  Bin 0 -> 2214 bytes
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diff --git a/man/coef_rename.html b/man/coef_rename.html
index b71a2bd06..f4c5ffef4 100644
--- a/man/coef_rename.html
+++ b/man/coef_rename.html
@@ -459,12 +459,12 @@
   
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 <meta name="viewport" content="width=device-width, initial-scale=1.0">
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 <style>
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+.table td.tinytable_css_oje9gbb7oieob7hs26tj, .table th.tinytable_css_oje9gbb7oieob7hs26tj {    text-align: left; }
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@@ -476,7 +476,7 @@
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+      <table class="table table-borderless" id="tinytable_rbap4rknvkgzumrzimoe" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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+      function styleCell_tinytable_63ygd2pq3hg6zni74n4f(i, j, css_id) {
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       function insertSpanRow(i, colspan, content) {
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@@ -564,8 +564,8 @@
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-      function spanCell_tinytable_wy83zgkh5ncp04ynfoxd(i, j, rowspan, colspan) {
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diff --git a/man/datasummary.html b/man/datasummary.html
index d45d0591e..5a310d30f 100644
--- a/man/datasummary.html
+++ b/man/datasummary.html
@@ -653,11 +653,11 @@ <h4 class="anchored">
   
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+.table td.tinytable_css_004b1qbg5dy0stu30oi6, .table th.tinytable_css_004b1qbg5dy0stu30oi6 {    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 = {
@@ -669,7 +669,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_cb8fc5hsp369x94z5o0a" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_s7semkrxgev97l5nkimp" 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>
@@ -682,12 +682,12 @@ <h4 class="anchored">
 </div>
 
     <script>
-      function styleCell_tinytable_zirzka3ypbzffwutr59k(i, j, css_id) {
-        var table = document.getElementById("tinytable_cb8fc5hsp369x94z5o0a");
+      function styleCell_tinytable_ikiy4l0kvix43vhliw94(i, j, css_id) {
+        var table = document.getElementById("tinytable_s7semkrxgev97l5nkimp");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_cb8fc5hsp369x94z5o0a');
+        var table = document.getElementById('tinytable_s7semkrxgev97l5nkimp');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -695,8 +695,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_zirzka3ypbzffwutr59k(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_cb8fc5hsp369x94z5o0a");
+      function spanCell_tinytable_ikiy4l0kvix43vhliw94(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_s7semkrxgev97l5nkimp");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -723,12 +723,12 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-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') })
+window.addEventListener('load', function () { styleCell_tinytable_ikiy4l0kvix43vhliw94(0, 0, 'tinytable_css_8a56nwnj2nsug3srfy35') })
+window.addEventListener('load', function () { styleCell_tinytable_ikiy4l0kvix43vhliw94(0, 1, 'tinytable_css_8a56nwnj2nsug3srfy35') })
+window.addEventListener('load', function () { styleCell_tinytable_ikiy4l0kvix43vhliw94(0, 0, 'tinytable_css_s9m0x9keyfkjic2qiuey') })
+window.addEventListener('load', function () { styleCell_tinytable_ikiy4l0kvix43vhliw94(1, 0, 'tinytable_css_s9m0x9keyfkjic2qiuey') })
+window.addEventListener('load', function () { styleCell_tinytable_ikiy4l0kvix43vhliw94(0, 1, 'tinytable_css_004b1qbg5dy0stu30oi6') })
+window.addEventListener('load', function () { styleCell_tinytable_ikiy4l0kvix43vhliw94(1, 1, 'tinytable_css_004b1qbg5dy0stu30oi6') })
     </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_mgqzua34nrdbo0v0heu9</title>
+<title>tinytable_m7khicbwak6fux9qxac0</title>
 <style>
-.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; }
+.table td.tinytable_css_bn7q7n2ilzpa8lh6di9k, .table th.tinytable_css_bn7q7n2ilzpa8lh6di9k {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_8e80nmqghfdsh96kaock, .table th.tinytable_css_8e80nmqghfdsh96kaock {    text-align: left; }
+.table td.tinytable_css_azywbsd42o1lvejn1znv, .table th.tinytable_css_azywbsd42o1lvejn1znv {    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_mgqzua34nrdbo0v0heu9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_m7khicbwak6fux9qxac0" 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_8jclipbphy9zbal64fjg(i, j, css_id) {
-        var table = document.getElementById("tinytable_mgqzua34nrdbo0v0heu9");
+      function styleCell_tinytable_ohra84koqd2cpfyrnr8y(i, j, css_id) {
+        var table = document.getElementById("tinytable_m7khicbwak6fux9qxac0");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_mgqzua34nrdbo0v0heu9');
+        var table = document.getElementById('tinytable_m7khicbwak6fux9qxac0');
         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_8jclipbphy9zbal64fjg(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_mgqzua34nrdbo0v0heu9");
+      function spanCell_tinytable_ohra84koqd2cpfyrnr8y(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_m7khicbwak6fux9qxac0");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_ohra84koqd2cpfyrnr8y(0, 0, 'tinytable_css_bn7q7n2ilzpa8lh6di9k') })
+window.addEventListener('load', function () { styleCell_tinytable_ohra84koqd2cpfyrnr8y(0, 1, 'tinytable_css_bn7q7n2ilzpa8lh6di9k') })
+window.addEventListener('load', function () { styleCell_tinytable_ohra84koqd2cpfyrnr8y(0, 0, 'tinytable_css_8e80nmqghfdsh96kaock') })
+window.addEventListener('load', function () { styleCell_tinytable_ohra84koqd2cpfyrnr8y(1, 0, 'tinytable_css_8e80nmqghfdsh96kaock') })
+window.addEventListener('load', function () { styleCell_tinytable_ohra84koqd2cpfyrnr8y(0, 1, 'tinytable_css_azywbsd42o1lvejn1znv') })
+window.addEventListener('load', function () { styleCell_tinytable_ohra84koqd2cpfyrnr8y(1, 1, 'tinytable_css_azywbsd42o1lvejn1znv') })
     </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_ydb1f98fs4e1zy5fytpa</title>
+<title>tinytable_5nbmj7vh0iruu3ozufjr</title>
 <style>
-.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; }
+.table td.tinytable_css_ed8meha949848y480xnb, .table th.tinytable_css_ed8meha949848y480xnb {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_lld9v8d5rgdgmciwifk1, .table th.tinytable_css_lld9v8d5rgdgmciwifk1 {    text-align: left; }
+.table td.tinytable_css_igeve7f02lawclupndst, .table th.tinytable_css_igeve7f02lawclupndst {    text-align: right; }
+.table td.tinytable_css_i2wdbe2nhlopait8vxpj, .table th.tinytable_css_i2wdbe2nhlopait8vxpj {    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_ydb1f98fs4e1zy5fytpa" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_5nbmj7vh0iruu3ozufjr" 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_dx9v5w2iw4mxrvirmoqn(i, j, css_id) {
-        var table = document.getElementById("tinytable_ydb1f98fs4e1zy5fytpa");
+      function styleCell_tinytable_p9orfurw0l33wuggo9rb(i, j, css_id) {
+        var table = document.getElementById("tinytable_5nbmj7vh0iruu3ozufjr");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_ydb1f98fs4e1zy5fytpa');
+        var table = document.getElementById('tinytable_5nbmj7vh0iruu3ozufjr');
         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_dx9v5w2iw4mxrvirmoqn(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_ydb1f98fs4e1zy5fytpa");
+      function spanCell_tinytable_p9orfurw0l33wuggo9rb(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_5nbmj7vh0iruu3ozufjr");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(0, 0, 'tinytable_css_ed8meha949848y480xnb') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(0, 1, 'tinytable_css_ed8meha949848y480xnb') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(0, 2, 'tinytable_css_ed8meha949848y480xnb') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(0, 0, 'tinytable_css_lld9v8d5rgdgmciwifk1') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(1, 0, 'tinytable_css_lld9v8d5rgdgmciwifk1') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(2, 0, 'tinytable_css_lld9v8d5rgdgmciwifk1') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(0, 1, 'tinytable_css_igeve7f02lawclupndst') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(1, 1, 'tinytable_css_igeve7f02lawclupndst') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(2, 1, 'tinytable_css_igeve7f02lawclupndst') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(0, 2, 'tinytable_css_i2wdbe2nhlopait8vxpj') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(1, 2, 'tinytable_css_i2wdbe2nhlopait8vxpj') })
+window.addEventListener('load', function () { styleCell_tinytable_p9orfurw0l33wuggo9rb(2, 2, 'tinytable_css_i2wdbe2nhlopait8vxpj') })
     </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_6ex7dqxtss02tkvvzr3x</title>
+<title>tinytable_ignv475l53xdk408swtw</title>
 <style>
-.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; }
+.table td.tinytable_css_0uo32mx92sw4lont3mck, .table th.tinytable_css_0uo32mx92sw4lont3mck {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_gvq8jbvuzs076ogz99z6, .table th.tinytable_css_gvq8jbvuzs076ogz99z6 {    text-align: left; }
+.table td.tinytable_css_n509qlgov0o98ul2o2zp, .table th.tinytable_css_n509qlgov0o98ul2o2zp {    text-align: right; }
+.table td.tinytable_css_oeyazjxsaombm9fhle9l, .table th.tinytable_css_oeyazjxsaombm9fhle9l {    text-align: right; }
+.table td.tinytable_css_9hk4q8qu5nsqkyf03yea, .table th.tinytable_css_9hk4q8qu5nsqkyf03yea {    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_6ex7dqxtss02tkvvzr3x" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ignv475l53xdk408swtw" 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_g2xv83gjqf5ce7gw809f(i, j, css_id) {
-        var table = document.getElementById("tinytable_6ex7dqxtss02tkvvzr3x");
+      function styleCell_tinytable_kxukx3wzg721u9n664m2(i, j, css_id) {
+        var table = document.getElementById("tinytable_ignv475l53xdk408swtw");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_6ex7dqxtss02tkvvzr3x');
+        var table = document.getElementById('tinytable_ignv475l53xdk408swtw');
         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_g2xv83gjqf5ce7gw809f(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_6ex7dqxtss02tkvvzr3x");
+      function spanCell_tinytable_kxukx3wzg721u9n664m2(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ignv475l53xdk408swtw");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 0, 'tinytable_css_0uo32mx92sw4lont3mck') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 1, 'tinytable_css_0uo32mx92sw4lont3mck') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 2, 'tinytable_css_0uo32mx92sw4lont3mck') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 3, 'tinytable_css_0uo32mx92sw4lont3mck') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 0, 'tinytable_css_gvq8jbvuzs076ogz99z6') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(1, 0, 'tinytable_css_gvq8jbvuzs076ogz99z6') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(2, 0, 'tinytable_css_gvq8jbvuzs076ogz99z6') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 1, 'tinytable_css_n509qlgov0o98ul2o2zp') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(1, 1, 'tinytable_css_n509qlgov0o98ul2o2zp') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(2, 1, 'tinytable_css_n509qlgov0o98ul2o2zp') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 2, 'tinytable_css_oeyazjxsaombm9fhle9l') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(1, 2, 'tinytable_css_oeyazjxsaombm9fhle9l') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(2, 2, 'tinytable_css_oeyazjxsaombm9fhle9l') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(0, 3, 'tinytable_css_9hk4q8qu5nsqkyf03yea') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(1, 3, 'tinytable_css_9hk4q8qu5nsqkyf03yea') })
+window.addEventListener('load', function () { styleCell_tinytable_kxukx3wzg721u9n664m2(2, 3, 'tinytable_css_9hk4q8qu5nsqkyf03yea') })
     </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_l52r23bymea3u35ybhe9</title>
+<title>tinytable_32pkvff7ay2rlgqbbfwy</title>
 <style>
-.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; }
+.table td.tinytable_css_ddbnad90mm74l0i4smm7, .table th.tinytable_css_ddbnad90mm74l0i4smm7 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_u6kw2pdsau2era07aw98, .table th.tinytable_css_u6kw2pdsau2era07aw98 {    text-align: left; }
+.table td.tinytable_css_zfs9mhk4kt0ekw35qys9, .table th.tinytable_css_zfs9mhk4kt0ekw35qys9 {    text-align: right; }
+.table td.tinytable_css_w3749mn4txk6367mjc62, .table th.tinytable_css_w3749mn4txk6367mjc62 {    text-align: right; }
+.table td.tinytable_css_i5ex5lvtiu3psedv6ogv, .table th.tinytable_css_i5ex5lvtiu3psedv6ogv {    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_l52r23bymea3u35ybhe9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_32pkvff7ay2rlgqbbfwy" 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_zlbxaol73ifi5zke8j49(i, j, css_id) {
-        var table = document.getElementById("tinytable_l52r23bymea3u35ybhe9");
+      function styleCell_tinytable_q0yqmcxjbzmy82x3265k(i, j, css_id) {
+        var table = document.getElementById("tinytable_32pkvff7ay2rlgqbbfwy");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_l52r23bymea3u35ybhe9');
+        var table = document.getElementById('tinytable_32pkvff7ay2rlgqbbfwy');
         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_zlbxaol73ifi5zke8j49(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_l52r23bymea3u35ybhe9");
+      function spanCell_tinytable_q0yqmcxjbzmy82x3265k(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_32pkvff7ay2rlgqbbfwy");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 0, 'tinytable_css_ddbnad90mm74l0i4smm7') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 1, 'tinytable_css_ddbnad90mm74l0i4smm7') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 2, 'tinytable_css_ddbnad90mm74l0i4smm7') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 3, 'tinytable_css_ddbnad90mm74l0i4smm7') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 0, 'tinytable_css_u6kw2pdsau2era07aw98') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(1, 0, 'tinytable_css_u6kw2pdsau2era07aw98') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(2, 0, 'tinytable_css_u6kw2pdsau2era07aw98') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 1, 'tinytable_css_zfs9mhk4kt0ekw35qys9') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(1, 1, 'tinytable_css_zfs9mhk4kt0ekw35qys9') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(2, 1, 'tinytable_css_zfs9mhk4kt0ekw35qys9') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 2, 'tinytable_css_w3749mn4txk6367mjc62') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(1, 2, 'tinytable_css_w3749mn4txk6367mjc62') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(2, 2, 'tinytable_css_w3749mn4txk6367mjc62') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(0, 3, 'tinytable_css_i5ex5lvtiu3psedv6ogv') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(1, 3, 'tinytable_css_i5ex5lvtiu3psedv6ogv') })
+window.addEventListener('load', function () { styleCell_tinytable_q0yqmcxjbzmy82x3265k(2, 3, 'tinytable_css_i5ex5lvtiu3psedv6ogv') })
     </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_7aa4ffdw2msezxtwh3hg</title>
+<title>tinytable_n00bdt35ylfp8306esnw</title>
 <style>
-.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; }
+.table td.tinytable_css_9ct52tfcw32uakfsah3h, .table th.tinytable_css_9ct52tfcw32uakfsah3h {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_511khq1ngmguce87e80o, .table th.tinytable_css_511khq1ngmguce87e80o {    text-align: center; }
+.table td.tinytable_css_olh516mw9gbko76s1qqt, .table th.tinytable_css_olh516mw9gbko76s1qqt {    border-bottom: solid 0.05em #d3d8dc; }
+.table td.tinytable_css_escr8zubrg061t8r2fv4, .table th.tinytable_css_escr8zubrg061t8r2fv4 {    text-align: left; }
+.table td.tinytable_css_e5s7tj4td4hlggk01f3u, .table th.tinytable_css_e5s7tj4td4hlggk01f3u {    text-align: right; }
+.table td.tinytable_css_qftczddrl652adiq03fp, .table th.tinytable_css_qftczddrl652adiq03fp {    text-align: right; }
+.table td.tinytable_css_6eiyi61odfpxw43mbadm, .table th.tinytable_css_6eiyi61odfpxw43mbadm {    text-align: right; }
+.table td.tinytable_css_lro7li2d00od8h6lkv2n, .table th.tinytable_css_lro7li2d00od8h6lkv2n {    text-align: right; }
+.table td.tinytable_css_4hsyx2681fk7iiq2op60, .table th.tinytable_css_4hsyx2681fk7iiq2op60 {    text-align: right; }
+.table td.tinytable_css_qci698obincvxj9phe7x, .table th.tinytable_css_qci698obincvxj9phe7x {    text-align: right; }
+.table td.tinytable_css_69uk50wc35v286rjwy6i, .table th.tinytable_css_69uk50wc35v286rjwy6i {    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_7aa4ffdw2msezxtwh3hg" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_n00bdt35ylfp8306esnw" 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_4waddbmhi2bv0uasjaa2(i, j, css_id) {
-        var table = document.getElementById("tinytable_7aa4ffdw2msezxtwh3hg");
+      function styleCell_tinytable_apd1fcrt12pjcocd1uli(i, j, css_id) {
+        var table = document.getElementById("tinytable_n00bdt35ylfp8306esnw");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_7aa4ffdw2msezxtwh3hg');
+        var table = document.getElementById('tinytable_n00bdt35ylfp8306esnw');
         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_4waddbmhi2bv0uasjaa2(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_7aa4ffdw2msezxtwh3hg");
+      function spanCell_tinytable_apd1fcrt12pjcocd1uli(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_n00bdt35ylfp8306esnw");
         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;
       }
 
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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_pawkzn9g4hyhhyucmnml</title>
+<title>tinytable_ricso3pa8t0vzuqh3t1d</title>
 <style>
-.table td.tinytable_css_40rrhcfwg8ng8gkn1iew, .table th.tinytable_css_40rrhcfwg8ng8gkn1iew {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_xr17geqrbmzeqveudoo3, .table th.tinytable_css_xr17geqrbmzeqveudoo3 {    text-align: left; }
-.table td.tinytable_css_ads43x0af95ka8m8d84f, .table th.tinytable_css_ads43x0af95ka8m8d84f {    text-align: right; }
-.table td.tinytable_css_kwsqjkh0lkjq9igrdhwg, .table th.tinytable_css_kwsqjkh0lkjq9igrdhwg {    text-align: right; }
+.table td.tinytable_css_szdf1jokyeivs4p4x239, .table th.tinytable_css_szdf1jokyeivs4p4x239 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_n9oc32yag8fu3fu2ubpw, .table th.tinytable_css_n9oc32yag8fu3fu2ubpw {    text-align: left; }
+.table td.tinytable_css_2masypm8zxak5xjhnucd, .table th.tinytable_css_2masypm8zxak5xjhnucd {    text-align: right; }
+.table td.tinytable_css_6uso3poss5r02c28f01j, .table th.tinytable_css_6uso3poss5r02c28f01j {    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 = {
@@ -1346,7 +1346,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_pawkzn9g4hyhhyucmnml" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ricso3pa8t0vzuqh3t1d" 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>
 
     <script>
-      function styleCell_tinytable_jilyaeydpb7v03wtnwkl(i, j, css_id) {
-        var table = document.getElementById("tinytable_pawkzn9g4hyhhyucmnml");
+      function styleCell_tinytable_canf3hkl17t3kqjuc808(i, j, css_id) {
+        var table = document.getElementById("tinytable_ricso3pa8t0vzuqh3t1d");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_pawkzn9g4hyhhyucmnml');
+        var table = document.getElementById('tinytable_ricso3pa8t0vzuqh3t1d');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1421,8 +1421,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_jilyaeydpb7v03wtnwkl(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_pawkzn9g4hyhhyucmnml");
+      function spanCell_tinytable_canf3hkl17t3kqjuc808(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ricso3pa8t0vzuqh3t1d");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -1449,42 +1449,42 @@ <h4 class="anchored">
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+window.addEventListener('load', function () { styleCell_tinytable_canf3hkl17t3kqjuc808(6, 2, 'tinytable_css_6uso3poss5r02c28f01j') })
+window.addEventListener('load', function () { styleCell_tinytable_canf3hkl17t3kqjuc808(7, 2, 'tinytable_css_6uso3poss5r02c28f01j') })
+window.addEventListener('load', function () { styleCell_tinytable_canf3hkl17t3kqjuc808(8, 2, 'tinytable_css_6uso3poss5r02c28f01j') })
+window.addEventListener('load', function () { styleCell_tinytable_canf3hkl17t3kqjuc808(9, 2, 'tinytable_css_6uso3poss5r02c28f01j') })
+window.addEventListener('load', function () { styleCell_tinytable_canf3hkl17t3kqjuc808(10, 2, 'tinytable_css_6uso3poss5r02c28f01j') })
     </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_4d9z84cto2ud51troo8a</title>
+<title>tinytable_9pat3wi7wv8h2vk57pqg</title>
 <style>
-.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; }
+.table td.tinytable_css_hr8axs69gxgsrm1g3xn2, .table th.tinytable_css_hr8axs69gxgsrm1g3xn2 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_10pg6mpofy24u1unnbiw, .table th.tinytable_css_10pg6mpofy24u1unnbiw {    text-align: left; }
+.table td.tinytable_css_agfwl51kvtbu9vava0jt, .table th.tinytable_css_agfwl51kvtbu9vava0jt {    text-align: right; }
+.table td.tinytable_css_22971kogg9hg97w626zd, .table th.tinytable_css_22971kogg9hg97w626zd {    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_4d9z84cto2ud51troo8a" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_9pat3wi7wv8h2vk57pqg" 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_3yuzrdqouxzni3nwaub6(i, j, css_id) {
-        var table = document.getElementById("tinytable_4d9z84cto2ud51troo8a");
+      function styleCell_tinytable_zsuuz0css96prnbuczfv(i, j, css_id) {
+        var table = document.getElementById("tinytable_9pat3wi7wv8h2vk57pqg");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_4d9z84cto2ud51troo8a');
+        var table = document.getElementById('tinytable_9pat3wi7wv8h2vk57pqg');
         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_3yuzrdqouxzni3nwaub6(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_4d9z84cto2ud51troo8a");
+      function spanCell_tinytable_zsuuz0css96prnbuczfv(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_9pat3wi7wv8h2vk57pqg");
         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_3yuzrdqouxzni3nwaub6(0, 0, 'tinytable_css_r0r7ct6y2gowqfbtm4d3') })
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-window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(1, 2, 'tinytable_css_m5u39kj559ow9oq7cqx1') })
-window.addEventListener('load', function () { styleCell_tinytable_3yuzrdqouxzni3nwaub6(2, 2, 'tinytable_css_m5u39kj559ow9oq7cqx1') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(0, 0, 'tinytable_css_hr8axs69gxgsrm1g3xn2') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(0, 1, 'tinytable_css_hr8axs69gxgsrm1g3xn2') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(0, 2, 'tinytable_css_hr8axs69gxgsrm1g3xn2') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(0, 0, 'tinytable_css_10pg6mpofy24u1unnbiw') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(1, 0, 'tinytable_css_10pg6mpofy24u1unnbiw') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(2, 0, 'tinytable_css_10pg6mpofy24u1unnbiw') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(0, 1, 'tinytable_css_agfwl51kvtbu9vava0jt') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(1, 1, 'tinytable_css_agfwl51kvtbu9vava0jt') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(2, 1, 'tinytable_css_agfwl51kvtbu9vava0jt') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(0, 2, 'tinytable_css_22971kogg9hg97w626zd') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(1, 2, 'tinytable_css_22971kogg9hg97w626zd') })
+window.addEventListener('load', function () { styleCell_tinytable_zsuuz0css96prnbuczfv(2, 2, 'tinytable_css_22971kogg9hg97w626zd') })
     </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_m2i8k8eiy3qx5in92i21</title>
+<title>tinytable_81ezsy5w7a0oyus0zl8x</title>
 <style>
-.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; }
+.table td.tinytable_css_l244dh35xkq5mahn41k9, .table th.tinytable_css_l244dh35xkq5mahn41k9 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_mdpkefbragrawu7wzwcg, .table th.tinytable_css_mdpkefbragrawu7wzwcg {    text-align: left; }
+.table td.tinytable_css_qlwpatpra5gw77u0d3zd, .table th.tinytable_css_qlwpatpra5gw77u0d3zd {    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_m2i8k8eiy3qx5in92i21" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_81ezsy5w7a0oyus0zl8x" 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_pa5tzaza71aoiy212634(i, j, css_id) {
-        var table = document.getElementById("tinytable_m2i8k8eiy3qx5in92i21");
+      function styleCell_tinytable_t8rwdp0grxi2488s6dxf(i, j, css_id) {
+        var table = document.getElementById("tinytable_81ezsy5w7a0oyus0zl8x");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_m2i8k8eiy3qx5in92i21');
+        var table = document.getElementById('tinytable_81ezsy5w7a0oyus0zl8x');
         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_pa5tzaza71aoiy212634(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_m2i8k8eiy3qx5in92i21");
+      function spanCell_tinytable_t8rwdp0grxi2488s6dxf(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_81ezsy5w7a0oyus0zl8x");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(0, 0, 'tinytable_css_l244dh35xkq5mahn41k9') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(0, 1, 'tinytable_css_l244dh35xkq5mahn41k9') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(0, 0, 'tinytable_css_mdpkefbragrawu7wzwcg') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(1, 0, 'tinytable_css_mdpkefbragrawu7wzwcg') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(2, 0, 'tinytable_css_mdpkefbragrawu7wzwcg') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(0, 1, 'tinytable_css_qlwpatpra5gw77u0d3zd') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(1, 1, 'tinytable_css_qlwpatpra5gw77u0d3zd') })
+window.addEventListener('load', function () { styleCell_tinytable_t8rwdp0grxi2488s6dxf(2, 1, 'tinytable_css_qlwpatpra5gw77u0d3zd') })
     </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_e7pur5j2cizht4r7vfbd</title>
+<title>tinytable_u7t4m9cdy5jzxj3fvtdx</title>
 <style>
-.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; }
+.table td.tinytable_css_5nvnfoz5f0uvhm2loo6q, .table th.tinytable_css_5nvnfoz5f0uvhm2loo6q {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_xdyi92ed0ugq38g3swuz, .table th.tinytable_css_xdyi92ed0ugq38g3swuz {    text-align: left; }
+.table td.tinytable_css_xbeq1i1tny5mcntmbag1, .table th.tinytable_css_xbeq1i1tny5mcntmbag1 {    text-align: right; }
+.table td.tinytable_css_304l5hmkxldjeyhdew9l, .table th.tinytable_css_304l5hmkxldjeyhdew9l {    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_e7pur5j2cizht4r7vfbd" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_u7t4m9cdy5jzxj3fvtdx" 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_hsrqont3sjmhtnvr95ej(i, j, css_id) {
-        var table = document.getElementById("tinytable_e7pur5j2cizht4r7vfbd");
+      function styleCell_tinytable_xtc08fgajxf8k5lydg0y(i, j, css_id) {
+        var table = document.getElementById("tinytable_u7t4m9cdy5jzxj3fvtdx");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_e7pur5j2cizht4r7vfbd');
+        var table = document.getElementById('tinytable_u7t4m9cdy5jzxj3fvtdx');
         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_hsrqont3sjmhtnvr95ej(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_e7pur5j2cizht4r7vfbd");
+      function spanCell_tinytable_xtc08fgajxf8k5lydg0y(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_u7t4m9cdy5jzxj3fvtdx");
         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;
       }
 
-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') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(0, 0, 'tinytable_css_5nvnfoz5f0uvhm2loo6q') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(0, 1, 'tinytable_css_5nvnfoz5f0uvhm2loo6q') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(0, 2, 'tinytable_css_5nvnfoz5f0uvhm2loo6q') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(0, 0, 'tinytable_css_xdyi92ed0ugq38g3swuz') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(1, 0, 'tinytable_css_xdyi92ed0ugq38g3swuz') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(2, 0, 'tinytable_css_xdyi92ed0ugq38g3swuz') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(0, 1, 'tinytable_css_xbeq1i1tny5mcntmbag1') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(1, 1, 'tinytable_css_xbeq1i1tny5mcntmbag1') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(2, 1, 'tinytable_css_xbeq1i1tny5mcntmbag1') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(0, 2, 'tinytable_css_304l5hmkxldjeyhdew9l') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(1, 2, 'tinytable_css_304l5hmkxldjeyhdew9l') })
+window.addEventListener('load', function () { styleCell_tinytable_xtc08fgajxf8k5lydg0y(2, 2, 'tinytable_css_304l5hmkxldjeyhdew9l') })
     </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_vzhxuj0m159outzpq0j8</title>
+<title>tinytable_m0d5ihrifpqu0szkmfnt</title>
 <style>
-.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; }
+.table td.tinytable_css_bgobflgk26at1qx2np8k, .table th.tinytable_css_bgobflgk26at1qx2np8k {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_vwimvpzmksd1gbisncmh, .table th.tinytable_css_vwimvpzmksd1gbisncmh {    text-align: left; }
+.table td.tinytable_css_25o60975544mzmvfm5x5, .table th.tinytable_css_25o60975544mzmvfm5x5 {    text-align: right; }
+.table td.tinytable_css_wteqliovg93xoiwb6d6v, .table th.tinytable_css_wteqliovg93xoiwb6d6v {    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_vzhxuj0m159outzpq0j8" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_m0d5ihrifpqu0szkmfnt" 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_whwnk72vrx8bkumfe0fu(i, j, css_id) {
-        var table = document.getElementById("tinytable_vzhxuj0m159outzpq0j8");
+      function styleCell_tinytable_zdt9ksg6d6orpgfdzt71(i, j, css_id) {
+        var table = document.getElementById("tinytable_m0d5ihrifpqu0szkmfnt");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_vzhxuj0m159outzpq0j8');
+        var table = document.getElementById('tinytable_m0d5ihrifpqu0szkmfnt');
         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_whwnk72vrx8bkumfe0fu(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_vzhxuj0m159outzpq0j8");
+      function spanCell_tinytable_zdt9ksg6d6orpgfdzt71(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_m0d5ihrifpqu0szkmfnt");
         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;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_whwnk72vrx8bkumfe0fu(0, 0, 'tinytable_css_4rhdptu6sw886vkncq32') })
-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') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(0, 0, 'tinytable_css_bgobflgk26at1qx2np8k') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(0, 1, 'tinytable_css_bgobflgk26at1qx2np8k') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(0, 2, 'tinytable_css_bgobflgk26at1qx2np8k') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(0, 0, 'tinytable_css_vwimvpzmksd1gbisncmh') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(1, 0, 'tinytable_css_vwimvpzmksd1gbisncmh') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(2, 0, 'tinytable_css_vwimvpzmksd1gbisncmh') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(3, 0, 'tinytable_css_vwimvpzmksd1gbisncmh') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(4, 0, 'tinytable_css_vwimvpzmksd1gbisncmh') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(0, 1, 'tinytable_css_25o60975544mzmvfm5x5') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(1, 1, 'tinytable_css_25o60975544mzmvfm5x5') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(2, 1, 'tinytable_css_25o60975544mzmvfm5x5') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(3, 1, 'tinytable_css_25o60975544mzmvfm5x5') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(4, 1, 'tinytable_css_25o60975544mzmvfm5x5') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(0, 2, 'tinytable_css_wteqliovg93xoiwb6d6v') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(1, 2, 'tinytable_css_wteqliovg93xoiwb6d6v') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(2, 2, 'tinytable_css_wteqliovg93xoiwb6d6v') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(3, 2, 'tinytable_css_wteqliovg93xoiwb6d6v') })
+window.addEventListener('load', function () { styleCell_tinytable_zdt9ksg6d6orpgfdzt71(4, 2, 'tinytable_css_wteqliovg93xoiwb6d6v') })
     </script>
 </div>
 </div>
diff --git a/man/datasummary_balance.html b/man/datasummary_balance.html
index 8f504e63c..0692bd316 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">
-<title>tinytable_ulh2ykegwv72f064kn1v</title>
+<title>tinytable_htibnfzqgnwsx37vn4fs</title>
 <style>
-.table td.tinytable_css_4rg5b16dmp3fml8zj87b, .table th.tinytable_css_4rg5b16dmp3fml8zj87b {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_sn58s2ip7h2fiszevpmg, .table th.tinytable_css_sn58s2ip7h2fiszevpmg {    text-align: center; }
-.table td.tinytable_css_ww4y2izzuigfm7145kgq, .table th.tinytable_css_ww4y2izzuigfm7145kgq {    border-bottom: solid 0.05em #d3d8dc; }
-.table td.tinytable_css_k79mp5nyiwv33iatsmjp, .table th.tinytable_css_k79mp5nyiwv33iatsmjp {    text-align: left; }
-.table td.tinytable_css_717dy0960t9j70dr889r, .table th.tinytable_css_717dy0960t9j70dr889r {    text-align: right; }
-.table td.tinytable_css_il0bg6hobfhp3hi5dksj, .table th.tinytable_css_il0bg6hobfhp3hi5dksj {    text-align: right; }
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-.table td.tinytable_css_1hx9sqnhiq6y4fxp4bt3, .table th.tinytable_css_1hx9sqnhiq6y4fxp4bt3 {    text-align: center; }
+.table td.tinytable_css_ugf6yegcrpkmi2w1jew2, .table th.tinytable_css_ugf6yegcrpkmi2w1jew2 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_w4ij9mc2hxa80ap8levd, .table th.tinytable_css_w4ij9mc2hxa80ap8levd {    text-align: center; }
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+.table td.tinytable_css_mb8zzfqp3a8lhodsqezx, .table th.tinytable_css_mb8zzfqp3a8lhodsqezx {    text-align: right; }
+.table td.tinytable_css_rjbzezp4ysmqva250kpk, .table th.tinytable_css_rjbzezp4ysmqva250kpk {    text-align: right; }
+.table td.tinytable_css_tdq8dwjrdppdz59lbdyn, .table th.tinytable_css_tdq8dwjrdppdz59lbdyn {    text-align: right; }
+.table td.tinytable_css_1ubeyzkl7uv1wtm5o4e1, .table th.tinytable_css_1ubeyzkl7uv1wtm5o4e1 {    text-align: right; }
+.table td.tinytable_css_9to5d6s51h9n10fxqff6, .table th.tinytable_css_9to5d6s51h9n10fxqff6 {    text-align: right; }
+.table td.tinytable_css_gc6mu8ksu1xgfb4uaxnz, .table th.tinytable_css_gc6mu8ksu1xgfb4uaxnz {    text-align: right; }
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     </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_ulh2ykegwv72f064kn1v" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_htibnfzqgnwsx37vn4fs" 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">
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-        var table = document.getElementById("tinytable_ulh2ykegwv72f064kn1v");
+      function styleCell_tinytable_2zw62cuxrgfqgztwj8hz(i, j, css_id) {
+        var table = document.getElementById("tinytable_htibnfzqgnwsx37vn4fs");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_ulh2ykegwv72f064kn1v');
+        var table = document.getElementById('tinytable_htibnfzqgnwsx37vn4fs');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -835,8 +835,8 @@ <h4 class="anchored">
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_lop34tmk1pvojfablen0(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_ulh2ykegwv72f064kn1v");
+      function spanCell_tinytable_2zw62cuxrgfqgztwj8hz(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_htibnfzqgnwsx37vn4fs");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -863,113 +863,113 @@ <h4 class="anchored">
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     </script>
 </div>
 </div>
diff --git a/man/datasummary_correlation.html b/man/datasummary_correlation.html
index b330e3146..983c48645 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_grzw8xi4ix63dmd78n0p</title>
+<title>tinytable_6uos5z8sgk9bsnoqyd87</title>
 <style>
-.table td.tinytable_css_i0q77tkjxz4qyevkxown, .table th.tinytable_css_i0q77tkjxz4qyevkxown {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_udz6a37hg1bn3c3b63se, .table th.tinytable_css_udz6a37hg1bn3c3b63se {    text-align: left; }
-.table td.tinytable_css_qe3s1m6fv7dam7f2vmla, .table th.tinytable_css_qe3s1m6fv7dam7f2vmla {    text-align: right; }
-.table td.tinytable_css_lwi5vt6a0qpi9fickwnq, .table th.tinytable_css_lwi5vt6a0qpi9fickwnq {    text-align: right; }
+.table td.tinytable_css_p2xqy6eiue6xc2bntcb4, .table th.tinytable_css_p2xqy6eiue6xc2bntcb4 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_fnh918topp8xmlcwqa1w, .table th.tinytable_css_fnh918topp8xmlcwqa1w {    text-align: left; }
+.table td.tinytable_css_g0zbcrwgyqqrgyhhjmlz, .table th.tinytable_css_g0zbcrwgyqqrgyhhjmlz {    text-align: right; }
+.table td.tinytable_css_65fgxm0n8hoptiu4qtb6, .table th.tinytable_css_65fgxm0n8hoptiu4qtb6 {    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 = {
@@ -666,7 +666,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_grzw8xi4ix63dmd78n0p" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_6uos5z8sgk9bsnoqyd87" 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">
 </div>
 
     <script>
-      function styleCell_tinytable_oljnyb6jbcz5i3vf17ut(i, j, css_id) {
-        var table = document.getElementById("tinytable_grzw8xi4ix63dmd78n0p");
+      function styleCell_tinytable_rfdh86ouvz8vahcjt4s4(i, j, css_id) {
+        var table = document.getElementById("tinytable_6uos5z8sgk9bsnoqyd87");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_grzw8xi4ix63dmd78n0p');
+        var table = document.getElementById('tinytable_6uos5z8sgk9bsnoqyd87');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -701,8 +701,8 @@ <h4 class="anchored">
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_oljnyb6jbcz5i3vf17ut(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_grzw8xi4ix63dmd78n0p");
+      function spanCell_tinytable_rfdh86ouvz8vahcjt4s4(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_6uos5z8sgk9bsnoqyd87");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -729,18 +729,18 @@ <h4 class="anchored">
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(0, 0, 'tinytable_css_i0q77tkjxz4qyevkxown') })
-window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(0, 1, 'tinytable_css_i0q77tkjxz4qyevkxown') })
-window.addEventListener('load', function () { styleCell_tinytable_oljnyb6jbcz5i3vf17ut(0, 2, 'tinytable_css_i0q77tkjxz4qyevkxown') })
-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') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(0, 0, 'tinytable_css_p2xqy6eiue6xc2bntcb4') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(0, 1, 'tinytable_css_p2xqy6eiue6xc2bntcb4') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(0, 2, 'tinytable_css_p2xqy6eiue6xc2bntcb4') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(0, 0, 'tinytable_css_fnh918topp8xmlcwqa1w') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(1, 0, 'tinytable_css_fnh918topp8xmlcwqa1w') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(2, 0, 'tinytable_css_fnh918topp8xmlcwqa1w') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(0, 1, 'tinytable_css_g0zbcrwgyqqrgyhhjmlz') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(1, 1, 'tinytable_css_g0zbcrwgyqqrgyhhjmlz') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(2, 1, 'tinytable_css_g0zbcrwgyqqrgyhhjmlz') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(0, 2, 'tinytable_css_65fgxm0n8hoptiu4qtb6') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(1, 2, 'tinytable_css_65fgxm0n8hoptiu4qtb6') })
+window.addEventListener('load', function () { styleCell_tinytable_rfdh86ouvz8vahcjt4s4(2, 2, 'tinytable_css_65fgxm0n8hoptiu4qtb6') })
     </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_5ox0wtvehi0xjlxiovka</title>
+<title>tinytable_lirm7rmbmb1bhm3csgcq</title>
 <style>
-.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; }
+.table td.tinytable_css_kg9jyxjv227hcwbds7k0, .table th.tinytable_css_kg9jyxjv227hcwbds7k0 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_6003y6cbejtjvz50w2fk, .table th.tinytable_css_6003y6cbejtjvz50w2fk {    text-align: left; }
+.table td.tinytable_css_mirau76gxq7xjptg4q2q, .table th.tinytable_css_mirau76gxq7xjptg4q2q {    text-align: right; }
+.table td.tinytable_css_xpyk3b6gpl1niu5jjbl9, .table th.tinytable_css_xpyk3b6gpl1niu5jjbl9 {    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_5ox0wtvehi0xjlxiovka" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_lirm7rmbmb1bhm3csgcq" 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_c3w4ikxy2qliuhuhiiib(i, j, css_id) {
-        var table = document.getElementById("tinytable_5ox0wtvehi0xjlxiovka");
+      function styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(i, j, css_id) {
+        var table = document.getElementById("tinytable_lirm7rmbmb1bhm3csgcq");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_5ox0wtvehi0xjlxiovka');
+        var table = document.getElementById('tinytable_lirm7rmbmb1bhm3csgcq');
         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_c3w4ikxy2qliuhuhiiib(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_5ox0wtvehi0xjlxiovka");
+      function spanCell_tinytable_qj0v1wfprp3ofbdg3i3f(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_lirm7rmbmb1bhm3csgcq");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(0, 0, 'tinytable_css_kg9jyxjv227hcwbds7k0') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(0, 1, 'tinytable_css_kg9jyxjv227hcwbds7k0') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(0, 2, 'tinytable_css_kg9jyxjv227hcwbds7k0') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(0, 0, 'tinytable_css_6003y6cbejtjvz50w2fk') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(1, 0, 'tinytable_css_6003y6cbejtjvz50w2fk') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(2, 0, 'tinytable_css_6003y6cbejtjvz50w2fk') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(0, 1, 'tinytable_css_mirau76gxq7xjptg4q2q') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(1, 1, 'tinytable_css_mirau76gxq7xjptg4q2q') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(2, 1, 'tinytable_css_mirau76gxq7xjptg4q2q') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(0, 2, 'tinytable_css_xpyk3b6gpl1niu5jjbl9') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(1, 2, 'tinytable_css_xpyk3b6gpl1niu5jjbl9') })
+window.addEventListener('load', function () { styleCell_tinytable_qj0v1wfprp3ofbdg3i3f(2, 2, 'tinytable_css_xpyk3b6gpl1niu5jjbl9') })
     </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_xqd02z3d30gyj47o4kgz</title>
+<title>tinytable_g1h47nvfzs3b4ydipnh2</title>
 <style>
-.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; }
+.table td.tinytable_css_atrvtgn94qepapibnkbq, .table th.tinytable_css_atrvtgn94qepapibnkbq {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_499tzruhgym796kji3jq, .table th.tinytable_css_499tzruhgym796kji3jq {    text-align: left; }
+.table td.tinytable_css_zsljs7jefb9atgw2bdeu, .table th.tinytable_css_zsljs7jefb9atgw2bdeu {    text-align: right; }
+.table td.tinytable_css_am8tzm2jrjccfg6gy8b5, .table th.tinytable_css_am8tzm2jrjccfg6gy8b5 {    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_xqd02z3d30gyj47o4kgz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_g1h47nvfzs3b4ydipnh2" 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_z5dz1c4ib2hfpuootvx4(i, j, css_id) {
-        var table = document.getElementById("tinytable_xqd02z3d30gyj47o4kgz");
+      function styleCell_tinytable_yqhmt2vzdflynkelfed0(i, j, css_id) {
+        var table = document.getElementById("tinytable_g1h47nvfzs3b4ydipnh2");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_xqd02z3d30gyj47o4kgz');
+        var table = document.getElementById('tinytable_g1h47nvfzs3b4ydipnh2');
         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_z5dz1c4ib2hfpuootvx4(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_xqd02z3d30gyj47o4kgz");
+      function spanCell_tinytable_yqhmt2vzdflynkelfed0(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_g1h47nvfzs3b4ydipnh2");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(0, 0, 'tinytable_css_atrvtgn94qepapibnkbq') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(0, 1, 'tinytable_css_atrvtgn94qepapibnkbq') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(0, 2, 'tinytable_css_atrvtgn94qepapibnkbq') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(0, 0, 'tinytable_css_499tzruhgym796kji3jq') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(1, 0, 'tinytable_css_499tzruhgym796kji3jq') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(2, 0, 'tinytable_css_499tzruhgym796kji3jq') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(0, 1, 'tinytable_css_zsljs7jefb9atgw2bdeu') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(1, 1, 'tinytable_css_zsljs7jefb9atgw2bdeu') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(2, 1, 'tinytable_css_zsljs7jefb9atgw2bdeu') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(0, 2, 'tinytable_css_am8tzm2jrjccfg6gy8b5') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(1, 2, 'tinytable_css_am8tzm2jrjccfg6gy8b5') })
+window.addEventListener('load', function () { styleCell_tinytable_yqhmt2vzdflynkelfed0(2, 2, 'tinytable_css_am8tzm2jrjccfg6gy8b5') })
     </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_2olu6wk3h3qqthxs8g0z</title>
+<title>tinytable_akw46qppai3kvnvxdcc9</title>
 <style>
-.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; }
+.table td.tinytable_css_kxsmfmwl7y6tnhdjkkhv, .table th.tinytable_css_kxsmfmwl7y6tnhdjkkhv {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_n717anabh0kzfslcv3b4, .table th.tinytable_css_n717anabh0kzfslcv3b4 {    text-align: left; }
+.table td.tinytable_css_7vz0udohidr95cn2czbi, .table th.tinytable_css_7vz0udohidr95cn2czbi {    text-align: right; }
+.table td.tinytable_css_h10qfiig9nd0sdbu9zdp, .table th.tinytable_css_h10qfiig9nd0sdbu9zdp {    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_2olu6wk3h3qqthxs8g0z" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_akw46qppai3kvnvxdcc9" 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_jz7orhtfuuobo48whm3x(i, j, css_id) {
-        var table = document.getElementById("tinytable_2olu6wk3h3qqthxs8g0z");
+      function styleCell_tinytable_gexpa95kk8p8hk4u6rm2(i, j, css_id) {
+        var table = document.getElementById("tinytable_akw46qppai3kvnvxdcc9");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_2olu6wk3h3qqthxs8g0z');
+        var table = document.getElementById('tinytable_akw46qppai3kvnvxdcc9');
         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_jz7orhtfuuobo48whm3x(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_2olu6wk3h3qqthxs8g0z");
+      function spanCell_tinytable_gexpa95kk8p8hk4u6rm2(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_akw46qppai3kvnvxdcc9");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(0, 0, 'tinytable_css_kxsmfmwl7y6tnhdjkkhv') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(0, 1, 'tinytable_css_kxsmfmwl7y6tnhdjkkhv') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(0, 2, 'tinytable_css_kxsmfmwl7y6tnhdjkkhv') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(0, 0, 'tinytable_css_n717anabh0kzfslcv3b4') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(1, 0, 'tinytable_css_n717anabh0kzfslcv3b4') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(2, 0, 'tinytable_css_n717anabh0kzfslcv3b4') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(0, 1, 'tinytable_css_7vz0udohidr95cn2czbi') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(1, 1, 'tinytable_css_7vz0udohidr95cn2czbi') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(2, 1, 'tinytable_css_7vz0udohidr95cn2czbi') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(0, 2, 'tinytable_css_h10qfiig9nd0sdbu9zdp') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(1, 2, 'tinytable_css_h10qfiig9nd0sdbu9zdp') })
+window.addEventListener('load', function () { styleCell_tinytable_gexpa95kk8p8hk4u6rm2(2, 2, 'tinytable_css_h10qfiig9nd0sdbu9zdp') })
     </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_unbyis56d7465brso0gh</title>
+<title>tinytable_roj4xo4qo0ukg7h53ryb</title>
 <style>
-.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; }
+.table td.tinytable_css_u73tqnnlygr66gtqk975, .table th.tinytable_css_u73tqnnlygr66gtqk975 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_kd10dbmwvi3024pvsu3s, .table th.tinytable_css_kd10dbmwvi3024pvsu3s {    text-align: left; }
+.table td.tinytable_css_b5swhcbnr8ie3sg8c1bv, .table th.tinytable_css_b5swhcbnr8ie3sg8c1bv {    text-align: right; }
+.table td.tinytable_css_48j1890c8zymhycp50nm, .table th.tinytable_css_48j1890c8zymhycp50nm {    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_unbyis56d7465brso0gh" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_roj4xo4qo0ukg7h53ryb" 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_4pb9eopwj3vxxcks9s4r(i, j, css_id) {
-        var table = document.getElementById("tinytable_unbyis56d7465brso0gh");
+      function styleCell_tinytable_mqnde5pq4llguizz90z0(i, j, css_id) {
+        var table = document.getElementById("tinytable_roj4xo4qo0ukg7h53ryb");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_unbyis56d7465brso0gh');
+        var table = document.getElementById('tinytable_roj4xo4qo0ukg7h53ryb');
         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_4pb9eopwj3vxxcks9s4r(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_unbyis56d7465brso0gh");
+      function spanCell_tinytable_mqnde5pq4llguizz90z0(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_roj4xo4qo0ukg7h53ryb");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(0, 0, 'tinytable_css_u73tqnnlygr66gtqk975') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(0, 1, 'tinytable_css_u73tqnnlygr66gtqk975') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(0, 2, 'tinytable_css_u73tqnnlygr66gtqk975') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(0, 0, 'tinytable_css_kd10dbmwvi3024pvsu3s') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(1, 0, 'tinytable_css_kd10dbmwvi3024pvsu3s') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(2, 0, 'tinytable_css_kd10dbmwvi3024pvsu3s') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(0, 1, 'tinytable_css_b5swhcbnr8ie3sg8c1bv') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(1, 1, 'tinytable_css_b5swhcbnr8ie3sg8c1bv') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(2, 1, 'tinytable_css_b5swhcbnr8ie3sg8c1bv') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(0, 2, 'tinytable_css_48j1890c8zymhycp50nm') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(1, 2, 'tinytable_css_48j1890c8zymhycp50nm') })
+window.addEventListener('load', function () { styleCell_tinytable_mqnde5pq4llguizz90z0(2, 2, 'tinytable_css_48j1890c8zymhycp50nm') })
     </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_290nz6nezudmuuclwueb</title>
+<title>tinytable_dtwgcvpkx4t7hz2i19th</title>
 <style>
-.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; }
+.table td.tinytable_css_2ckt1c1ceae1xs0pv4n4, .table th.tinytable_css_2ckt1c1ceae1xs0pv4n4 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_vpzpp7qdjvx18efbcuaz, .table th.tinytable_css_vpzpp7qdjvx18efbcuaz {    text-align: left; }
+.table td.tinytable_css_6i4qlu7sr36o38yez62y, .table th.tinytable_css_6i4qlu7sr36o38yez62y {    text-align: right; }
+.table td.tinytable_css_fo0osioaqzppir7x7lw9, .table th.tinytable_css_fo0osioaqzppir7x7lw9 {    text-align: right; }
+.table td.tinytable_css_lxffc8m47yx57lbjzdvl, .table th.tinytable_css_lxffc8m47yx57lbjzdvl {    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_290nz6nezudmuuclwueb" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_dtwgcvpkx4t7hz2i19th" 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_dax3yx6eorf8726dd67o(i, j, css_id) {
-        var table = document.getElementById("tinytable_290nz6nezudmuuclwueb");
+      function styleCell_tinytable_3we3ud66jstrqbgd8eox(i, j, css_id) {
+        var table = document.getElementById("tinytable_dtwgcvpkx4t7hz2i19th");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_290nz6nezudmuuclwueb');
+        var table = document.getElementById('tinytable_dtwgcvpkx4t7hz2i19th');
         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_dax3yx6eorf8726dd67o(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_290nz6nezudmuuclwueb");
+      function spanCell_tinytable_3we3ud66jstrqbgd8eox(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_dtwgcvpkx4t7hz2i19th");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 0, 'tinytable_css_2ckt1c1ceae1xs0pv4n4') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 1, 'tinytable_css_2ckt1c1ceae1xs0pv4n4') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 2, 'tinytable_css_2ckt1c1ceae1xs0pv4n4') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 3, 'tinytable_css_2ckt1c1ceae1xs0pv4n4') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 0, 'tinytable_css_vpzpp7qdjvx18efbcuaz') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(1, 0, 'tinytable_css_vpzpp7qdjvx18efbcuaz') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(2, 0, 'tinytable_css_vpzpp7qdjvx18efbcuaz') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(3, 0, 'tinytable_css_vpzpp7qdjvx18efbcuaz') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 1, 'tinytable_css_6i4qlu7sr36o38yez62y') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(1, 1, 'tinytable_css_6i4qlu7sr36o38yez62y') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(2, 1, 'tinytable_css_6i4qlu7sr36o38yez62y') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(3, 1, 'tinytable_css_6i4qlu7sr36o38yez62y') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 2, 'tinytable_css_fo0osioaqzppir7x7lw9') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(1, 2, 'tinytable_css_fo0osioaqzppir7x7lw9') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(2, 2, 'tinytable_css_fo0osioaqzppir7x7lw9') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(3, 2, 'tinytable_css_fo0osioaqzppir7x7lw9') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(0, 3, 'tinytable_css_lxffc8m47yx57lbjzdvl') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(1, 3, 'tinytable_css_lxffc8m47yx57lbjzdvl') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(2, 3, 'tinytable_css_lxffc8m47yx57lbjzdvl') })
+window.addEventListener('load', function () { styleCell_tinytable_3we3ud66jstrqbgd8eox(3, 3, 'tinytable_css_lxffc8m47yx57lbjzdvl') })
     </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_mq9s8yxqc31x4l6kzddz</title>
+<title>tinytable_aeb5nr6svrb95kerwvmv</title>
 <style>
-.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; }
+.table td.tinytable_css_z76uhqfey3kktgb1cv3f, .table th.tinytable_css_z76uhqfey3kktgb1cv3f {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_c25ro9rtbuacunsymuj4, .table th.tinytable_css_c25ro9rtbuacunsymuj4 {    text-align: left; }
+.table td.tinytable_css_zl2k0e8cdkm5airje3zy, .table th.tinytable_css_zl2k0e8cdkm5airje3zy {    text-align: right; }
+.table td.tinytable_css_xycnd8ethvffnp22czjz, .table th.tinytable_css_xycnd8ethvffnp22czjz {    text-align: right; }
+.table td.tinytable_css_h5sunota9k9hpw5bmr4o, .table th.tinytable_css_h5sunota9k9hpw5bmr4o {    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_mq9s8yxqc31x4l6kzddz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_aeb5nr6svrb95kerwvmv" 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_y8d6rfprennbmjp9rgpg(i, j, css_id) {
-        var table = document.getElementById("tinytable_mq9s8yxqc31x4l6kzddz");
+      function styleCell_tinytable_hdawtzilubaro1ksy734(i, j, css_id) {
+        var table = document.getElementById("tinytable_aeb5nr6svrb95kerwvmv");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_mq9s8yxqc31x4l6kzddz');
+        var table = document.getElementById('tinytable_aeb5nr6svrb95kerwvmv');
         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_y8d6rfprennbmjp9rgpg(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_mq9s8yxqc31x4l6kzddz");
+      function spanCell_tinytable_hdawtzilubaro1ksy734(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_aeb5nr6svrb95kerwvmv");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 0, 'tinytable_css_z76uhqfey3kktgb1cv3f') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 1, 'tinytable_css_z76uhqfey3kktgb1cv3f') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 2, 'tinytable_css_z76uhqfey3kktgb1cv3f') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 3, 'tinytable_css_z76uhqfey3kktgb1cv3f') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 0, 'tinytable_css_c25ro9rtbuacunsymuj4') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(1, 0, 'tinytable_css_c25ro9rtbuacunsymuj4') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(2, 0, 'tinytable_css_c25ro9rtbuacunsymuj4') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(3, 0, 'tinytable_css_c25ro9rtbuacunsymuj4') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 1, 'tinytable_css_zl2k0e8cdkm5airje3zy') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(1, 1, 'tinytable_css_zl2k0e8cdkm5airje3zy') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(2, 1, 'tinytable_css_zl2k0e8cdkm5airje3zy') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(3, 1, 'tinytable_css_zl2k0e8cdkm5airje3zy') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 2, 'tinytable_css_xycnd8ethvffnp22czjz') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(1, 2, 'tinytable_css_xycnd8ethvffnp22czjz') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(2, 2, 'tinytable_css_xycnd8ethvffnp22czjz') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(3, 2, 'tinytable_css_xycnd8ethvffnp22czjz') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(0, 3, 'tinytable_css_h5sunota9k9hpw5bmr4o') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(1, 3, 'tinytable_css_h5sunota9k9hpw5bmr4o') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(2, 3, 'tinytable_css_h5sunota9k9hpw5bmr4o') })
+window.addEventListener('load', function () { styleCell_tinytable_hdawtzilubaro1ksy734(3, 3, 'tinytable_css_h5sunota9k9hpw5bmr4o') })
     </script>
 </div>
 </div>
diff --git a/man/datasummary_correlation_format.html b/man/datasummary_correlation_format.html
index 6c02b81d8..d1e9267ba 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_392nnoahgseq58zawjya</title>
+<title>tinytable_3kc2pfl0q9hno0z8edkj</title>
 <style>
-.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; }
+.table td.tinytable_css_4bhvj3fno98vasc8q08e, .table th.tinytable_css_4bhvj3fno98vasc8q08e {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_rk689zaix88t7hha8c9g, .table th.tinytable_css_rk689zaix88t7hha8c9g {    text-align: left; }
+.table td.tinytable_css_n50p1slioactaqkev8y4, .table th.tinytable_css_n50p1slioactaqkev8y4 {    text-align: right; }
+.table td.tinytable_css_0ruzcd9wuuk9tqsx2hfi, .table th.tinytable_css_0ruzcd9wuuk9tqsx2hfi {    text-align: right; }
+.table td.tinytable_css_jv963s1tg8tu7qkjv414, .table th.tinytable_css_jv963s1tg8tu7qkjv414 {    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_392nnoahgseq58zawjya" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_3kc2pfl0q9hno0z8edkj" 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_wt317nyi4k2unr4jplgs(i, j, css_id) {
-        var table = document.getElementById("tinytable_392nnoahgseq58zawjya");
+      function styleCell_tinytable_ie9t9x56ayqu8on3934h(i, j, css_id) {
+        var table = document.getElementById("tinytable_3kc2pfl0q9hno0z8edkj");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_392nnoahgseq58zawjya');
+        var table = document.getElementById('tinytable_3kc2pfl0q9hno0z8edkj');
         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_wt317nyi4k2unr4jplgs(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_392nnoahgseq58zawjya");
+      function spanCell_tinytable_ie9t9x56ayqu8on3934h(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_3kc2pfl0q9hno0z8edkj");
         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_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') })
-window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(3, 2, 'tinytable_css_dqbrchbx0baumic82mxo') })
-window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(0, 3, 'tinytable_css_v7xt5savhf1efcaumj4v') })
-window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(1, 3, 'tinytable_css_v7xt5savhf1efcaumj4v') })
-window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(2, 3, 'tinytable_css_v7xt5savhf1efcaumj4v') })
-window.addEventListener('load', function () { styleCell_tinytable_wt317nyi4k2unr4jplgs(3, 3, 'tinytable_css_v7xt5savhf1efcaumj4v') })
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diff --git a/man/datasummary_crosstab.html b/man/datasummary_crosstab.html
index 3ad9361bd..68f50388c 100644
--- a/man/datasummary_crosstab.html
+++ b/man/datasummary_crosstab.html
@@ -664,15 +664,15 @@ <h4 class="anchored">
   
     <meta charset="UTF-8">
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-<title>tinytable_lf0vwp3ves3ldpvhgfhj</title>
+<title>tinytable_byny0s6ew83pn9r1jmnp</title>
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+.table td.tinytable_css_u98rkamscx5vkl6h99vk, .table th.tinytable_css_u98rkamscx5vkl6h99vk {    text-align: left; }
+.table td.tinytable_css_slmpdm6m9gkzy5bku4bi, .table th.tinytable_css_slmpdm6m9gkzy5bku4bi {    text-align: left; }
+.table td.tinytable_css_xo05oxxpxjn6xlljm18t, .table th.tinytable_css_xo05oxxpxjn6xlljm18t {    text-align: right; }
+.table td.tinytable_css_7l82faz9m77r1luka6qw, .table th.tinytable_css_7l82faz9m77r1luka6qw {    text-align: right; }
+.table td.tinytable_css_mzkl0nevknzu1l7nk5w5, .table th.tinytable_css_mzkl0nevknzu1l7nk5w5 {    text-align: right; }
+.table td.tinytable_css_hiy0z8om3ebmfz3gcum9, .table th.tinytable_css_hiy0z8om3ebmfz3gcum9 {    text-align: right; }
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-      <table class="table table-borderless" id="tinytable_lf0vwp3ves3ldpvhgfhj" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_byny0s6ew83pn9r1jmnp" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
 <th scope="col">cyl</th>
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@@ -763,12 +763,12 @@ <h4 class="anchored">
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-        var table = document.getElementById("tinytable_lf0vwp3ves3ldpvhgfhj");
+      function styleCell_tinytable_f2x6lhbet7ry4zi80yoa(i, j, css_id) {
+        var table = document.getElementById("tinytable_byny0s6ew83pn9r1jmnp");
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       function insertSpanRow(i, colspan, content) {
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 </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_663m5e7737wydockz4f6</title>
+<title>tinytable_9y0mx65dvv7t2lrbz8pl</title>
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-      <table class="table table-borderless" id="tinytable_663m5e7737wydockz4f6" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_9y0mx65dvv7t2lrbz8pl" 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">
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-      function styleCell_tinytable_7w86ierjqmrh93xko8kc(i, j, css_id) {
-        var table = document.getElementById("tinytable_663m5e7737wydockz4f6");
+      function styleCell_tinytable_clpx32puq7vl52iddral(i, j, css_id) {
+        var table = document.getElementById("tinytable_9y0mx65dvv7t2lrbz8pl");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_663m5e7737wydockz4f6');
+        var table = document.getElementById('tinytable_9y0mx65dvv7t2lrbz8pl');
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         var newCell = newRow.insertCell(0);
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@@ -941,8 +941,8 @@ <h4 class="anchored">
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-      function spanCell_tinytable_7w86ierjqmrh93xko8kc(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_663m5e7737wydockz4f6");
+      function spanCell_tinytable_clpx32puq7vl52iddral(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_9y0mx65dvv7t2lrbz8pl");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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+window.addEventListener('load', function () { styleCell_tinytable_clpx32puq7vl52iddral(1, 3, 'tinytable_css_f0fdoxy96jojai0lkofz') })
+window.addEventListener('load', function () { styleCell_tinytable_clpx32puq7vl52iddral(2, 3, 'tinytable_css_f0fdoxy96jojai0lkofz') })
+window.addEventListener('load', function () { styleCell_tinytable_clpx32puq7vl52iddral(3, 3, 'tinytable_css_f0fdoxy96jojai0lkofz') })
+window.addEventListener('load', function () { styleCell_tinytable_clpx32puq7vl52iddral(0, 4, 'tinytable_css_sxrxnwr4tt06ckkgnx7q') })
+window.addEventListener('load', function () { styleCell_tinytable_clpx32puq7vl52iddral(1, 4, 'tinytable_css_sxrxnwr4tt06ckkgnx7q') })
+window.addEventListener('load', function () { styleCell_tinytable_clpx32puq7vl52iddral(2, 4, 'tinytable_css_sxrxnwr4tt06ckkgnx7q') })
+window.addEventListener('load', function () { styleCell_tinytable_clpx32puq7vl52iddral(3, 4, 'tinytable_css_sxrxnwr4tt06ckkgnx7q') })
     </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_6aeigmuvtiy8r06xetaa</title>
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+.table td.tinytable_css_87gmroj9tks2e7eas9en, .table th.tinytable_css_87gmroj9tks2e7eas9en {    text-align: right; }
+.table td.tinytable_css_s221n71cx3aqsufovkko, .table th.tinytable_css_s221n71cx3aqsufovkko {    text-align: right; }
+.table td.tinytable_css_3vr5xpcf7wigvf18ltl6, .table th.tinytable_css_3vr5xpcf7wigvf18ltl6 {    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 = {
@@ -1025,7 +1025,7 @@ <h4 class="anchored">
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_6aeigmuvtiy8r06xetaa" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_h7nqis884hosurzbrir4" 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_5gzph0z6zm87cnomtyd7(i, j, css_id) {
-        var table = document.getElementById("tinytable_6aeigmuvtiy8r06xetaa");
+      function styleCell_tinytable_8vfgwevhwhsk89qip01g(i, j, css_id) {
+        var table = document.getElementById("tinytable_h7nqis884hosurzbrir4");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_6aeigmuvtiy8r06xetaa');
+        var table = document.getElementById('tinytable_h7nqis884hosurzbrir4');
         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_5gzph0z6zm87cnomtyd7(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_6aeigmuvtiy8r06xetaa");
+      function spanCell_tinytable_8vfgwevhwhsk89qip01g(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_h7nqis884hosurzbrir4");
         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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+window.addEventListener('load', function () { styleCell_tinytable_8vfgwevhwhsk89qip01g(14, 6, 'tinytable_css_3vr5xpcf7wigvf18ltl6') })
     </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_a07ln2dlnp2nawgi6t0p</title>
+<title>tinytable_th6htlsfxr7lsk6i4if1</title>
 <style>
-.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; }
+.table td.tinytable_css_8m8q840ukxksrmhee6zp, .table th.tinytable_css_8m8q840ukxksrmhee6zp {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_mftdcfbxtuo2v5okadw7, .table th.tinytable_css_mftdcfbxtuo2v5okadw7 {    text-align: left; }
+.table td.tinytable_css_tdduh1kvph7bk7x5j7be, .table th.tinytable_css_tdduh1kvph7bk7x5j7be {    text-align: left; }
+.table td.tinytable_css_36wkacj3urcqawuvv8dj, .table th.tinytable_css_36wkacj3urcqawuvv8dj {    text-align: right; }
+.table td.tinytable_css_wf90y906zidfwleavtt3, .table th.tinytable_css_wf90y906zidfwleavtt3 {    text-align: right; }
+.table td.tinytable_css_lq883k9vlv2c513u9977, .table th.tinytable_css_lq883k9vlv2c513u9977 {    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_a07ln2dlnp2nawgi6t0p" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_th6htlsfxr7lsk6i4if1" 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_k0th350euycljdtznfa0(i, j, css_id) {
-        var table = document.getElementById("tinytable_a07ln2dlnp2nawgi6t0p");
+      function styleCell_tinytable_91wispufxhpaeg9lk62i(i, j, css_id) {
+        var table = document.getElementById("tinytable_th6htlsfxr7lsk6i4if1");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_a07ln2dlnp2nawgi6t0p');
+        var table = document.getElementById('tinytable_th6htlsfxr7lsk6i4if1');
         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_k0th350euycljdtznfa0(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_a07ln2dlnp2nawgi6t0p");
+      function spanCell_tinytable_91wispufxhpaeg9lk62i(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_th6htlsfxr7lsk6i4if1");
         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;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_k0th350euycljdtznfa0(0, 0, 'tinytable_css_ip1zaadraqojfzfggxrj') })
-window.addEventListener('load', function () { styleCell_tinytable_k0th350euycljdtznfa0(0, 1, 'tinytable_css_ip1zaadraqojfzfggxrj') })
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     </script>
 </div>
 </div>
diff --git a/man/dvnames.html b/man/dvnames.html
index e25979847..911502b37 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_u2hh31udbdeemx98iz83</title>
+<title>tinytable_772j9f8vorr2x1vfz6mc</title>
 <style>
-.table td.tinytable_css_pq24fzy3na1z44sg8own, .table th.tinytable_css_pq24fzy3na1z44sg8own {    border-bottom: solid 0.1em #d3d8dc; }
-.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; }
-.table td.tinytable_css_9kcgml1pnigy37pjydf7, .table th.tinytable_css_9kcgml1pnigy37pjydf7 {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_wv4xwov9y89opu2va992, .table th.tinytable_css_wv4xwov9y89opu2va992 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_dp76jq0t7suhxz7u213e, .table th.tinytable_css_dp76jq0t7suhxz7u213e {    text-align: left; }
+.table td.tinytable_css_hu0te6veygfg16jsw93d, .table th.tinytable_css_hu0te6veygfg16jsw93d {    text-align: center; }
+.table td.tinytable_css_ju5sgfnj1eecmr6caklp, .table th.tinytable_css_ju5sgfnj1eecmr6caklp {    text-align: center; }
+.table td.tinytable_css_jxww2bgadqt8tlkg9tds, .table th.tinytable_css_jxww2bgadqt8tlkg9tds {    border-bottom: solid 0.05em black; }
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     MathJax = {
@@ -446,7 +446,7 @@
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     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_u2hh31udbdeemx98iz83" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_772j9f8vorr2x1vfz6mc" 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>
@@ -528,12 +528,12 @@
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-      function styleCell_tinytable_yc3kxc0z2u95wvt6dapt(i, j, css_id) {
-        var table = document.getElementById("tinytable_u2hh31udbdeemx98iz83");
+      function styleCell_tinytable_lafr0ific03tpbjdregt(i, j, css_id) {
+        var table = document.getElementById("tinytable_772j9f8vorr2x1vfz6mc");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_u2hh31udbdeemx98iz83');
+        var table = document.getElementById('tinytable_772j9f8vorr2x1vfz6mc');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -541,8 +541,8 @@
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
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-      function spanCell_tinytable_yc3kxc0z2u95wvt6dapt(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_u2hh31udbdeemx98iz83");
+      function spanCell_tinytable_lafr0ific03tpbjdregt(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_772j9f8vorr2x1vfz6mc");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -569,57 +569,57 @@
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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"># 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_f852o222qpbpp0nismmp</title>
+<title>tinytable_rw10sx05a65jh3z1p9vp</title>
 <style>
-.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; }
+.table td.tinytable_css_u8ev1aqrkgj42whfid7b, .table th.tinytable_css_u8ev1aqrkgj42whfid7b {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_racoxdqhu9dztotgzboc, .table th.tinytable_css_racoxdqhu9dztotgzboc {    text-align: left; }
+.table td.tinytable_css_rhhkdwusoawi09pzn3ei, .table th.tinytable_css_rhhkdwusoawi09pzn3ei {    text-align: center; }
+.table td.tinytable_css_22thyogljtnjyab4giey, .table th.tinytable_css_22thyogljtnjyab4giey {    text-align: center; }
+.table td.tinytable_css_odujg1j4n3g4j0mtdyau, .table th.tinytable_css_odujg1j4n3g4j0mtdyau {    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_f852o222qpbpp0nismmp" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_rw10sx05a65jh3z1p9vp" 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_7g6ktv5vzso0u1iarh20(i, j, css_id) {
-        var table = document.getElementById("tinytable_f852o222qpbpp0nismmp");
+      function styleCell_tinytable_pp19l9nk16k7iy196g6k(i, j, css_id) {
+        var table = document.getElementById("tinytable_rw10sx05a65jh3z1p9vp");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_f852o222qpbpp0nismmp');
+        var table = document.getElementById('tinytable_rw10sx05a65jh3z1p9vp');
         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_7g6ktv5vzso0u1iarh20(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_f852o222qpbpp0nismmp");
+      function spanCell_tinytable_pp19l9nk16k7iy196g6k(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_rw10sx05a65jh3z1p9vp");
         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 f4688a610..96e760643 100644
--- a/search.json
+++ b/search.json
@@ -26,7 +26,7 @@
     "href": "vignettes/get_started.html",
     "title": "Getting started",
     "section": "",
-    "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\")",
+    "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_fu41m92s1ycg9slfxym7\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_jix3v2s3wx24jey8dms4\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_6p6juxs5bbgjbyf563kj\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_11gc7k5h1ifepyq47x4q\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_tmbigvd1v3gu1torosoe\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_0oye7fg5i25ioctkqqb4\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\")",
     "crumbs": [
       "Get started",
       "Getting started"
@@ -133,7 +133,7 @@
     "href": "man/datasummary.html",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_s7semkrxgev97l5nkimp\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_m7khicbwak6fux9qxac0\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_5nbmj7vh0iruu3ozufjr\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_ignv475l53xdk408swtw\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_32pkvff7ay2rlgqbbfwy\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_n00bdt35ylfp8306esnw\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_ricso3pa8t0vzuqh3t1d\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_9pat3wi7wv8h2vk57pqg\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_81ezsy5w7a0oyus0zl8x\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_u7t4m9cdy5jzxj3fvtdx\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_m0d5ihrifpqu0szkmfnt\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",
     "crumbs": [
       "Get started",
       "Functions",
@@ -171,7 +171,7 @@
     "href": "man/datasummary_crosstab.html",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_byny0s6ew83pn9r1jmnp\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_9y0mx65dvv7t2lrbz8pl\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_h7nqis884hosurzbrir4\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_th6htlsfxr7lsk6i4if1\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",
     "crumbs": [
       "Get started",
       "Functions",
@@ -183,7 +183,7 @@
     "href": "man/datasummary_crosstab.html#cross-tabulations-for-categorical-variables",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_byny0s6ew83pn9r1jmnp\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_9y0mx65dvv7t2lrbz8pl\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_h7nqis884hosurzbrir4\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_th6htlsfxr7lsk6i4if1\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",
     "crumbs": [
       "Get started",
       "Functions",
@@ -273,7 +273,7 @@
     "href": "man/dvnames.html",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_772j9f8vorr2x1vfz6mc\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_rw10sx05a65jh3z1p9vp\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",
@@ -286,7 +286,7 @@
     "href": "man/dvnames.html#title-models-with-their-dependent-variables",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_772j9f8vorr2x1vfz6mc\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_rw10sx05a65jh3z1p9vp\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",
@@ -323,7 +323,7 @@
     "href": "man/coef_rename.html",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_rbap4rknvkgzumrzimoe\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",
     "crumbs": [
       "Get started",
       "Functions",
@@ -336,7 +336,7 @@
     "href": "man/coef_rename.html#rename-model-terms",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_rbap4rknvkgzumrzimoe\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",
     "crumbs": [
       "Get started",
       "Functions",
@@ -1043,7 +1043,7 @@
     "href": "man/datasummary_correlation_format.html",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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                  .",
+    "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_3kc2pfl0q9hno0z8edkj\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                  .",
     "crumbs": [
       "Get started",
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@@ -1056,7 +1056,7 @@
     "href": "man/datasummary_correlation_format.html#format-the-content-of-a-correlation-table",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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                  .",
+    "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_3kc2pfl0q9hno0z8edkj\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                  .",
     "crumbs": [
       "Get started",
       "Functions",
@@ -1069,7 +1069,7 @@
     "href": "man/datasummary_balance.html",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_htibnfzqgnwsx37vn4fs\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",
     "crumbs": [
       "Get started",
       "Functions",
@@ -1081,7 +1081,7 @@
     "href": "man/datasummary_correlation.html",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_6uos5z8sgk9bsnoqyd87\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_lirm7rmbmb1bhm3csgcq\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_g1h47nvfzs3b4ydipnh2\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_akw46qppai3kvnvxdcc9\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_roj4xo4qo0ukg7h53ryb\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_dtwgcvpkx4t7hz2i19th\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_aeb5nr6svrb95kerwvmv\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",
     "crumbs": [
       "Get started",
       "Functions",
@@ -1093,7 +1093,7 @@
     "href": "man/datasummary_correlation.html#generate-a-correlation-table-for-all-numeric-variables-in-your-dataset.",
     "title": "modelsummary: Data and Model Summaries in R",
     "section": "",
-    "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",
+    "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_6uos5z8sgk9bsnoqyd87\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_lirm7rmbmb1bhm3csgcq\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_g1h47nvfzs3b4ydipnh2\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_akw46qppai3kvnvxdcc9\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_roj4xo4qo0ukg7h53ryb\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_dtwgcvpkx4t7hz2i19th\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_aeb5nr6svrb95kerwvmv\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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     "title": "Extension and Customization",
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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_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",
+    "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_urqnt8gm8dgtlm7xko2e\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_ab02b4lvfzpm7ypayfvc\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_sccr6fhs89g8wlpjbn8w\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_v5tlkoopaevlgnaetdns\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_ua5gn2kfm4t1cyiowe8f\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",
     "crumbs": [
       "Get started",
       "Extension and Customization"
@@ -1166,7 +1166,7 @@
     "href": "vignettes/modelsummary.html",
     "title": "Model Summaries",
     "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_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",
+    "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_85prb6brv9r2i92zsojz\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1177,7 +1177,7 @@
     "href": "vignettes/modelsummary.html#formula",
     "title": "Model Summaries",
     "section": "Formula",
-    "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",
+    "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_gvxx11zwzlmlxe6n01vc\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_era8a74xfo7uu80e5pmz\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_33cqm5etwh6wf9vstami\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_tivra0mxkzdop8f1yv0o\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_csw4aot1onqmeqb2wa7c\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_2fqs6dihbm72q4yk1im4\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1188,7 +1188,7 @@
     "href": "vignettes/modelsummary.html#string-rbind-or-rcollapse-panels-of-models-in-stacked-regression-tables",
     "title": "Model Summaries",
     "section": "String (“rbind” or “rcollapse”): Panels of models in stacked regression tables",
-    "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",
+    "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_kqnnz69un3lcjdwhqysf\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_db6m43uvrfmvm9agdguz\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1199,7 +1199,7 @@
     "href": "vignettes/modelsummary.html#string-cbind-automatic-spanning-column-labels",
     "title": "Model Summaries",
     "section": "String (“cbind”): Automatic spanning column labels",
-    "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",
+    "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_pxtrux1ja6chraeg3sje\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1210,7 +1210,7 @@
     "href": "vignettes/modelsummary.html#renaming-estimates-and-statistics",
     "title": "Model Summaries",
     "section": "Renaming estimates and statistics",
-    "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\")",
+    "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_073hvhj1uymt3tqdoc9q\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_oqucziaa2f5rxye5oddz\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_a02mgv0yq2aqefnf2nxf\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\")",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1221,7 +1221,7 @@
     "href": "vignettes/modelsummary.html#reference-categories",
     "title": "Model Summaries",
     "section": "Reference categories",
-    "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",
+    "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_b7lpy6o8nvigde8rkmkp\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1254,7 +1254,7 @@
     "href": "vignettes/modelsummary.html#quarto",
     "title": "Model Summaries",
     "section": "Quarto",
-    "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 td.tinytable_css_fp2odl38al328h1phmpm, .table th.tinytable_css_fp2odl38al328h1phmpm {    text-align: center; }\n.table td.tinytable_css_btmepwmwd9kej9dtkji6, .table th.tinytable_css_btmepwmwd9kej9dtkji6 {    border-bottom: solid 0.05em black; }\n    &lt;/style&gt;\n    &lt;script src=\"https://polyfill.io/v3/polyfill.min.js?features=es6\"&gt;&lt;/script&gt;\n    &lt;script id=\"MathJax-script\" async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"&gt;&lt;/script&gt;\n    &lt;script&gt;\n    MathJax = {\n      tex: {\n        inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n      },\n      svg: {\n        fontCache: 'global'\n      }\n    };\n    &lt;/script&gt;\n  &lt;/head&gt;\n\n  &lt;body&gt;\n    &lt;div class=\"container\"&gt;\n      &lt;table class=\"table table-borderless\" id=\"tinytable_wd2p3rre3btolfnvj1e1\" style=\"width: auto; margin-left: auto; margin-right: auto;\" data-quarto-disable-processing='true'&gt;\n        &lt;thead&gt;\n        \n              &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_wn0cfg0rrf6tneafdah2(4, 1, 'tinytable_css_btmepwmwd9kej9dtkji6') })\n    &lt;/script&gt;\n\n  &lt;/body&gt;\n\n&lt;/html&gt;\n```\n\n:::\n:::",
+    "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_81hl7auedfh8w7f5qwmh&lt;/title&gt;\n    &lt;style&gt;\n.table td.tinytable_css_2i97j7tjrnvyb873si2p, .table th.tinytable_css_2i97j7tjrnvyb873si2p {    border-bottom: solid 0.1em #d3d8dc; }\n.table td.tinytable_css_1bylhko206yu4pic1doq, .table th.tinytable_css_1bylhko206yu4pic1doq {    text-align: left; }\n.table td.tinytable_css_y9xylka3k21i45qc7wxb, .table th.tinytable_css_y9xylka3k21i45qc7wxb {    text-align: center; }\n.table td.tinytable_css_dr6ybqipd0iv3huqoxdw, .table th.tinytable_css_dr6ybqipd0iv3huqoxdw {    border-bottom: solid 0.05em black; }\n    &lt;/style&gt;\n    &lt;script src=\"https://polyfill.io/v3/polyfill.min.js?features=es6\"&gt;&lt;/script&gt;\n    &lt;script id=\"MathJax-script\" async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"&gt;&lt;/script&gt;\n    &lt;script&gt;\n    MathJax = {\n      tex: {\n        inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n      },\n      svg: {\n        fontCache: 'global'\n      }\n    };\n    &lt;/script&gt;\n  &lt;/head&gt;\n\n  &lt;body&gt;\n    &lt;div class=\"container\"&gt;\n      &lt;table class=\"table table-borderless\" id=\"tinytable_81hl7auedfh8w7f5qwmh\" style=\"width: auto; margin-left: auto; margin-right: auto;\" data-quarto-disable-processing='true'&gt;\n        &lt;thead&gt;\n        \n              &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       &lt;/td&gt;\n                  &lt;td&gt;3.74   &lt;/td&gt;\n                &lt;/tr&gt;\n        &lt;/tbody&gt;\n      &lt;/table&gt;\n    &lt;/div&gt;\n\n    &lt;script&gt;\n      function styleCell_tinytable_e028ka9fujpe5fx6oh6y(i, j, css_id) {\n        var table = document.getElementById(\"tinytable_81hl7auedfh8w7f5qwmh\");\n        table.rows[i].cells[j].classList.add(css_id);\n      }\n      function insertSpanRow(i, colspan, content) {\n        var table = document.getElementById('tinytable_81hl7auedfh8w7f5qwmh');\n        var newRow = table.insertRow(i);\n        var newCell = newRow.insertCell(0);\n        newCell.setAttribute(\"colspan\", colspan);\n        // newCell.innerText = content;\n        // this may be unsafe, but innerText does not interpret &lt;br&gt;\n        newCell.innerHTML = content;\n      }\n      function spanCell_tinytable_e028ka9fujpe5fx6oh6y(i, j, rowspan, colspan) {\n        var table = document.getElementById(\"tinytable_81hl7auedfh8w7f5qwmh\");\n     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})\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(7, 1, 'tinytable_css_y9xylka3k21i45qc7wxb') })\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(8, 1, 'tinytable_css_y9xylka3k21i45qc7wxb') })\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(9, 1, 'tinytable_css_y9xylka3k21i45qc7wxb') })\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(10, 1, 'tinytable_css_y9xylka3k21i45qc7wxb') })\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(11, 1, 'tinytable_css_y9xylka3k21i45qc7wxb') })\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(12, 1, 'tinytable_css_y9xylka3k21i45qc7wxb') })\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(4, 0, 'tinytable_css_dr6ybqipd0iv3huqoxdw') })\nwindow.addEventListener('load', function () { styleCell_tinytable_e028ka9fujpe5fx6oh6y(4, 1, 'tinytable_css_dr6ybqipd0iv3huqoxdw') })\n    &lt;/script&gt;\n\n  &lt;/body&gt;\n\n&lt;/html&gt;\n```\n\n:::\n:::",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1276,7 +1276,7 @@
     "href": "vignettes/modelsummary.html#collapse-control-variables-into-an-indicator",
     "title": "Model Summaries",
     "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\"))",
+    "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_myl0s6bnpg44bqowtle9\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\"))",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1287,7 +1287,7 @@
     "href": "vignettes/modelsummary.html#subgroup-estimation-with-nest_by",
     "title": "Model Summaries",
     "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",
+    "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_0lrpjbx6d6z8avuxxh14\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1298,7 +1298,7 @@
     "href": "vignettes/modelsummary.html#bootstrap",
     "title": "Model Summaries",
     "section": "Bootstrap",
-    "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",
+    "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_k2n4ehsa16320dp7gqma\n\n\n      \n\n \n                (1)\n                (2)\n              \n\n\n(Intercept)\n                  326.362 \n                  283.790 \n                \n\n           \n                  (31.592)\n                  (41.181)\n                \n\nmpg        \n                  -8.991  \n                  -10.225 \n                \n\n           \n                  (1.410) \n                  (2.207) \n                \n\ndrat       \n                          \n                  18.796  \n                \n\n           \n                          \n                  (19.682)\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1309,7 +1309,7 @@
     "href": "vignettes/modelsummary.html#fixest-fixed-effects-and-instrumental-variable-regression",
     "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\")",
+    "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_mlspi5k0y98jwq9glrge\n\n\n      \n\n \n                (1)\n              \n\n\nfit_x_endo_1   \n                  13.669           \n                \n\n               \n                  (39.671)         \n                \n\nfit_x_endo_2   \n                  77.421           \n                \n\n               \n                  (190.021)        \n                \n\nx1             \n                  -17.688          \n                \n\n               \n                  (45.941)         \n                \n\nNum.Obs.       \n                  150              \n                \n\nR2             \n                  -2286.721        \n                \n\nR2 Adj.        \n                  -2366.156        \n                \n\nR2 Within      \n                  -5998.883        \n                \n\nR2 Within Adj. \n                  -6123.881        \n                \n\nAIC            \n                  1540.4           \n                \n\nBIC            \n                  1558.4           \n                \n\nRMSE           \n                  39.47            \n                \n\nStd.Errors     \n                  by: fe           \n                \n\nFE: fe         \n                  X                \n                \n\nWald (x_endo_1)\n                  7.24363130222479 \n                \n\nWald (x_endo_2)\n                  0.858059193776838\n                \n\n\n\n\n    \n\n\n\nrm(\"glance_custom.fixest\")",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1320,7 +1320,7 @@
     "href": "vignettes/modelsummary.html#multiple-imputation",
     "title": "Model Summaries",
     "section": "Multiple imputation",
-    "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",
+    "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_50onqcwlrm5lbajno0z3\n\n\n      \n\n \n                Listwise deletion\n                Mice\n                Amelia\n              \n\n\n(Intercept)\n                  71.717  \n                  73.303  \n                  72.284  \n                \n\n           \n                  (14.112)\n                  (14.664)\n                  (12.809)\n                \n\nLiteracy   \n                  -0.418  \n                  -0.470  \n                  -0.435  \n                \n\n           \n                  (0.221) \n                  (0.228) \n                  (0.194) \n                \n\nCommerce   \n                  -0.269  \n                  -0.266  \n                  -0.297  \n                \n\n           \n                  (0.159) \n                  (0.157) \n                  (0.152) \n                \n\nNum.Obs.   \n                  60      \n                  86      \n                  86      \n                \n\nNum.Imp.   \n                          \n                  5       \n                  5       \n                \n\nR2         \n                  0.068   \n                  0.072   \n                  0.077   \n                \n\nR2 Adj.    \n                  0.035   \n                  0.047   \n                  0.054   \n                \n\nAIC        \n                  561.8   \n                          \n                          \n                \n\nBIC        \n                  570.2   \n                          \n                          \n                \n\nLog.Lik.   \n                  -276.887\n                          \n                          \n                \n\nF          \n                  2.071   \n                          \n                          \n                \n\nRMSE       \n                  24.43",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1375,7 +1375,7 @@
     "href": "vignettes/modelsummary.html#how-can-i-speed-up-modelsummary",
     "title": "Model Summaries",
     "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",
+    "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: 0.002 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: 0.021 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_zd23lfshk9ui5tb2okyn\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",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1397,7 +1397,7 @@
     "href": "vignettes/modelsummary.html#bayesian-models",
     "title": "Model Summaries",
     "section": "Bayesian models",
-    "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",
+    "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) -2.1971590667 0.57768137       0.95 -3.334876772 -1.105943165             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006333653 0.00105191       0.95 -0.001422686  0.002819079             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.6992566587 0.13402411       0.95  0.437680965  0.969048697             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_r1vayi7jelm981qpeb1x\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  -2.197 \n                \n\n           \n                  (0.578)\n                \n\nhp         \n                  0.001  \n                \n\n           \n                  (0.001)\n                \n\ndrat       \n                  0.699  \n                \n\n           \n                  (0.134)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.495  \n                \n\nR2 Adj.    \n                  0.437  \n                \n\nLog.Lik.   \n                  -12.133\n                \n\nELPD       \n                  -15.1  \n                \n\nELPD s.e.  \n                  3.1    \n                \n\nLOOIC      \n                  30.2   \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) -2.2124440913 0.579236863       0.95 -3.334876772 -1.105943165             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006557409 0.001073215       0.95 -0.001422686  0.002819079             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.7008098831 0.136555046       0.95  0.437680965  0.969048697             normal        0.00000  2.33313429              NA        NA      NA\n\nmodelsummary(mod, statistic = \"std.dev\", centrality = \"mean\")\n\n \n\n  \n    \n\ntinytable_89ye4dcynow3zz9elow2\n\n\n      \n\n \n                (1)\n              \n\n\n(Intercept)\n                  -2.212 \n                \n\n           \n                  (0.579)\n                \n\nhp         \n                  0.001  \n                \n\n           \n                  (0.001)\n                \n\ndrat       \n                  0.701  \n                \n\n           \n                  (0.137)\n                \n\nNum.Obs.   \n                  32     \n                \n\nR2         \n                  0.495  \n                \n\nR2 Adj.    \n                  0.437  \n                \n\nLog.Lik.   \n                  -12.133\n                \n\nELPD       \n                  -15.1  \n                \n\nELPD s.e.  \n                  3.1    \n                \n\nLOOIC      \n                  30.2   \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) -2.1971590667 0.57768137       0.95 -3.334876772 -1.105943165 0.9995               0             normal        0.40625  1.24747729              NA        NA      NA\n2          hp  0.0006333653 0.00105191       0.95 -0.001422686  0.002819079 0.7270               1             normal        0.00000  0.01819465              NA        NA      NA\n3        drat  0.6992566587 0.13402411       0.95  0.437680965  0.969048697 1.0000               0             normal        0.00000  2.33313429              NA        NA      NA",
     "crumbs": [
       "Get started",
       "Model Summaries"
@@ -1419,7 +1419,7 @@
     "href": "vignettes/datasummary.html#custom-summary-functions",
     "title": "Data Summaries",
     "section": "Custom summary functions",
-    "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]",
+    "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_ujlh99j6r39njwqr68rb\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_7f6louttieuzdq67awtk\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_v00ecm16vt4dolwhr0ui\n\n\n      \n\n \n                MinMax\n              \n\nflipper_length_mm\n                  [172, 231]",
     "crumbs": [
       "Get started",
       "Data Summaries"
@@ -1430,7 +1430,7 @@
     "href": "vignettes/datasummary.html#concatenating-with",
     "title": "Data Summaries",
     "section": "Concatenating with +\n",
-    "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.",
+    "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_k9caxbs5kadtfjt2qdxa\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_stkwuzlxydvsy4ns2jbh\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.",
     "crumbs": [
       "Get started",
       "Data Summaries"
@@ -1441,7 +1441,7 @@
     "href": "vignettes/datasummary.html#nesting-with",
     "title": "Data Summaries",
     "section": "Nesting with *\n",
-    "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.",
+    "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_tmyrfjvbvpjqcmxkglqr\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_m4yifvil1lpu998ia1a6\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_ey8pqh5qs8j5jvmhicf7\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_sxcetvov6yvb3w2wbjut\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_si3izgn8vcbesjbi6gjk\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_kegb4vo01ajf4bmqqiec\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.",
     "crumbs": [
       "Get started",
       "Data Summaries"
@@ -1452,7 +1452,7 @@
     "href": "vignettes/datasummary.html#renaming-with",
     "title": "Data Summaries",
     "section": "Renaming with =\n",
-    "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",
+    "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_iumsxlta6zg41ityra0j\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_elradgsleqqzq2dnm9z0\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_mk2hpc9z6m2zcgucz5xh\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_n3lurag5cccmmajojyk2\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_ck5sgz7fpby2tzdv1tmf\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",
     "crumbs": [
       "Get started",
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@@ -1463,7 +1463,7 @@
     "href": "vignettes/datasummary.html#counts-and-percentages",
     "title": "Data Summaries",
     "section": "Counts and Percentages",
-    "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.",
+    "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_eq61q01iaatkuz23z13r\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.",
     "crumbs": [
       "Get started",
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@@ -1474,7 +1474,7 @@
     "href": "vignettes/datasummary.html#weighted-percentages",
     "title": "Data Summaries",
     "section": "Weighted percentages",
-    "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.",
+    "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_ugg61a2fcw5fh73ssepo\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_ncj0pge4avt822em8vkz\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.",
     "crumbs": [
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@@ -1485,7 +1485,7 @@
     "href": "vignettes/datasummary.html#custom-percentages",
     "title": "Data Summaries",
     "section": "Custom percentages",
-    "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%)",
+    "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_u6xr06x66j9udy6bdbyj\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%)",
     "crumbs": [
       "Get started",
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@@ -1496,7 +1496,7 @@
     "href": "vignettes/datasummary.html#factor",
     "title": "Data Summaries",
     "section": "Factor",
-    "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",
+    "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_7jiapf7qjcmlxc4hhu1z\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",
     "crumbs": [
       "Get started",
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@@ -1507,7 +1507,7 @@
     "href": "vignettes/datasummary.html#arguments-na.rmtrue",
     "title": "Data Summaries",
     "section": "\nArguments: na.rm=TRUE\n",
-    "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",
+    "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_76pbdi9rf9pge3ij6nba\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_759htqlplz69gmk90hpj\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_veqgpyihij7b0xdstw4v\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",
     "crumbs": [
       "Get started",
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@@ -1518,7 +1518,7 @@
     "href": "vignettes/datasummary.html#arguments-weighted-mean",
     "title": "Data Summaries",
     "section": "\nArguments: Weighted Mean",
-    "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",
+    "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_87jgtvlo97h3vw0n5e0f\n\n\n      \n\n \n                weighted.mean\n              \n\n\nx\n                  0.25\n                \n\ny\n                  0.81\n                \n\n\n\n\n    \n\n\nWhich produces the same results as:\n\nweighted.mean(newdata$x, newdata$w)\n\n[1] 0.2528909\n\nweighted.mean(newdata$y, newdata$w)\n\n[1] 0.8139608\n\n\nBut different results from:\n\nmean(newdata$x)\n\n[1] -0.07968538\n\nmean(newdata$y)\n\n[1] 0.03824354",
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@@ -1529,7 +1529,7 @@
     "href": "vignettes/datasummary.html#logical-subsets",
     "title": "Data Summaries",
     "section": "Logical subsets",
-    "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.",
+    "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_0mrj1qvmrwd2zjtvmftr\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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@@ -1540,7 +1540,7 @@
     "href": "vignettes/datasummary.html#missing-values",
     "title": "Data Summaries",
     "section": "Missing values",
-    "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",
+    "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_i8xaudjn5mnk60b34eo1\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_n8jrcz3bdw9i5xhgzrbh\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$z))\n\n\n        0         1 \n0.6666667 0.3333333 \n\ndatasummary(Factor(z) ~ N + Percent(), data = dat)\n\n \n\n  \n    \n\ntinytable_ylbwte7zkkjwyuvh1iwf\n\n\n      \n\nz\n                N\n                Percent\n              \n\n\n0\n                  4\n                  57.14\n                \n\n1\n                  2\n                  28.57\n                \n\n\n\n\n    \n\n\nTo count percentages without missing values, we can exploit the fact that Percent() accepts a denom argument which can be a logical vector:\n\ndatasummary(Factor(z) ~ N + Percent(denom = !is.na(dat$z)), data = dat)\n\n \n\n  \n    \n\ntinytable_jgvmg1nxadjkcudfnifi\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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@@ -1551,7 +1551,7 @@
     "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                  ?",
+    "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_0cctplbg8f3xae6kao9j\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                  ?",
     "crumbs": [
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@@ -1562,7 +1562,7 @@
     "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",
+    "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_pgiyv9kyp8cwwtudkiw2\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                  0.52\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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diff --git a/vignettes/appearance.html b/vignettes/appearance.html
index c30df9ea7..7b9c28814 100644
--- a/vignettes/appearance.html
+++ b/vignettes/appearance.html
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     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
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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_jyo96m2834xzj6gd3wuw" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_lb5dult3s72u791eryl2" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
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@@ -608,12 +608,12 @@ <h1 class="title">Themes and Styles</h1>
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+      function styleCell_tinytable_ip4i2ie1x9czh4ut7fk8(i, j, css_id) {
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@@ -621,8 +621,8 @@ <h1 class="title">Themes and Styles</h1>
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 </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_b2gtywmo2zsk58ugy5y9</title>
+<title>tinytable_ggy9riftm1d6aywhbadj</title>
 <style>
-.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; }
+.table td.tinytable_css_9if0y29wg8y99sok3uje, .table th.tinytable_css_9if0y29wg8y99sok3uje {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_uijwcj9hn7ixgiv3te4i, .table th.tinytable_css_uijwcj9hn7ixgiv3te4i {    text-align: left; }
+.table td.tinytable_css_2cheluj97qx52ru6fitc, .table th.tinytable_css_2cheluj97qx52ru6fitc {    text-align: right; }
+.table td.tinytable_css_np6oiloo3ssme1w7ofpf, .table th.tinytable_css_np6oiloo3ssme1w7ofpf {    text-align: right; }
+.table td.tinytable_css_o7f73xmg7lhd2pmo3crc, .table th.tinytable_css_o7f73xmg7lhd2pmo3crc {    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_b2gtywmo2zsk58ugy5y9" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ggy9riftm1d6aywhbadj" 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/idjfprst9k9bsfeou90g32.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idir7hvoenps9jcos2892c.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>hp </td>
                   <td>146.69</td>
                   <td>68.56</td>
-                  <td><img src="./tinytable_assets/iduav95z03lohvd4pped8f.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idelrgotvcsxg0cj5ewz79.png" style="height: 1em;"></td>
                 </tr>
 </tbody>
 </table>
 </div>
 
     <script>
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-        var table = document.getElementById("tinytable_b2gtywmo2zsk58ugy5y9");
+      function styleCell_tinytable_xcbz4adqrtt1p1u3ranl(i, j, css_id) {
+        var table = document.getElementById("tinytable_ggy9riftm1d6aywhbadj");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_b2gtywmo2zsk58ugy5y9');
+        var table = document.getElementById('tinytable_ggy9riftm1d6aywhbadj');
         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;
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-      function spanCell_tinytable_jn30kpjf1s7xzvojkaf7(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_b2gtywmo2zsk58ugy5y9");
+      function spanCell_tinytable_xcbz4adqrtt1p1u3ranl(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ggy9riftm1d6aywhbadj");
         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>
         targetCell.colSpan = colspan;
       }
 
-window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 0, 'tinytable_css_sgggohi7eyujhsuo7p54') })
-window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(0, 1, 'tinytable_css_sgggohi7eyujhsuo7p54') })
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-window.addEventListener('load', function () { styleCell_tinytable_jn30kpjf1s7xzvojkaf7(2, 3, 'tinytable_css_vfpt7v81qiqwlz3p49mz') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 0, 'tinytable_css_9if0y29wg8y99sok3uje') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 1, 'tinytable_css_9if0y29wg8y99sok3uje') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 2, 'tinytable_css_9if0y29wg8y99sok3uje') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 3, 'tinytable_css_9if0y29wg8y99sok3uje') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 0, 'tinytable_css_uijwcj9hn7ixgiv3te4i') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(1, 0, 'tinytable_css_uijwcj9hn7ixgiv3te4i') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(2, 0, 'tinytable_css_uijwcj9hn7ixgiv3te4i') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 1, 'tinytable_css_2cheluj97qx52ru6fitc') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(1, 1, 'tinytable_css_2cheluj97qx52ru6fitc') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(2, 1, 'tinytable_css_2cheluj97qx52ru6fitc') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 2, 'tinytable_css_np6oiloo3ssme1w7ofpf') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(1, 2, 'tinytable_css_np6oiloo3ssme1w7ofpf') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(2, 2, 'tinytable_css_np6oiloo3ssme1w7ofpf') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(0, 3, 'tinytable_css_o7f73xmg7lhd2pmo3crc') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(1, 3, 'tinytable_css_o7f73xmg7lhd2pmo3crc') })
+window.addEventListener('load', function () { styleCell_tinytable_xcbz4adqrtt1p1u3ranl(2, 3, 'tinytable_css_o7f73xmg7lhd2pmo3crc') })
     </script>
 </div>
 </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_0fygmd91861j6n6jf2dq</title>
+<title>tinytable_0xumlszc2chwpp50gehr</title>
 <style>
-.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; }
+.table td.tinytable_css_2y8iwrcsyngqms452a24, .table th.tinytable_css_2y8iwrcsyngqms452a24 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_ixjwjy1vge27vjafwg9c, .table th.tinytable_css_ixjwjy1vge27vjafwg9c {    text-align: left; }
+.table td.tinytable_css_f4gge4ytob3eo8wj5ikf, .table th.tinytable_css_f4gge4ytob3eo8wj5ikf {    text-align: center; }
+.table td.tinytable_css_a8sx8djgokwdf1kir1os, .table th.tinytable_css_a8sx8djgokwdf1kir1os {    text-align: center; }
+.table td.tinytable_css_ynoi8qvscgab2km4ary1, .table th.tinytable_css_ynoi8qvscgab2km4ary1 {    text-align: center; }
+.table td.tinytable_css_9orvvr07ue1hdujzqfeb, .table th.tinytable_css_9orvvr07ue1hdujzqfeb {    text-align: center; }
+.table td.tinytable_css_fbbbyn14kks7g8u6v8kc, .table th.tinytable_css_fbbbyn14kks7g8u6v8kc {    text-align: center; }
+.table td.tinytable_css_wtt50vo3l5q23685km7s, .table th.tinytable_css_wtt50vo3l5q23685km7s {    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_0fygmd91861j6n6jf2dq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless mytable" id="tinytable_0xumlszc2chwpp50gehr" 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_ck79q7vb1gn7lmi82qnk(i, j, css_id) {
-        var table = document.getElementById("tinytable_0fygmd91861j6n6jf2dq");
+      function styleCell_tinytable_x8xw9knrk7vepox8sjba(i, j, css_id) {
+        var table = document.getElementById("tinytable_0xumlszc2chwpp50gehr");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_0fygmd91861j6n6jf2dq');
+        var table = document.getElementById('tinytable_0xumlszc2chwpp50gehr');
         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_ck79q7vb1gn7lmi82qnk(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_0fygmd91861j6n6jf2dq");
+      function spanCell_tinytable_x8xw9knrk7vepox8sjba(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_0xumlszc2chwpp50gehr");
         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_x8xw9knrk7vepox8sjba(6, 3, 'tinytable_css_wtt50vo3l5q23685km7s') })
+window.addEventListener('load', function () { styleCell_tinytable_x8xw9knrk7vepox8sjba(6, 4, 'tinytable_css_wtt50vo3l5q23685km7s') })
+window.addEventListener('load', function () { styleCell_tinytable_x8xw9knrk7vepox8sjba(6, 5, 'tinytable_css_wtt50vo3l5q23685km7s') })
     </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="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 {
+<div id="ynwbldewgf" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#ynwbldewgf 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;
 }
 
-#boieojxtpv thead, #boieojxtpv tbody, #boieojxtpv tfoot, #boieojxtpv tr, #boieojxtpv td, #boieojxtpv th {
+#ynwbldewgf thead, #ynwbldewgf tbody, #ynwbldewgf tfoot, #ynwbldewgf tr, #ynwbldewgf td, #ynwbldewgf th {
   border-style: none;
 }
 
-#boieojxtpv p {
+#ynwbldewgf p {
   margin: 0;
   padding: 0;
 }
 
-#boieojxtpv .gt_table {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_caption {
+#ynwbldewgf .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#boieojxtpv .gt_title {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_subtitle {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_heading {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_bottom_border {
+#ynwbldewgf .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#boieojxtpv .gt_col_headings {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_col_heading {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_column_spanner_outer {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_column_spanner_outer:first-child {
+#ynwbldewgf .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#boieojxtpv .gt_column_spanner_outer:last-child {
+#ynwbldewgf .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#boieojxtpv .gt_column_spanner {
+#ynwbldewgf .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%;
 }
 
-#boieojxtpv .gt_spanner_row {
+#ynwbldewgf .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#boieojxtpv .gt_group_heading {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_empty_group_heading {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_from_md > :first-child {
+#ynwbldewgf .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#boieojxtpv .gt_from_md > :last-child {
+#ynwbldewgf .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#boieojxtpv .gt_row {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_stub {
+#ynwbldewgf .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1496,7 +1496,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#boieojxtpv .gt_stub_row_group {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_row_group_first td {
+#ynwbldewgf .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#boieojxtpv .gt_row_group_first th {
+#ynwbldewgf .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#boieojxtpv .gt_summary_row {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_first_summary_row {
+#ynwbldewgf .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#boieojxtpv .gt_first_summary_row.thick {
+#ynwbldewgf .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#boieojxtpv .gt_last_summary_row {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_grand_summary_row {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_first_grand_summary_row {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_last_grand_summary_row_top {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_striped {
+#ynwbldewgf .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#boieojxtpv .gt_table_body {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_footnotes {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_footnote {
+#ynwbldewgf .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1613,7 +1613,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#boieojxtpv .gt_sourcenotes {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_sourcenote {
+#ynwbldewgf .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;
 }
 
-#boieojxtpv .gt_left {
+#ynwbldewgf .gt_left {
   text-align: left;
 }
 
-#boieojxtpv .gt_center {
+#ynwbldewgf .gt_center {
   text-align: center;
 }
 
-#boieojxtpv .gt_right {
+#ynwbldewgf .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#boieojxtpv .gt_font_normal {
+#ynwbldewgf .gt_font_normal {
   font-weight: normal;
 }
 
-#boieojxtpv .gt_font_bold {
+#ynwbldewgf .gt_font_bold {
   font-weight: bold;
 }
 
-#boieojxtpv .gt_font_italic {
+#ynwbldewgf .gt_font_italic {
   font-style: italic;
 }
 
-#boieojxtpv .gt_super {
+#ynwbldewgf .gt_super {
   font-size: 65%;
 }
 
-#boieojxtpv .gt_footnote_marks {
+#ynwbldewgf .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#boieojxtpv .gt_asterisk {
+#ynwbldewgf .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#boieojxtpv .gt_indent_1 {
+#ynwbldewgf .gt_indent_1 {
   text-indent: 5px;
 }
 
-#boieojxtpv .gt_indent_2 {
+#ynwbldewgf .gt_indent_2 {
   text-indent: 10px;
 }
 
-#boieojxtpv .gt_indent_3 {
+#ynwbldewgf .gt_indent_3 {
   text-indent: 15px;
 }
 
-#boieojxtpv .gt_indent_4 {
+#ynwbldewgf .gt_indent_4 {
   text-indent: 20px;
 }
 
-#boieojxtpv .gt_indent_5 {
+#ynwbldewgf .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="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 {
+<div id="rbmtlzishx" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#rbmtlzishx 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;
 }
 
-#wuzsczbbqf thead, #wuzsczbbqf tbody, #wuzsczbbqf tfoot, #wuzsczbbqf tr, #wuzsczbbqf td, #wuzsczbbqf th {
+#rbmtlzishx thead, #rbmtlzishx tbody, #rbmtlzishx tfoot, #rbmtlzishx tr, #rbmtlzishx td, #rbmtlzishx th {
   border-style: none;
 }
 
-#wuzsczbbqf p {
+#rbmtlzishx p {
   margin: 0;
   padding: 0;
 }
 
-#wuzsczbbqf .gt_table {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_caption {
+#rbmtlzishx .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#wuzsczbbqf .gt_title {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_subtitle {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_heading {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_bottom_border {
+#rbmtlzishx .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#wuzsczbbqf .gt_col_headings {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_col_heading {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_column_spanner_outer {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_column_spanner_outer:first-child {
+#rbmtlzishx .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#wuzsczbbqf .gt_column_spanner_outer:last-child {
+#rbmtlzishx .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#wuzsczbbqf .gt_column_spanner {
+#rbmtlzishx .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%;
 }
 
-#wuzsczbbqf .gt_spanner_row {
+#rbmtlzishx .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#wuzsczbbqf .gt_group_heading {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_empty_group_heading {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_from_md > :first-child {
+#rbmtlzishx .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#wuzsczbbqf .gt_from_md > :last-child {
+#rbmtlzishx .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#wuzsczbbqf .gt_row {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_stub {
+#rbmtlzishx .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2061,7 +2061,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#wuzsczbbqf .gt_stub_row_group {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_row_group_first td {
+#rbmtlzishx .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#wuzsczbbqf .gt_row_group_first th {
+#rbmtlzishx .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#wuzsczbbqf .gt_summary_row {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_first_summary_row {
+#rbmtlzishx .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#wuzsczbbqf .gt_first_summary_row.thick {
+#rbmtlzishx .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#wuzsczbbqf .gt_last_summary_row {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_grand_summary_row {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_first_grand_summary_row {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_last_grand_summary_row_top {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_striped {
+#rbmtlzishx .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#wuzsczbbqf .gt_table_body {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_footnotes {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_footnote {
+#rbmtlzishx .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2178,7 +2178,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#wuzsczbbqf .gt_sourcenotes {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_sourcenote {
+#rbmtlzishx .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;
 }
 
-#wuzsczbbqf .gt_left {
+#rbmtlzishx .gt_left {
   text-align: left;
 }
 
-#wuzsczbbqf .gt_center {
+#rbmtlzishx .gt_center {
   text-align: center;
 }
 
-#wuzsczbbqf .gt_right {
+#rbmtlzishx .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#wuzsczbbqf .gt_font_normal {
+#rbmtlzishx .gt_font_normal {
   font-weight: normal;
 }
 
-#wuzsczbbqf .gt_font_bold {
+#rbmtlzishx .gt_font_bold {
   font-weight: bold;
 }
 
-#wuzsczbbqf .gt_font_italic {
+#rbmtlzishx .gt_font_italic {
   font-style: italic;
 }
 
-#wuzsczbbqf .gt_super {
+#rbmtlzishx .gt_super {
   font-size: 65%;
 }
 
-#wuzsczbbqf .gt_footnote_marks {
+#rbmtlzishx .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#wuzsczbbqf .gt_asterisk {
+#rbmtlzishx .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#wuzsczbbqf .gt_indent_1 {
+#rbmtlzishx .gt_indent_1 {
   text-indent: 5px;
 }
 
-#wuzsczbbqf .gt_indent_2 {
+#rbmtlzishx .gt_indent_2 {
   text-indent: 10px;
 }
 
-#wuzsczbbqf .gt_indent_3 {
+#rbmtlzishx .gt_indent_3 {
   text-indent: 15px;
 }
 
-#wuzsczbbqf .gt_indent_4 {
+#rbmtlzishx .gt_indent_4 {
   text-indent: 20px;
 }
 
-#wuzsczbbqf .gt_indent_5 {
+#rbmtlzishx .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="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 {
+<div id="ngnkbqgmsp" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#ngnkbqgmsp 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;
 }
 
-#rltfckoqzq thead, #rltfckoqzq tbody, #rltfckoqzq tfoot, #rltfckoqzq tr, #rltfckoqzq td, #rltfckoqzq th {
+#ngnkbqgmsp thead, #ngnkbqgmsp tbody, #ngnkbqgmsp tfoot, #ngnkbqgmsp tr, #ngnkbqgmsp td, #ngnkbqgmsp th {
   border-style: none;
 }
 
-#rltfckoqzq p {
+#ngnkbqgmsp p {
   margin: 0;
   padding: 0;
 }
 
-#rltfckoqzq .gt_table {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_caption {
+#ngnkbqgmsp .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#rltfckoqzq .gt_title {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_subtitle {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_heading {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_bottom_border {
+#ngnkbqgmsp .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#rltfckoqzq .gt_col_headings {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_col_heading {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_column_spanner_outer {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_column_spanner_outer:first-child {
+#ngnkbqgmsp .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#rltfckoqzq .gt_column_spanner_outer:last-child {
+#ngnkbqgmsp .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#rltfckoqzq .gt_column_spanner {
+#ngnkbqgmsp .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%;
 }
 
-#rltfckoqzq .gt_spanner_row {
+#ngnkbqgmsp .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#rltfckoqzq .gt_group_heading {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_empty_group_heading {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_from_md > :first-child {
+#ngnkbqgmsp .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#rltfckoqzq .gt_from_md > :last-child {
+#ngnkbqgmsp .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#rltfckoqzq .gt_row {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_stub {
+#ngnkbqgmsp .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2617,7 +2617,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#rltfckoqzq .gt_stub_row_group {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_row_group_first td {
+#ngnkbqgmsp .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#rltfckoqzq .gt_row_group_first th {
+#ngnkbqgmsp .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#rltfckoqzq .gt_summary_row {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_first_summary_row {
+#ngnkbqgmsp .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#rltfckoqzq .gt_first_summary_row.thick {
+#ngnkbqgmsp .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#rltfckoqzq .gt_last_summary_row {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_grand_summary_row {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_first_grand_summary_row {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_last_grand_summary_row_top {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_striped {
+#ngnkbqgmsp .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#rltfckoqzq .gt_table_body {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_footnotes {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_footnote {
+#ngnkbqgmsp .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2734,7 +2734,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#rltfckoqzq .gt_sourcenotes {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_sourcenote {
+#ngnkbqgmsp .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;
 }
 
-#rltfckoqzq .gt_left {
+#ngnkbqgmsp .gt_left {
   text-align: left;
 }
 
-#rltfckoqzq .gt_center {
+#ngnkbqgmsp .gt_center {
   text-align: center;
 }
 
-#rltfckoqzq .gt_right {
+#ngnkbqgmsp .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#rltfckoqzq .gt_font_normal {
+#ngnkbqgmsp .gt_font_normal {
   font-weight: normal;
 }
 
-#rltfckoqzq .gt_font_bold {
+#ngnkbqgmsp .gt_font_bold {
   font-weight: bold;
 }
 
-#rltfckoqzq .gt_font_italic {
+#ngnkbqgmsp .gt_font_italic {
   font-style: italic;
 }
 
-#rltfckoqzq .gt_super {
+#ngnkbqgmsp .gt_super {
   font-size: 65%;
 }
 
-#rltfckoqzq .gt_footnote_marks {
+#ngnkbqgmsp .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#rltfckoqzq .gt_asterisk {
+#ngnkbqgmsp .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#rltfckoqzq .gt_indent_1 {
+#ngnkbqgmsp .gt_indent_1 {
   text-indent: 5px;
 }
 
-#rltfckoqzq .gt_indent_2 {
+#ngnkbqgmsp .gt_indent_2 {
   text-indent: 10px;
 }
 
-#rltfckoqzq .gt_indent_3 {
+#ngnkbqgmsp .gt_indent_3 {
   text-indent: 15px;
 }
 
-#rltfckoqzq .gt_indent_4 {
+#ngnkbqgmsp .gt_indent_4 {
   text-indent: 20px;
 }
 
-#rltfckoqzq .gt_indent_5 {
+#ngnkbqgmsp .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="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 {
+<div id="mtgxggdoma" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#mtgxggdoma 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;
 }
 
-#sonvwxdofp thead, #sonvwxdofp tbody, #sonvwxdofp tfoot, #sonvwxdofp tr, #sonvwxdofp td, #sonvwxdofp th {
+#mtgxggdoma thead, #mtgxggdoma tbody, #mtgxggdoma tfoot, #mtgxggdoma tr, #mtgxggdoma td, #mtgxggdoma th {
   border-style: none;
 }
 
-#sonvwxdofp p {
+#mtgxggdoma p {
   margin: 0;
   padding: 0;
 }
 
-#sonvwxdofp .gt_table {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_caption {
+#mtgxggdoma .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#sonvwxdofp .gt_title {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_subtitle {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_heading {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_bottom_border {
+#mtgxggdoma .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#sonvwxdofp .gt_col_headings {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_col_heading {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_column_spanner_outer {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_column_spanner_outer:first-child {
+#mtgxggdoma .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#sonvwxdofp .gt_column_spanner_outer:last-child {
+#mtgxggdoma .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#sonvwxdofp .gt_column_spanner {
+#mtgxggdoma .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%;
 }
 
-#sonvwxdofp .gt_spanner_row {
+#mtgxggdoma .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#sonvwxdofp .gt_group_heading {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_empty_group_heading {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_from_md > :first-child {
+#mtgxggdoma .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#sonvwxdofp .gt_from_md > :last-child {
+#mtgxggdoma .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#sonvwxdofp .gt_row {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_stub {
+#mtgxggdoma .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3132,7 +3132,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#sonvwxdofp .gt_stub_row_group {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_row_group_first td {
+#mtgxggdoma .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#sonvwxdofp .gt_row_group_first th {
+#mtgxggdoma .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#sonvwxdofp .gt_summary_row {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_first_summary_row {
+#mtgxggdoma .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#sonvwxdofp .gt_first_summary_row.thick {
+#mtgxggdoma .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#sonvwxdofp .gt_last_summary_row {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_grand_summary_row {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_first_grand_summary_row {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_last_grand_summary_row_top {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_striped {
+#mtgxggdoma .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#sonvwxdofp .gt_table_body {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_footnotes {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_footnote {
+#mtgxggdoma .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -3249,7 +3249,7 @@ <h1 class="title">Themes and Styles</h1>
   padding-right: 5px;
 }
 
-#sonvwxdofp .gt_sourcenotes {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_sourcenote {
+#mtgxggdoma .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;
 }
 
-#sonvwxdofp .gt_left {
+#mtgxggdoma .gt_left {
   text-align: left;
 }
 
-#sonvwxdofp .gt_center {
+#mtgxggdoma .gt_center {
   text-align: center;
 }
 
-#sonvwxdofp .gt_right {
+#mtgxggdoma .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#sonvwxdofp .gt_font_normal {
+#mtgxggdoma .gt_font_normal {
   font-weight: normal;
 }
 
-#sonvwxdofp .gt_font_bold {
+#mtgxggdoma .gt_font_bold {
   font-weight: bold;
 }
 
-#sonvwxdofp .gt_font_italic {
+#mtgxggdoma .gt_font_italic {
   font-style: italic;
 }
 
-#sonvwxdofp .gt_super {
+#mtgxggdoma .gt_super {
   font-size: 65%;
 }
 
-#sonvwxdofp .gt_footnote_marks {
+#mtgxggdoma .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#sonvwxdofp .gt_asterisk {
+#mtgxggdoma .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#sonvwxdofp .gt_indent_1 {
+#mtgxggdoma .gt_indent_1 {
   text-indent: 5px;
 }
 
-#sonvwxdofp .gt_indent_2 {
+#mtgxggdoma .gt_indent_2 {
   text-indent: 10px;
 }
 
-#sonvwxdofp .gt_indent_3 {
+#mtgxggdoma .gt_indent_3 {
   text-indent: 15px;
 }
 
-#sonvwxdofp .gt_indent_4 {
+#mtgxggdoma .gt_indent_4 {
   text-indent: 20px;
 }
 
-#sonvwxdofp .gt_indent_5 {
+#mtgxggdoma .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-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 class="datatables html-widget html-fill-item" id="htmlwidget-fc7bcc62936f06d7a12a" style="width:100%;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-fc7bcc62936f06d7a12a">{"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_gsd4n4w1rb783ti9cvgb</title>
+<title>tinytable_pjjhdevneeiz588ls4bx</title>
 <style>
-.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; }
+.table td.tinytable_css_5np33csrvy76u1s1r9xr, .table th.tinytable_css_5np33csrvy76u1s1r9xr {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_86brxok1wydkvg54krb6, .table th.tinytable_css_86brxok1wydkvg54krb6 {    text-align: left; }
+.table td.tinytable_css_q9dz8ea21vp33210cree, .table th.tinytable_css_q9dz8ea21vp33210cree {    text-align: center; }
+.table td.tinytable_css_158eydv8aq8od2o3hbn5, .table th.tinytable_css_158eydv8aq8od2o3hbn5 {    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_gsd4n4w1rb783ti9cvgb" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_pjjhdevneeiz588ls4bx" 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_lhxbtw5d72tivv0mf6bx(i, j, css_id) {
-        var table = document.getElementById("tinytable_gsd4n4w1rb783ti9cvgb");
+      function styleCell_tinytable_5u3r3ziil921zev4z3aj(i, j, css_id) {
+        var table = document.getElementById("tinytable_pjjhdevneeiz588ls4bx");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_gsd4n4w1rb783ti9cvgb');
+        var table = document.getElementById('tinytable_pjjhdevneeiz588ls4bx');
         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_lhxbtw5d72tivv0mf6bx(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_gsd4n4w1rb783ti9cvgb");
+      function spanCell_tinytable_5u3r3ziil921zev4z3aj(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_pjjhdevneeiz588ls4bx");
         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_lhxbtw5d72tivv0mf6bx(0, 0, 'tinytable_css_azy8vs8o06ve930rabmh') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(0, 1, 'tinytable_css_azy8vs8o06ve930rabmh') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(0, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(1, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(2, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(3, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(4, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(5, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(6, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(7, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(8, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(9, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(10, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(11, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(12, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(13, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(14, 0, 'tinytable_css_aly4vxfv07n55qdh1scz') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(0, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(1, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(2, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(3, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(4, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(5, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(6, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(7, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(8, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(9, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(10, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(11, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(12, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(13, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(14, 1, 'tinytable_css_ymusypjvmjssc12v5emx') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(6, 0, 'tinytable_css_1zobxh9b69l6gy03uyom') })
-window.addEventListener('load', function () { styleCell_tinytable_lhxbtw5d72tivv0mf6bx(6, 1, 'tinytable_css_1zobxh9b69l6gy03uyom') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(0, 0, 'tinytable_css_5np33csrvy76u1s1r9xr') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(0, 1, 'tinytable_css_5np33csrvy76u1s1r9xr') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(0, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(1, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(2, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(3, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(4, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(5, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(6, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(7, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(8, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(9, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(10, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(11, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
+window.addEventListener('load', function () { styleCell_tinytable_5u3r3ziil921zev4z3aj(12, 0, 'tinytable_css_86brxok1wydkvg54krb6') })
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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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@@ -3725,7 +3725,7 @@ <h1 class="title">Themes and Styles</h1>
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@@ -3736,7 +3736,7 @@ <h1 class="title">Themes and Styles</h1>
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diff --git a/vignettes/datasummary.html b/vignettes/datasummary.html
index 3e337ac94..378fee8cb 100644
--- a/vignettes/datasummary.html
+++ b/vignettes/datasummary.html
@@ -477,10 +477,10 @@ <h1 class="title">Data Summaries</h1>
   
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       }
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-      <table class="table table-borderless" id="tinytable_r72mqokptlgq0ac5ldz8" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ymros459i5p7tvezii5t" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -514,7 +514,7 @@ <h1 class="title">Data Summaries</h1>
                   <td>1.0</td>
                   <td>172.5</td>
                   <td>344.0</td>
-                  <td><img src="./tinytable_assets/idtnrxpp35luksrqecgsd7.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idtik5javjkduihvbp61cr.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>bill_length_mm</td>
@@ -525,7 +525,7 @@ <h1 class="title">Data Summaries</h1>
                   <td>32.1</td>
                   <td>44.5</td>
                   <td>59.6</td>
-                  <td><img src="./tinytable_assets/id0zmnrt1femwj6yso59lr.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/ideq6y0kxnhl7mkj3dul50.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>bill_depth_mm</td>
@@ -536,7 +536,7 @@ <h1 class="title">Data Summaries</h1>
                   <td>13.1</td>
                   <td>17.3</td>
                   <td>21.5</td>
-                  <td><img src="./tinytable_assets/idqjrkldgeimxs0gjw36yv.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idqm4203et4k7rt9d9hzyj.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>flipper_length_mm</td>
@@ -547,7 +547,7 @@ <h1 class="title">Data Summaries</h1>
                   <td>172.0</td>
                   <td>197.0</td>
                   <td>231.0</td>
-                  <td><img src="./tinytable_assets/id2daaxm4r6sympr9jgqdt.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idbvvzhsj7tpyk2th6f6xm.png" style="height: 1em;"></td>
                 </tr>
 <tr>
 <td>body_mass_g</td>
@@ -558,7 +558,7 @@ <h1 class="title">Data Summaries</h1>
                   <td>2700.0</td>
                   <td>4050.0</td>
                   <td>6300.0</td>
-                  <td><img src="./tinytable_assets/idpghd4i3bsqq6sspgc3iz.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idiwkunjr9bokgaaske8uh.png" style="height: 1em;"></td>
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 <tr>
 <td>year</td>
@@ -569,7 +569,7 @@ <h1 class="title">Data Summaries</h1>
                   <td>2007.0</td>
                   <td>2008.0</td>
                   <td>2009.0</td>
-                  <td><img src="./tinytable_assets/idi1www7sbrwij83bcdis3.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/ids456uxws03qlzan7a5xl.png" style="height: 1em;"></td>
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@@ -686,12 +686,12 @@ <h1 class="title">Data Summaries</h1>
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@@ -757,13 +757,13 @@ <h1 class="title">Data Summaries</h1>
   
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@@ -775,7 +775,7 @@ <h1 class="title">Data Summaries</h1>
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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">
+      <table class="table table-borderless" id="tinytable_zkqzbyb7i05si7a91u88" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -973,14 +973,14 @@ <h1 class="title">Data Summaries</h1>
   
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         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_t2rzze4kj6hc28gs4o33(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_51jj21etu2wk1ftv6e5u");
+      function spanCell_tinytable_3o177cszsiny7fb8f8xk(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_c55b5kbpcyscc9w9q4tm");
         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>
         targetCell.colSpan = colspan;
       }
 
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@@ -2068,12 +2068,12 @@ <h1 class="title">Data Summaries</h1>
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@@ -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_0oc8o58rle4vir4jlagy</title>
+<title>tinytable_2xazg2wr5sxi3nm8fypz</title>
 <style>
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-.table td.tinytable_css_r5ir3p53gl31p0ogrwe0, .table th.tinytable_css_r5ir3p53gl31p0ogrwe0 {    text-align: right; }
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+.table td.tinytable_css_nwh0op73cw07gc3bbnjz, .table th.tinytable_css_nwh0op73cw07gc3bbnjz {    text-align: left; }
+.table td.tinytable_css_5l3bz8v5fbovlzgopwmg, .table th.tinytable_css_5l3bz8v5fbovlzgopwmg {    text-align: right; }
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     MathJax = {
@@ -2638,7 +2638,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_0oc8o58rle4vir4jlagy" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_2xazg2wr5sxi3nm8fypz" 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>
 
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-        var table = document.getElementById("tinytable_0oc8o58rle4vir4jlagy");
+      function styleCell_tinytable_7z81eecz67y9vhciti9h(i, j, css_id) {
+        var table = document.getElementById("tinytable_2xazg2wr5sxi3nm8fypz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_0oc8o58rle4vir4jlagy');
+        var table = document.getElementById('tinytable_2xazg2wr5sxi3nm8fypz');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2664,8 +2664,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_t5xwn7cq1zgupw79qdct(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_0oc8o58rle4vir4jlagy");
+      function spanCell_tinytable_7z81eecz67y9vhciti9h(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_2xazg2wr5sxi3nm8fypz");
         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>
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+window.addEventListener('load', function () { styleCell_tinytable_7z81eecz67y9vhciti9h(0, 1, 'tinytable_css_27tl5xyiwvvg75q8vyym') })
+window.addEventListener('load', function () { styleCell_tinytable_7z81eecz67y9vhciti9h(0, 0, 'tinytable_css_nwh0op73cw07gc3bbnjz') })
+window.addEventListener('load', function () { styleCell_tinytable_7z81eecz67y9vhciti9h(1, 0, 'tinytable_css_nwh0op73cw07gc3bbnjz') })
+window.addEventListener('load', function () { styleCell_tinytable_7z81eecz67y9vhciti9h(0, 1, 'tinytable_css_5l3bz8v5fbovlzgopwmg') })
+window.addEventListener('load', function () { styleCell_tinytable_7z81eecz67y9vhciti9h(1, 1, 'tinytable_css_5l3bz8v5fbovlzgopwmg') })
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@@ -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_8e2hlswrnqomu78pmoc4</title>
+<title>tinytable_juhabvsox6bgrpwgjjf0</title>
 <style>
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-.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; }
+.table td.tinytable_css_crsyc382py77sk0b25xh, .table th.tinytable_css_crsyc382py77sk0b25xh {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_d17b1tqpziik9gbu9tb2, .table th.tinytable_css_d17b1tqpziik9gbu9tb2 {    text-align: left; }
+.table td.tinytable_css_qb0l78t34yc2jjoyr355, .table th.tinytable_css_qb0l78t34yc2jjoyr355 {    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_8e2hlswrnqomu78pmoc4" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_juhabvsox6bgrpwgjjf0" 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_jy1nlvct4wl51jlmige1(i, j, css_id) {
-        var table = document.getElementById("tinytable_8e2hlswrnqomu78pmoc4");
+      function styleCell_tinytable_i2cn1bdx41tq5vqeha02(i, j, css_id) {
+        var table = document.getElementById("tinytable_juhabvsox6bgrpwgjjf0");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_8e2hlswrnqomu78pmoc4');
+        var table = document.getElementById('tinytable_juhabvsox6bgrpwgjjf0');
         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_jy1nlvct4wl51jlmige1(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_8e2hlswrnqomu78pmoc4");
+      function spanCell_tinytable_i2cn1bdx41tq5vqeha02(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_juhabvsox6bgrpwgjjf0");
         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_jy1nlvct4wl51jlmige1(1, 1, 'tinytable_css_yyg83ky78e00d5vkhia6') })
+window.addEventListener('load', function () { styleCell_tinytable_i2cn1bdx41tq5vqeha02(0, 0, 'tinytable_css_crsyc382py77sk0b25xh') })
+window.addEventListener('load', function () { styleCell_tinytable_i2cn1bdx41tq5vqeha02(0, 1, 'tinytable_css_crsyc382py77sk0b25xh') })
+window.addEventListener('load', function () { styleCell_tinytable_i2cn1bdx41tq5vqeha02(0, 0, 'tinytable_css_d17b1tqpziik9gbu9tb2') })
+window.addEventListener('load', function () { styleCell_tinytable_i2cn1bdx41tq5vqeha02(1, 0, 'tinytable_css_d17b1tqpziik9gbu9tb2') })
+window.addEventListener('load', function () { styleCell_tinytable_i2cn1bdx41tq5vqeha02(0, 1, 'tinytable_css_qb0l78t34yc2jjoyr355') })
+window.addEventListener('load', function () { styleCell_tinytable_i2cn1bdx41tq5vqeha02(1, 1, 'tinytable_css_qb0l78t34yc2jjoyr355') })
     </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_j5v7hmg8vynw9ut2e8lx</title>
+<title>tinytable_ujlh99j6r39njwqr68rb</title>
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-.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; }
+.table td.tinytable_css_twb8pg1wkvwl36kt0mr1, .table th.tinytable_css_twb8pg1wkvwl36kt0mr1 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_p7wd3t6vga3xhx98nyk7, .table th.tinytable_css_p7wd3t6vga3xhx98nyk7 {    text-align: left; }
+.table td.tinytable_css_geg7zhb2ej3kj61afdxc, .table th.tinytable_css_geg7zhb2ej3kj61afdxc {    text-align: right; }
     </style>
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     MathJax = {
@@ -2817,7 +2817,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_j5v7hmg8vynw9ut2e8lx" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ujlh99j6r39njwqr68rb" 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_rlr9vwthw697ra83zepe(i, j, css_id) {
-        var table = document.getElementById("tinytable_j5v7hmg8vynw9ut2e8lx");
+      function styleCell_tinytable_haaywp0k17y8y7bqyszz(i, j, css_id) {
+        var table = document.getElementById("tinytable_ujlh99j6r39njwqr68rb");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_j5v7hmg8vynw9ut2e8lx');
+        var table = document.getElementById('tinytable_ujlh99j6r39njwqr68rb');
         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_rlr9vwthw697ra83zepe(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_j5v7hmg8vynw9ut2e8lx");
+      function spanCell_tinytable_haaywp0k17y8y7bqyszz(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ujlh99j6r39njwqr68rb");
         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_rlr9vwthw697ra83zepe(1, 1, 'tinytable_css_buxlvm2lx13hd7tp9krd') })
+window.addEventListener('load', function () { styleCell_tinytable_haaywp0k17y8y7bqyszz(0, 0, 'tinytable_css_twb8pg1wkvwl36kt0mr1') })
+window.addEventListener('load', function () { styleCell_tinytable_haaywp0k17y8y7bqyszz(0, 1, 'tinytable_css_twb8pg1wkvwl36kt0mr1') })
+window.addEventListener('load', function () { styleCell_tinytable_haaywp0k17y8y7bqyszz(0, 0, 'tinytable_css_p7wd3t6vga3xhx98nyk7') })
+window.addEventListener('load', function () { styleCell_tinytable_haaywp0k17y8y7bqyszz(1, 0, 'tinytable_css_p7wd3t6vga3xhx98nyk7') })
+window.addEventListener('load', function () { styleCell_tinytable_haaywp0k17y8y7bqyszz(0, 1, 'tinytable_css_geg7zhb2ej3kj61afdxc') })
+window.addEventListener('load', function () { styleCell_tinytable_haaywp0k17y8y7bqyszz(1, 1, 'tinytable_css_geg7zhb2ej3kj61afdxc') })
     </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_7mdf14epg6tplt7461i1</title>
+<title>tinytable_7f6louttieuzdq67awtk</title>
 <style>
-.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; }
+.table td.tinytable_css_6jduidisig1nv2qml37b, .table th.tinytable_css_6jduidisig1nv2qml37b {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_trwsk1ue4t8vv4at2zbc, .table th.tinytable_css_trwsk1ue4t8vv4at2zbc {    text-align: left; }
+.table td.tinytable_css_ssgh5r05oce2qwjogq3b, .table th.tinytable_css_ssgh5r05oce2qwjogq3b {    text-align: right; }
     </style>
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     MathJax = {
@@ -2909,7 +2909,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_7mdf14epg6tplt7461i1" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_7f6louttieuzdq67awtk" 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_9gtx5gse5fpgrgq90v23(i, j, css_id) {
-        var table = document.getElementById("tinytable_7mdf14epg6tplt7461i1");
+      function styleCell_tinytable_ioukb0fnp5zougvhr8hk(i, j, css_id) {
+        var table = document.getElementById("tinytable_7f6louttieuzdq67awtk");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_7mdf14epg6tplt7461i1');
+        var table = document.getElementById('tinytable_7f6louttieuzdq67awtk');
         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_9gtx5gse5fpgrgq90v23(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_7mdf14epg6tplt7461i1");
+      function spanCell_tinytable_ioukb0fnp5zougvhr8hk(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_7f6louttieuzdq67awtk");
         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_9gtx5gse5fpgrgq90v23(1, 1, 'tinytable_css_0vsotyqe8801663mcrzv') })
+window.addEventListener('load', function () { styleCell_tinytable_ioukb0fnp5zougvhr8hk(0, 0, 'tinytable_css_6jduidisig1nv2qml37b') })
+window.addEventListener('load', function () { styleCell_tinytable_ioukb0fnp5zougvhr8hk(0, 1, 'tinytable_css_6jduidisig1nv2qml37b') })
+window.addEventListener('load', function () { styleCell_tinytable_ioukb0fnp5zougvhr8hk(0, 0, 'tinytable_css_trwsk1ue4t8vv4at2zbc') })
+window.addEventListener('load', function () { styleCell_tinytable_ioukb0fnp5zougvhr8hk(1, 0, 'tinytable_css_trwsk1ue4t8vv4at2zbc') })
+window.addEventListener('load', function () { styleCell_tinytable_ioukb0fnp5zougvhr8hk(0, 1, 'tinytable_css_ssgh5r05oce2qwjogq3b') })
+window.addEventListener('load', function () { styleCell_tinytable_ioukb0fnp5zougvhr8hk(1, 1, 'tinytable_css_ssgh5r05oce2qwjogq3b') })
     </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_078za3ozts6f5b69kczq</title>
+<title>tinytable_v00ecm16vt4dolwhr0ui</title>
 <style>
-.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; }
+.table td.tinytable_css_y3x5wppf1exj7wq1ale1, .table th.tinytable_css_y3x5wppf1exj7wq1ale1 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_kn5p3xhnhyjbc3imhcms, .table th.tinytable_css_kn5p3xhnhyjbc3imhcms {    text-align: left; }
+.table td.tinytable_css_thin94uh41ooptria61n, .table th.tinytable_css_thin94uh41ooptria61n {    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_078za3ozts6f5b69kczq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_v00ecm16vt4dolwhr0ui" 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_hulgmqdke868qep8umqx(i, j, css_id) {
-        var table = document.getElementById("tinytable_078za3ozts6f5b69kczq");
+      function styleCell_tinytable_49kr8wotbhkmq20uo6n5(i, j, css_id) {
+        var table = document.getElementById("tinytable_v00ecm16vt4dolwhr0ui");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_078za3ozts6f5b69kczq');
+        var table = document.getElementById('tinytable_v00ecm16vt4dolwhr0ui');
         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_hulgmqdke868qep8umqx(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_078za3ozts6f5b69kczq");
+      function spanCell_tinytable_49kr8wotbhkmq20uo6n5(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_v00ecm16vt4dolwhr0ui");
         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_hulgmqdke868qep8umqx(0, 0, 'tinytable_css_gxruu1lkdfqwbdwv650r') })
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-window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(0, 0, 'tinytable_css_t2p9zypln5ebt4h5qzwm') })
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-window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(0, 1, 'tinytable_css_gpe3gvsjvfks0mbgfkfw') })
-window.addEventListener('load', function () { styleCell_tinytable_hulgmqdke868qep8umqx(1, 1, 'tinytable_css_gpe3gvsjvfks0mbgfkfw') })
+window.addEventListener('load', function () { styleCell_tinytable_49kr8wotbhkmq20uo6n5(0, 0, 'tinytable_css_y3x5wppf1exj7wq1ale1') })
+window.addEventListener('load', function () { styleCell_tinytable_49kr8wotbhkmq20uo6n5(0, 1, 'tinytable_css_y3x5wppf1exj7wq1ale1') })
+window.addEventListener('load', function () { styleCell_tinytable_49kr8wotbhkmq20uo6n5(0, 0, 'tinytable_css_kn5p3xhnhyjbc3imhcms') })
+window.addEventListener('load', function () { styleCell_tinytable_49kr8wotbhkmq20uo6n5(1, 0, 'tinytable_css_kn5p3xhnhyjbc3imhcms') })
+window.addEventListener('load', function () { styleCell_tinytable_49kr8wotbhkmq20uo6n5(0, 1, 'tinytable_css_thin94uh41ooptria61n') })
+window.addEventListener('load', function () { styleCell_tinytable_49kr8wotbhkmq20uo6n5(1, 1, 'tinytable_css_thin94uh41ooptria61n') })
     </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_4ndr0ikwtgv4ym7nlxgg</title>
+<title>tinytable_k9caxbs5kadtfjt2qdxa</title>
 <style>
-.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; }
+.table td.tinytable_css_uh03aa05c2whp48ggy9y, .table th.tinytable_css_uh03aa05c2whp48ggy9y {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_svgakrnu30cpyxkas7hj, .table th.tinytable_css_svgakrnu30cpyxkas7hj {    text-align: left; }
+.table td.tinytable_css_w9uvsdot8qyzhxue3nqa, .table th.tinytable_css_w9uvsdot8qyzhxue3nqa {    text-align: right; }
+.table td.tinytable_css_e71nd913nd841rhplljc, .table th.tinytable_css_e71nd913nd841rhplljc {    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_4ndr0ikwtgv4ym7nlxgg" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_k9caxbs5kadtfjt2qdxa" 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_0y7l5ifk2m1qf876e8wa(i, j, css_id) {
-        var table = document.getElementById("tinytable_4ndr0ikwtgv4ym7nlxgg");
+      function styleCell_tinytable_b508uckoith26drt48wx(i, j, css_id) {
+        var table = document.getElementById("tinytable_k9caxbs5kadtfjt2qdxa");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_4ndr0ikwtgv4ym7nlxgg');
+        var table = document.getElementById('tinytable_k9caxbs5kadtfjt2qdxa');
         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_0y7l5ifk2m1qf876e8wa(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_4ndr0ikwtgv4ym7nlxgg");
+      function spanCell_tinytable_b508uckoith26drt48wx(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_k9caxbs5kadtfjt2qdxa");
         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;
       }
 
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-window.addEventListener('load', function () { styleCell_tinytable_0y7l5ifk2m1qf876e8wa(2, 2, 'tinytable_css_4or0y0risatk3rbwefma') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(0, 0, 'tinytable_css_uh03aa05c2whp48ggy9y') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(0, 1, 'tinytable_css_uh03aa05c2whp48ggy9y') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(0, 2, 'tinytable_css_uh03aa05c2whp48ggy9y') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(0, 0, 'tinytable_css_svgakrnu30cpyxkas7hj') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(1, 0, 'tinytable_css_svgakrnu30cpyxkas7hj') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(2, 0, 'tinytable_css_svgakrnu30cpyxkas7hj') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(0, 1, 'tinytable_css_w9uvsdot8qyzhxue3nqa') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(1, 1, 'tinytable_css_w9uvsdot8qyzhxue3nqa') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(2, 1, 'tinytable_css_w9uvsdot8qyzhxue3nqa') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(0, 2, 'tinytable_css_e71nd913nd841rhplljc') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(1, 2, 'tinytable_css_e71nd913nd841rhplljc') })
+window.addEventListener('load', function () { styleCell_tinytable_b508uckoith26drt48wx(2, 2, 'tinytable_css_e71nd913nd841rhplljc') })
     </script>
 </div>
 </div>
@@ -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_0lrl0y7y9a6aetp3h30z</title>
+<title>tinytable_stkwuzlxydvsy4ns2jbh</title>
 <style>
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-.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; }
-.table td.tinytable_css_abxpxcck87m3pahyrvrm, .table th.tinytable_css_abxpxcck87m3pahyrvrm {    text-align: right; }
+.table td.tinytable_css_06mkmuj645utg2iikg73, .table th.tinytable_css_06mkmuj645utg2iikg73 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_rz8krb4ya51g5gikauqt, .table th.tinytable_css_rz8krb4ya51g5gikauqt {    text-align: left; }
+.table td.tinytable_css_2aeu5ty8jzy08u6jmk0b, .table th.tinytable_css_2aeu5ty8jzy08u6jmk0b {    text-align: right; }
+.table td.tinytable_css_p2ay018vg5d26lxl5rvi, .table th.tinytable_css_p2ay018vg5d26lxl5rvi {    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_0lrl0y7y9a6aetp3h30z" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_stkwuzlxydvsy4ns2jbh" 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>
@@ -3236,12 +3236,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
     <script>
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-        var table = document.getElementById("tinytable_0lrl0y7y9a6aetp3h30z");
+      function styleCell_tinytable_8tkxg67nqvai4kbnkcf2(i, j, css_id) {
+        var table = document.getElementById("tinytable_stkwuzlxydvsy4ns2jbh");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_0lrl0y7y9a6aetp3h30z');
+        var table = document.getElementById('tinytable_stkwuzlxydvsy4ns2jbh');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3249,8 +3249,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_frm4y5zsd9o8o1z2v0bk(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_0lrl0y7y9a6aetp3h30z");
+      function spanCell_tinytable_8tkxg67nqvai4kbnkcf2(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_stkwuzlxydvsy4ns2jbh");
         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_8tkxg67nqvai4kbnkcf2(2, 1, 'tinytable_css_2aeu5ty8jzy08u6jmk0b') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(3, 1, 'tinytable_css_2aeu5ty8jzy08u6jmk0b') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(4, 1, 'tinytable_css_2aeu5ty8jzy08u6jmk0b') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(5, 1, 'tinytable_css_2aeu5ty8jzy08u6jmk0b') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(6, 1, 'tinytable_css_2aeu5ty8jzy08u6jmk0b') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(0, 2, 'tinytable_css_p2ay018vg5d26lxl5rvi') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(1, 2, 'tinytable_css_p2ay018vg5d26lxl5rvi') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(2, 2, 'tinytable_css_p2ay018vg5d26lxl5rvi') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(3, 2, 'tinytable_css_p2ay018vg5d26lxl5rvi') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(4, 2, 'tinytable_css_p2ay018vg5d26lxl5rvi') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(5, 2, 'tinytable_css_p2ay018vg5d26lxl5rvi') })
+window.addEventListener('load', function () { styleCell_tinytable_8tkxg67nqvai4kbnkcf2(6, 2, 'tinytable_css_p2ay018vg5d26lxl5rvi') })
     </script>
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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_gmpomhntrat7k9d0i4e3</title>
+<title>tinytable_tmyrfjvbvpjqcmxkglqr</title>
 <style>
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-.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; }
+.table td.tinytable_css_sa3x2jb1m24v3u1r7uzi, .table th.tinytable_css_sa3x2jb1m24v3u1r7uzi {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_v6d5p1qxalv1cov4z6yv, .table th.tinytable_css_v6d5p1qxalv1cov4z6yv {    text-align: left; }
+.table td.tinytable_css_sfapn42hhw0l998sovuj, .table th.tinytable_css_sfapn42hhw0l998sovuj {    text-align: right; }
+.table td.tinytable_css_io6nf2j1vnsgrumzy2hg, .table th.tinytable_css_io6nf2j1vnsgrumzy2hg {    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 = {
@@ -3334,7 +3334,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_gmpomhntrat7k9d0i4e3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_tmyrfjvbvpjqcmxkglqr" 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_f4g2og9yt13q2je89g3y(i, j, css_id) {
-        var table = document.getElementById("tinytable_gmpomhntrat7k9d0i4e3");
+      function styleCell_tinytable_wyc895ndfz8t1t2kz0e8(i, j, css_id) {
+        var table = document.getElementById("tinytable_tmyrfjvbvpjqcmxkglqr");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_gmpomhntrat7k9d0i4e3');
+        var table = document.getElementById('tinytable_tmyrfjvbvpjqcmxkglqr');
         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_f4g2og9yt13q2je89g3y(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_gmpomhntrat7k9d0i4e3");
+      function spanCell_tinytable_wyc895ndfz8t1t2kz0e8(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_tmyrfjvbvpjqcmxkglqr");
         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(2, 2, 'tinytable_css_smq6a9wrcifnttwx3eqv') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(0, 0, 'tinytable_css_sa3x2jb1m24v3u1r7uzi') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(0, 1, 'tinytable_css_sa3x2jb1m24v3u1r7uzi') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(0, 2, 'tinytable_css_sa3x2jb1m24v3u1r7uzi') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(0, 0, 'tinytable_css_v6d5p1qxalv1cov4z6yv') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(1, 0, 'tinytable_css_v6d5p1qxalv1cov4z6yv') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(2, 0, 'tinytable_css_v6d5p1qxalv1cov4z6yv') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(0, 1, 'tinytable_css_sfapn42hhw0l998sovuj') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(1, 1, 'tinytable_css_sfapn42hhw0l998sovuj') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(2, 1, 'tinytable_css_sfapn42hhw0l998sovuj') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(0, 2, 'tinytable_css_io6nf2j1vnsgrumzy2hg') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(1, 2, 'tinytable_css_io6nf2j1vnsgrumzy2hg') })
+window.addEventListener('load', function () { styleCell_tinytable_wyc895ndfz8t1t2kz0e8(2, 2, 'tinytable_css_io6nf2j1vnsgrumzy2hg') })
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@@ -3422,17 +3422,17 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_61gfvsq6e63e6kvnf1gv</title>
+<title>tinytable_m4yifvil1lpu998ia1a6</title>
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+.table td.tinytable_css_asjlsvvmf61asae9vb9e, .table th.tinytable_css_asjlsvvmf61asae9vb9e {    text-align: right; }
+.table td.tinytable_css_qk6biyscvu31eua8hj0g, .table th.tinytable_css_qk6biyscvu31eua8hj0g {    text-align: right; }
+.table td.tinytable_css_tw0ndtwmjibn61771yob, .table th.tinytable_css_tw0ndtwmjibn61771yob {    text-align: right; }
+.table td.tinytable_css_crvrnqmtrowgihs5pe5g, .table th.tinytable_css_crvrnqmtrowgihs5pe5g {    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 = {
@@ -3444,7 +3444,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_61gfvsq6e63e6kvnf1gv" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_m4yifvil1lpu998ia1a6" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -3470,12 +3470,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_61gfvsq6e63e6kvnf1gv");
+      function styleCell_tinytable_7op32yqsafltm2szlg4x(i, j, css_id) {
+        var table = document.getElementById("tinytable_m4yifvil1lpu998ia1a6");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_61gfvsq6e63e6kvnf1gv');
+        var table = document.getElementById('tinytable_m4yifvil1lpu998ia1a6');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3483,8 +3483,8 @@ <h1 class="title">Data Summaries</h1>
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-      function spanCell_tinytable_ajrwd10hhiysowhud2ga(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_61gfvsq6e63e6kvnf1gv");
+      function spanCell_tinytable_7op32yqsafltm2szlg4x(i, j, rowspan, colspan) {
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+window.addEventListener('load', function () { styleCell_tinytable_7op32yqsafltm2szlg4x(2, 4, 'tinytable_css_tw0ndtwmjibn61771yob') })
+window.addEventListener('load', function () { styleCell_tinytable_7op32yqsafltm2szlg4x(0, 0, 'tinytable_css_crvrnqmtrowgihs5pe5g') })
+window.addEventListener('load', function () { styleCell_tinytable_7op32yqsafltm2szlg4x(0, 1, 'tinytable_css_crvrnqmtrowgihs5pe5g') })
+window.addEventListener('load', function () { styleCell_tinytable_7op32yqsafltm2szlg4x(0, 2, 'tinytable_css_crvrnqmtrowgihs5pe5g') })
+window.addEventListener('load', function () { styleCell_tinytable_7op32yqsafltm2szlg4x(0, 3, 'tinytable_css_crvrnqmtrowgihs5pe5g') })
+window.addEventListener('load', function () { styleCell_tinytable_7op32yqsafltm2szlg4x(0, 4, 'tinytable_css_crvrnqmtrowgihs5pe5g') })
     </script>
 </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_5g2gknzl8ajp7jr2aw6y</title>
+<title>tinytable_ey8pqh5qs8j5jvmhicf7</title>
 <style>
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-.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; }
+.table td.tinytable_css_eekwn7vx0pdvt4dnxa0u, .table th.tinytable_css_eekwn7vx0pdvt4dnxa0u {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_7o3w6m17ylvi3cu411tq, .table th.tinytable_css_7o3w6m17ylvi3cu411tq {    text-align: left; }
+.table td.tinytable_css_2bgvm3sizg8y3vh7j1hy, .table th.tinytable_css_2bgvm3sizg8y3vh7j1hy {    text-align: left; }
+.table td.tinytable_css_4vssvyj6kdumbpb6sfp7, .table th.tinytable_css_4vssvyj6kdumbpb6sfp7 {    text-align: right; }
+.table td.tinytable_css_gujnw1bv485e6uis32v5, .table th.tinytable_css_gujnw1bv485e6uis32v5 {    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 = {
@@ -3574,7 +3574,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_5g2gknzl8ajp7jr2aw6y" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ey8pqh5qs8j5jvmhicf7" 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>
 </div>
 
     <script>
-      function styleCell_tinytable_lmuv3ecibr0lbnvao3wt(i, j, css_id) {
-        var table = document.getElementById("tinytable_5g2gknzl8ajp7jr2aw6y");
+      function styleCell_tinytable_o924y5v0ge1rqkp3099v(i, j, css_id) {
+        var table = document.getElementById("tinytable_ey8pqh5qs8j5jvmhicf7");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_5g2gknzl8ajp7jr2aw6y');
+        var table = document.getElementById('tinytable_ey8pqh5qs8j5jvmhicf7');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -3624,8 +3624,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_lmuv3ecibr0lbnvao3wt(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_5g2gknzl8ajp7jr2aw6y");
+      function spanCell_tinytable_o924y5v0ge1rqkp3099v(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ey8pqh5qs8j5jvmhicf7");
         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>
         targetCell.colSpan = colspan;
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+      <table class="table table-borderless" id="tinytable_elradgsleqqzq2dnm9z0" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -4381,12 +4381,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_4lg5ajybsnt7mc21r5tt");
+      function styleCell_tinytable_ahrjrwd748l87c53tsxi(i, j, css_id) {
+        var table = document.getElementById("tinytable_elradgsleqqzq2dnm9z0");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_4lg5ajybsnt7mc21r5tt');
+        var table = document.getElementById('tinytable_elradgsleqqzq2dnm9z0');
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         newCell.setAttribute("colspan", colspan);
@@ -4394,8 +4394,8 @@ <h1 class="title">Data Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_ewsbqedjqx5jiqvda85t(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_4lg5ajybsnt7mc21r5tt");
+      function spanCell_tinytable_ahrjrwd748l87c53tsxi(i, j, rowspan, colspan) {
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@@ -4422,58 +4422,58 @@ <h1 class="title">Data Summaries</h1>
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@@ -4487,19 +4487,19 @@ <h1 class="title">Data Summaries</h1>
   
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+.table td.tinytable_css_qvvygdwowfysgltzhyq5, .table th.tinytable_css_qvvygdwowfysgltzhyq5 {    text-align: right; }
+.table td.tinytable_css_j3xpanyc8r56amfhq6dk, .table th.tinytable_css_j3xpanyc8r56amfhq6dk {    text-align: right; }
+.table td.tinytable_css_9bzli8pm3f6m1vx8lgcs, .table th.tinytable_css_9bzli8pm3f6m1vx8lgcs {    text-align: center; }
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@@ -4511,7 +4511,7 @@ <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">
+      <table class="table table-borderless" id="tinytable_mk2hpc9z6m2zcgucz5xh" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 <tr>
 <th scope="col" align="center" colspan="1"> </th>
@@ -4553,12 +4553,12 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_prmzsvq6vsc6j93abpup");
+      function styleCell_tinytable_mknd0trqeuyc86u3a66t(i, j, css_id) {
+        var table = document.getElementById("tinytable_mk2hpc9z6m2zcgucz5xh");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_prmzsvq6vsc6j93abpup');
+        var table = document.getElementById('tinytable_mk2hpc9z6m2zcgucz5xh');
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@@ -4659,13 +4659,13 @@ <h1 class="title">Data Summaries</h1>
   
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     MathJax = {
@@ -4677,7 +4677,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_shg3lq1fzassz2vtqz4c" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_n3lurag5cccmmajojyk2" 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>
@@ -4720,12 +4720,12 @@ <h1 class="title">Data Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_shg3lq1fzassz2vtqz4c");
+      function styleCell_tinytable_gcrnes46uefuf0ggixj2(i, j, css_id) {
+        var table = document.getElementById("tinytable_n3lurag5cccmmajojyk2");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_shg3lq1fzassz2vtqz4c');
+        var table = document.getElementById('tinytable_n3lurag5cccmmajojyk2');
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         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
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-      function spanCell_tinytable_d6q3fvpcyw9t7c1alsv2(i, j, rowspan, colspan) {
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+      function spanCell_tinytable_gcrnes46uefuf0ggixj2(i, j, rowspan, colspan) {
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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_1jwng5p5tqqkn755em2n</title>
+<title>tinytable_ck5sgz7fpby2tzdv1tmf</title>
 <style>
-.table td.tinytable_css_gtmgie6r8ocxp4mm1qdh, .table th.tinytable_css_gtmgie6r8ocxp4mm1qdh {    border-bottom: solid 0.1em #d3d8dc; }
-.table td.tinytable_css_7t2txy07hcl7sxitwfkn, .table th.tinytable_css_7t2txy07hcl7sxitwfkn {    text-align: left; }
-.table td.tinytable_css_10s24v8rdu6y6vrsxwbc, .table th.tinytable_css_10s24v8rdu6y6vrsxwbc {    text-align: left; }
-.table td.tinytable_css_uqr59y206oo509w2eyp6, .table th.tinytable_css_uqr59y206oo509w2eyp6 {    text-align: right; }
-.table td.tinytable_css_4wlrqkxi9uhnr3pgs25i, .table th.tinytable_css_4wlrqkxi9uhnr3pgs25i {    text-align: right; }
+.table td.tinytable_css_moshdk99pza3cpou375e, .table th.tinytable_css_moshdk99pza3cpou375e {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_hgw70tntjszt495fuiw0, .table th.tinytable_css_hgw70tntjszt495fuiw0 {    text-align: left; }
+.table td.tinytable_css_k6qks9ugj4eiranexwck, .table th.tinytable_css_k6qks9ugj4eiranexwck {    text-align: left; }
+.table td.tinytable_css_9wmtlkmq2oaj6h0qnhke, .table th.tinytable_css_9wmtlkmq2oaj6h0qnhke {    text-align: right; }
+.table td.tinytable_css_hofmix4nq9od0nl994di, .table th.tinytable_css_hofmix4nq9od0nl994di {    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 = {
@@ -4817,7 +4817,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_1jwng5p5tqqkn755em2n" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ck5sgz7fpby2tzdv1tmf" 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>
 
     <script>
-      function styleCell_tinytable_vwg4vnao7nsnivcnw0el(i, j, css_id) {
-        var table = document.getElementById("tinytable_1jwng5p5tqqkn755em2n");
+      function styleCell_tinytable_jorxvuj8tmeelphehap9(i, j, css_id) {
+        var table = document.getElementById("tinytable_ck5sgz7fpby2tzdv1tmf");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_1jwng5p5tqqkn755em2n');
+        var table = document.getElementById('tinytable_ck5sgz7fpby2tzdv1tmf');
         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;
       }
-      function spanCell_tinytable_vwg4vnao7nsnivcnw0el(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_1jwng5p5tqqkn755em2n");
+      function spanCell_tinytable_jorxvuj8tmeelphehap9(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ck5sgz7fpby2tzdv1tmf");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -4901,34 +4901,34 @@ <h1 class="title">Data Summaries</h1>
         targetCell.colSpan = colspan;
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+.table td.tinytable_css_9ek9bqh59pi1c84eg6t2, .table th.tinytable_css_9ek9bqh59pi1c84eg6t2 {    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 = {
@@ -5131,7 +5131,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_0mder98ikpie4acsvb3e" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ugg61a2fcw5fh73ssepo" 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>
 
     <script>
-      function styleCell_tinytable_kp5kvztenb0hiwkxz892(i, j, css_id) {
-        var table = document.getElementById("tinytable_0mder98ikpie4acsvb3e");
+      function styleCell_tinytable_f8r4ehvjzlk20buovvnr(i, j, css_id) {
+        var table = document.getElementById("tinytable_ugg61a2fcw5fh73ssepo");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_0mder98ikpie4acsvb3e');
+        var table = document.getElementById('tinytable_ugg61a2fcw5fh73ssepo');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -5199,8 +5199,8 @@ <h1 class="title">Data Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_kp5kvztenb0hiwkxz892(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_0mder98ikpie4acsvb3e");
+      function spanCell_tinytable_f8r4ehvjzlk20buovvnr(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ugg61a2fcw5fh73ssepo");
         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_f8r4ehvjzlk20buovvnr(0, 3, 'tinytable_css_ebgoetmjc613mqtq3qly') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(0, 0, 'tinytable_css_s8q0j092u1sz1r0e3uy9') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(1, 0, 'tinytable_css_s8q0j092u1sz1r0e3uy9') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(2, 0, 'tinytable_css_s8q0j092u1sz1r0e3uy9') })
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+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(3, 2, 'tinytable_css_di7arwdyqxhatppff38l') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(4, 2, 'tinytable_css_di7arwdyqxhatppff38l') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(5, 2, 'tinytable_css_di7arwdyqxhatppff38l') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(6, 2, 'tinytable_css_di7arwdyqxhatppff38l') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(7, 2, 'tinytable_css_di7arwdyqxhatppff38l') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(0, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(1, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(2, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(3, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(4, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(5, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(6, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
+window.addEventListener('load', function () { styleCell_tinytable_f8r4ehvjzlk20buovvnr(7, 3, 'tinytable_css_9ek9bqh59pi1c84eg6t2') })
     </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_bk8prpbmxl80j6xwm16l</title>
+<title>tinytable_ncj0pge4avt822em8vkz</title>
 <style>
-.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; }
+.table td.tinytable_css_diagdbvl0qffpgqonr40, .table th.tinytable_css_diagdbvl0qffpgqonr40 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_l6rvjhwk0tfm6c8sz687, .table th.tinytable_css_l6rvjhwk0tfm6c8sz687 {    text-align: left; }
+.table td.tinytable_css_34tasgkvetc0wpyp0h61, .table th.tinytable_css_34tasgkvetc0wpyp0h61 {    text-align: left; }
+.table td.tinytable_css_kfmypcls1y66d3zmable, .table th.tinytable_css_kfmypcls1y66d3zmable {    text-align: right; }
+.table td.tinytable_css_vn9dglwedoqgy8xa5yl5, .table th.tinytable_css_vn9dglwedoqgy8xa5yl5 {    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 = {
@@ -5296,7 +5296,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_bk8prpbmxl80j6xwm16l" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ncj0pge4avt822em8vkz" 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_ugpuje4q1v2lsy5ie9f6(i, j, css_id) {
-        var table = document.getElementById("tinytable_bk8prpbmxl80j6xwm16l");
+      function styleCell_tinytable_br34h8696yb65zmxsgfg(i, j, css_id) {
+        var table = document.getElementById("tinytable_ncj0pge4avt822em8vkz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_bk8prpbmxl80j6xwm16l');
+        var table = document.getElementById('tinytable_ncj0pge4avt822em8vkz');
         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_ugpuje4q1v2lsy5ie9f6(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_bk8prpbmxl80j6xwm16l");
+      function spanCell_tinytable_br34h8696yb65zmxsgfg(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ncj0pge4avt822em8vkz");
         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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@@ -5708,12 +5708,12 @@ <h1 class="title">Data Summaries</h1>
   
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       }
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     </script><div class="container">
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+      <table class="table table-borderless" id="tinytable_76pbdi9rf9pge3ij6nba" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -5740,12 +5740,12 @@ <h1 class="title">Data Summaries</h1>
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+      function styleCell_tinytable_7egkvi2w2g0jgeod9qxk(i, j, css_id) {
+        var table = document.getElementById("tinytable_76pbdi9rf9pge3ij6nba");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_xb3z1hpx3lbrxr80f6d0');
+        var table = document.getElementById('tinytable_76pbdi9rf9pge3ij6nba');
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         var newCell = newRow.insertCell(0);
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-        var table = document.getElementById("tinytable_xb3z1hpx3lbrxr80f6d0");
+      function spanCell_tinytable_7egkvi2w2g0jgeod9qxk(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_76pbdi9rf9pge3ij6nba");
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@@ -5781,15 +5781,15 @@ <h1 class="title">Data Summaries</h1>
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@@ -5804,12 +5804,12 @@ <h1 class="title">Data Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+<title>tinytable_759htqlplz69gmk90hpj</title>
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     MathJax = {
@@ -5821,7 +5821,7 @@ <h1 class="title">Data Summaries</h1>
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_c9pct73bay4ctr12laor" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_759htqlplz69gmk90hpj" 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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-        var table = document.getElementById("tinytable_c9pct73bay4ctr12laor");
+      function styleCell_tinytable_bvm55yjwzuktapjuwi4u(i, j, css_id) {
+        var table = document.getElementById("tinytable_759htqlplz69gmk90hpj");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_c9pct73bay4ctr12laor');
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@@ -6104,22 +6104,22 @@ <h1 class="title">Data Summaries</h1>
 <div class="cell">
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 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 1.315068</code></pre>
+<pre><code>[1] 0.2528909</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] -0.236075</code></pre>
+<pre><code>[1] 0.8139608</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">$</span><span class="va">x</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 0.3049217</code></pre>
+<pre><code>[1] -0.07968538</code></pre>
 </div>
 <div class="sourceCode" id="cb46"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">newdata</span><span class="op">$</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] -0.2411163</code></pre>
+<pre><code>[1] 0.03824354</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_i4taciijcbvi2p1ce0gp" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ylbwte7zkkjwyuvh1iwf" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -6790,12 +6790,12 @@ <h1 class="title">Data Summaries</h1>
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-      function styleCell_tinytable_2qn5klq1rf54rsaieg69(i, j, css_id) {
-        var table = document.getElementById("tinytable_i4taciijcbvi2p1ce0gp");
+      function styleCell_tinytable_9owauygn1zcsvmioo4dl(i, j, css_id) {
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@@ -6872,7 +6872,7 @@ <h1 class="title">Data Summaries</h1>
       }
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-      <table class="table table-borderless" id="tinytable_fke4aukdx87ay8s90e40" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_jgvmg1nxadjkcudfnifi" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -6894,12 +6894,12 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_fke4aukdx87ay8s90e40");
+      function styleCell_tinytable_55i7l9onh64wzq99z9av(i, j, css_id) {
+        var table = document.getElementById("tinytable_jgvmg1nxadjkcudfnifi");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_fke4aukdx87ay8s90e40');
+        var table = document.getElementById('tinytable_jgvmg1nxadjkcudfnifi');
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         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -6907,8 +6907,8 @@ <h1 class="title">Data Summaries</h1>
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@@ -6968,19 +6968,19 @@ <h1 class="title">Data Summaries</h1>
   
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@@ -7043,12 +7043,12 @@ <h1 class="title">Data Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_5q567l8nxv8yhtt47pdl" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -7469,12 +7469,12 @@ <h1 class="title">Data Summaries</h1>
   
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@@ -7486,7 +7486,7 @@ <h1 class="title">Data Summaries</h1>
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-      <table class="table table-borderless" id="tinytable_337coon4mk3qsa9fdg8f" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_b63qkmjd3gmcbc5jfap4" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -7501,12 +7501,12 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_337coon4mk3qsa9fdg8f");
+      function styleCell_tinytable_5pns1lkbj96fmpj6kpcc(i, j, css_id) {
+        var table = document.getElementById("tinytable_b63qkmjd3gmcbc5jfap4");
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-        var table = document.getElementById('tinytable_337coon4mk3qsa9fdg8f');
+        var table = document.getElementById('tinytable_b63qkmjd3gmcbc5jfap4');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -7514,8 +7514,8 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_337coon4mk3qsa9fdg8f");
+      function spanCell_tinytable_5pns1lkbj96fmpj6kpcc(i, j, rowspan, colspan) {
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+window.addEventListener('load', function () { styleCell_tinytable_5pns1lkbj96fmpj6kpcc(0, 0, 'tinytable_css_lxf5xow4tjss4c464qzh') })
+window.addEventListener('load', function () { styleCell_tinytable_5pns1lkbj96fmpj6kpcc(1, 0, 'tinytable_css_lxf5xow4tjss4c464qzh') })
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+window.addEventListener('load', function () { styleCell_tinytable_5pns1lkbj96fmpj6kpcc(1, 1, 'tinytable_css_1c0tqe5yygf60m3icwsy') })
+window.addEventListener('load', function () { styleCell_tinytable_5pns1lkbj96fmpj6kpcc(0, 2, 'tinytable_css_l53jcoqsw1xg09lirsth') })
+window.addEventListener('load', function () { styleCell_tinytable_5pns1lkbj96fmpj6kpcc(1, 2, 'tinytable_css_l53jcoqsw1xg09lirsth') })
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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_m7m8ed84j2a9tptbeswu</title>
+<title>tinytable_ljd8ndzsoknxp9yudxt7</title>
 <style>
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+.table td.tinytable_css_f77a8urg0rde3zbktyi6, .table th.tinytable_css_f77a8urg0rde3zbktyi6 {    text-align: left; }
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     MathJax = {
@@ -7581,7 +7581,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_m7m8ed84j2a9tptbeswu" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ljd8ndzsoknxp9yudxt7" 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>332,919</td>
+                  <td>333,802</td>
                 </tr>
 <tr>
 <td>b</td>
-                  <td>333,564</td>
+                  <td>332,990</td>
                 </tr>
 <tr>
 <td>c</td>
-                  <td>333,517</td>
+                  <td>333,208</td>
                 </tr>
 </tbody>
 </table>
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-        var table = document.getElementById("tinytable_m7m8ed84j2a9tptbeswu");
+      function styleCell_tinytable_fn1bhck6pkn41hnmv3uh(i, j, css_id) {
+        var table = document.getElementById("tinytable_ljd8ndzsoknxp9yudxt7");
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       function insertSpanRow(i, colspan, content) {
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+        var table = document.getElementById('tinytable_ljd8ndzsoknxp9yudxt7');
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         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -7617,8 +7617,8 @@ <h1 class="title">Data Summaries</h1>
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@@ -7645,16 +7645,16 @@ <h1 class="title">Data Summaries</h1>
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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_993pqqsi04yuatltlpsh</title>
+<title>tinytable_vimww3kczuljuhamnc25</title>
 <style>
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-.table td.tinytable_css_oau1ctj260661rrovtku, .table th.tinytable_css_oau1ctj260661rrovtku {    text-align: right; }
+.table td.tinytable_css_k1bu9vcfu7p0bj4iqt7p, .table th.tinytable_css_k1bu9vcfu7p0bj4iqt7p {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_txsoieei4x9gqnsepje8, .table th.tinytable_css_txsoieei4x9gqnsepje8 {    text-align: left; }
+.table td.tinytable_css_q5g9g853rrjwt60djikx, .table th.tinytable_css_q5g9g853rrjwt60djikx {    text-align: right; }
+.table td.tinytable_css_kb66j7jpsqseoods7udl, .table th.tinytable_css_kb66j7jpsqseoods7udl {    text-align: right; }
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     MathJax = {
@@ -7688,7 +7688,7 @@ <h1 class="title">Data Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_993pqqsi04yuatltlpsh" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_vimww3kczuljuhamnc25" 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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-      function styleCell_tinytable_s8sfpr8m48c6cj28lfm9(i, j, css_id) {
-        var table = document.getElementById("tinytable_993pqqsi04yuatltlpsh");
+      function styleCell_tinytable_mkzgvpjnj14uje96zuoa(i, j, css_id) {
+        var table = document.getElementById("tinytable_vimww3kczuljuhamnc25");
         table.rows[i].cells[j].classList.add(css_id);
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_993pqqsi04yuatltlpsh');
+        var table = document.getElementById('tinytable_vimww3kczuljuhamnc25');
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@@ -7747,8 +7747,8 @@ <h1 class="title">Data Summaries</h1>
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-        var table = document.getElementById("tinytable_993pqqsi04yuatltlpsh");
+      function spanCell_tinytable_mkzgvpjnj14uje96zuoa(i, j, rowspan, colspan) {
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         const targetRow = table.rows[i];
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@@ -7775,30 +7775,30 @@ <h1 class="title">Data Summaries</h1>
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 <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_x3w3330a5tfs1pnwm7x4');
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         var newCell = newRow.insertCell(0);
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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 a68dce7c2..f56e87eeb 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_5s5s0ho4i0d11mopqcm0</title>
+<title>tinytable_fu41m92s1ycg9slfxym7</title>
 <style>
-.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; }
+.table td.tinytable_css_tsv1n21yhu446znc1wzc, .table th.tinytable_css_tsv1n21yhu446znc1wzc {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_wl5uc0h7m9fznlcd2tbi, .table th.tinytable_css_wl5uc0h7m9fznlcd2tbi {    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_5s5s0ho4i0d11mopqcm0" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_fu41m92s1ycg9slfxym7" 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/idhb8vbjg7cxgojustlo00.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idruwrdfp1vfhcj5ks8dg1.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/idorwn5dl4dwudwz3kb7dm.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/id7gq3p7xrmxzgz3czxz5e.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/idx8yn8l8vz8kjq5yxmqzz.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idw88w31jtcs29h0jx3a7y.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/idvfj107zcc181ae9yhm6c.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/idpf8ieywfpep7fblh2nxg.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/idixxqslczpg9thoz97s4n.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/ido6vuqjrohozlglxc3oah.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/id3tsb3ss7el2y7v3o6n6m.png" style="height: 1em;"></td>
+                  <td><img src="./tinytable_assets/id0514m942vvnijlnihcrn.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_s59lhv5bxhaqj816axkr(i, j, css_id) {
-        var table = document.getElementById("tinytable_5s5s0ho4i0d11mopqcm0");
+      function styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(i, j, css_id) {
+        var table = document.getElementById("tinytable_fu41m92s1ycg9slfxym7");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_5s5s0ho4i0d11mopqcm0');
+        var table = document.getElementById('tinytable_fu41m92s1ycg9slfxym7');
         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_s59lhv5bxhaqj816axkr(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_5s5s0ho4i0d11mopqcm0");
+      function spanCell_tinytable_1sy9fdhhlt6t7n0k3z63(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_fu41m92s1ycg9slfxym7");
         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_1sy9fdhhlt6t7n0k3z63(0, 1, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(0, 2, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(0, 3, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(0, 4, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(0, 5, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(0, 6, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(0, 7, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(0, 8, 'tinytable_css_tsv1n21yhu446znc1wzc') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 0, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 1, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 2, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 3, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 4, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 5, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 6, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 7, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
+window.addEventListener('load', function () { styleCell_tinytable_1sy9fdhhlt6t7n0k3z63(7, 8, 'tinytable_css_wl5uc0h7m9fznlcd2tbi') })
     </script>
 </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_l9y4jxu9shckkefbll5k</title>
+<title>tinytable_jix3v2s3wx24jey8dms4</title>
 <style>
-.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; }
+.table td.tinytable_css_8dmr6wi5a1garavi0txr, .table th.tinytable_css_8dmr6wi5a1garavi0txr {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_da2pb49hng9non6o8pnr, .table th.tinytable_css_da2pb49hng9non6o8pnr {    text-align: center; }
+.table td.tinytable_css_itq4hglkm7jybc45dxvm, .table th.tinytable_css_itq4hglkm7jybc45dxvm {    border-bottom: solid 0.05em #d3d8dc; }
+.table td.tinytable_css_n6vp5ci0nnosp07kvsd1, .table th.tinytable_css_n6vp5ci0nnosp07kvsd1 {    text-align: left; }
+.table td.tinytable_css_osctwurxgwowirkxkctg, .table th.tinytable_css_osctwurxgwowirkxkctg {    text-align: right; }
+.table td.tinytable_css_hqueo3htzm7wanpx5de1, .table th.tinytable_css_hqueo3htzm7wanpx5de1 {    text-align: right; }
+.table td.tinytable_css_0lr99xowm05criqtnhcn, .table th.tinytable_css_0lr99xowm05criqtnhcn {    text-align: right; }
+.table td.tinytable_css_63gjorelb2cbys9hoefz, .table th.tinytable_css_63gjorelb2cbys9hoefz {    text-align: right; }
+.table td.tinytable_css_07opb7z7q8gupnpz1hx8, .table th.tinytable_css_07opb7z7q8gupnpz1hx8 {    text-align: right; }
+.table td.tinytable_css_esgc561238ywydrnqin8, .table th.tinytable_css_esgc561238ywydrnqin8 {    text-align: right; }
+.table td.tinytable_css_ixrgtjxidq9ts79xnubl, .table th.tinytable_css_ixrgtjxidq9ts79xnubl {    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_l9y4jxu9shckkefbll5k" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_jix3v2s3wx24jey8dms4" 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_aytqd2l1z2ncifcp4z1r(i, j, css_id) {
-        var table = document.getElementById("tinytable_l9y4jxu9shckkefbll5k");
+      function styleCell_tinytable_dezsxgu28lji0w7q25du(i, j, css_id) {
+        var table = document.getElementById("tinytable_jix3v2s3wx24jey8dms4");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_l9y4jxu9shckkefbll5k');
+        var table = document.getElementById('tinytable_jix3v2s3wx24jey8dms4');
         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_aytqd2l1z2ncifcp4z1r(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_l9y4jxu9shckkefbll5k");
+      function spanCell_tinytable_dezsxgu28lji0w7q25du(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_jix3v2s3wx24jey8dms4");
         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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@@ -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_8zpjnjnivhvk23crvyu3</title>
+<title>tinytable_6p6juxs5bbgjbyf563kj</title>
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+.table td.tinytable_css_8z64peyl1z6h2j2scls8, .table th.tinytable_css_8z64peyl1z6h2j2scls8 {    text-align: right; }
+.table td.tinytable_css_uni6honxwrniwfz2xzzu, .table th.tinytable_css_uni6honxwrniwfz2xzzu {    text-align: right; }
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+.table td.tinytable_css_kucdl4e18jmva0djgjmt, .table th.tinytable_css_kucdl4e18jmva0djgjmt {    text-align: right; }
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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>
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@@ -873,7 +873,7 @@ <h1 class="title">Getting started</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_8zpjnjnivhvk23crvyu3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_6p6juxs5bbgjbyf563kj" 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>
 
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-      function styleCell_tinytable_zz73box012pzt4hwcknv(i, j, css_id) {
-        var table = document.getElementById("tinytable_8zpjnjnivhvk23crvyu3");
+      function styleCell_tinytable_1ucvoghuln6rgzmo8ghn(i, j, css_id) {
+        var table = document.getElementById("tinytable_6p6juxs5bbgjbyf563kj");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_8zpjnjnivhvk23crvyu3');
+        var table = document.getElementById('tinytable_6p6juxs5bbgjbyf563kj');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -956,8 +956,8 @@ <h1 class="title">Getting started</h1>
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         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_zz73box012pzt4hwcknv(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_8zpjnjnivhvk23crvyu3");
+      function spanCell_tinytable_1ucvoghuln6rgzmo8ghn(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_6p6juxs5bbgjbyf563kj");
         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>
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@@ -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_h3xk51iw9talhwi0q5um</title>
+<title>tinytable_11gc7k5h1ifepyq47x4q</title>
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-.table td.tinytable_css_iocq30pnf2wzaubdfo2i, .table th.tinytable_css_iocq30pnf2wzaubdfo2i {    text-align: center; }
+.table td.tinytable_css_jsw51zwdvbcjqbp2wul6, .table th.tinytable_css_jsw51zwdvbcjqbp2wul6 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_d0evrp3a0e12zi3sbsj6, .table th.tinytable_css_d0evrp3a0e12zi3sbsj6 {    text-align: center; }
+.table td.tinytable_css_0075x5lgkgprh6wdnszz, .table th.tinytable_css_0075x5lgkgprh6wdnszz {    border-bottom: solid 0.05em #d3d8dc; }
+.table td.tinytable_css_mz8lbnv7jl7o86euvztk, .table th.tinytable_css_mz8lbnv7jl7o86euvztk {    text-align: left; }
+.table td.tinytable_css_3zbtwzhbefu2s5jlgqkh, .table th.tinytable_css_3zbtwzhbefu2s5jlgqkh {    text-align: right; }
+.table td.tinytable_css_nnpplc48gcwsafletsr4, .table th.tinytable_css_nnpplc48gcwsafletsr4 {    text-align: right; }
+.table td.tinytable_css_80ap8srdn6vw0x18kgda, .table th.tinytable_css_80ap8srdn6vw0x18kgda {    text-align: right; }
+.table td.tinytable_css_p48ened4bdcugll0lap7, .table th.tinytable_css_p48ened4bdcugll0lap7 {    text-align: right; }
+.table td.tinytable_css_pp4e57a77ffekgnlysze, .table th.tinytable_css_pp4e57a77ffekgnlysze {    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_h3xk51iw9talhwi0q5um" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_11gc7k5h1ifepyq47x4q" 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_zipr10i9rfp2genf6xpt(i, j, css_id) {
-        var table = document.getElementById("tinytable_h3xk51iw9talhwi0q5um");
+      function styleCell_tinytable_oiiir1xqu4p1sopd2ib5(i, j, css_id) {
+        var table = document.getElementById("tinytable_11gc7k5h1ifepyq47x4q");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_h3xk51iw9talhwi0q5um');
+        var table = document.getElementById('tinytable_11gc7k5h1ifepyq47x4q');
         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_zipr10i9rfp2genf6xpt(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_h3xk51iw9talhwi0q5um");
+      function spanCell_tinytable_oiiir1xqu4p1sopd2ib5(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_11gc7k5h1ifepyq47x4q");
         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_oiiir1xqu4p1sopd2ib5(0, 1, 'tinytable_css_pp4e57a77ffekgnlysze') })
+window.addEventListener('load', function () { styleCell_tinytable_oiiir1xqu4p1sopd2ib5(0, 2, 'tinytable_css_pp4e57a77ffekgnlysze') })
+window.addEventListener('load', function () { styleCell_tinytable_oiiir1xqu4p1sopd2ib5(0, 3, 'tinytable_css_pp4e57a77ffekgnlysze') })
+window.addEventListener('load', function () { styleCell_tinytable_oiiir1xqu4p1sopd2ib5(0, 4, 'tinytable_css_pp4e57a77ffekgnlysze') })
     </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_fa6zc8q2mjc0ixwi1rfq</title>
+<title>tinytable_tmbigvd1v3gu1torosoe</title>
 <style>
-.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; }
+.table td.tinytable_css_4sinv4hb2t4akjhpd7cp, .table th.tinytable_css_4sinv4hb2t4akjhpd7cp {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_ul718h7rg5a6fl5g3ryd, .table th.tinytable_css_ul718h7rg5a6fl5g3ryd {    text-align: left; }
+.table td.tinytable_css_xva92x2zpu3blodvnep0, .table th.tinytable_css_xva92x2zpu3blodvnep0 {    text-align: center; }
+.table td.tinytable_css_5hfr7h2zb5gtxs2om7no, .table th.tinytable_css_5hfr7h2zb5gtxs2om7no {    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_fa6zc8q2mjc0ixwi1rfq" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_tmbigvd1v3gu1torosoe" 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_xfk6nvwvkm63632uq4to(i, j, css_id) {
-        var table = document.getElementById("tinytable_fa6zc8q2mjc0ixwi1rfq");
+      function styleCell_tinytable_g6mb45hq435wzbiijrk9(i, j, css_id) {
+        var table = document.getElementById("tinytable_tmbigvd1v3gu1torosoe");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_fa6zc8q2mjc0ixwi1rfq');
+        var table = document.getElementById('tinytable_tmbigvd1v3gu1torosoe');
         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_xfk6nvwvkm63632uq4to(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_fa6zc8q2mjc0ixwi1rfq");
+      function spanCell_tinytable_g6mb45hq435wzbiijrk9(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_tmbigvd1v3gu1torosoe");
         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_xfk6nvwvkm63632uq4to(4, 1, 'tinytable_css_7b9xp0hisw6kzi1pfwjx') })
+window.addEventListener('load', function () { styleCell_tinytable_g6mb45hq435wzbiijrk9(0, 0, 'tinytable_css_4sinv4hb2t4akjhpd7cp') })
+window.addEventListener('load', function () { styleCell_tinytable_g6mb45hq435wzbiijrk9(0, 1, 'tinytable_css_4sinv4hb2t4akjhpd7cp') })
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+window.addEventListener('load', function () { styleCell_tinytable_g6mb45hq435wzbiijrk9(1, 0, 'tinytable_css_ul718h7rg5a6fl5g3ryd') })
+window.addEventListener('load', function () { styleCell_tinytable_g6mb45hq435wzbiijrk9(2, 0, 'tinytable_css_ul718h7rg5a6fl5g3ryd') })
+window.addEventListener('load', function () { styleCell_tinytable_g6mb45hq435wzbiijrk9(3, 0, 'tinytable_css_ul718h7rg5a6fl5g3ryd') })
+window.addEventListener('load', function () { styleCell_tinytable_g6mb45hq435wzbiijrk9(4, 0, 'tinytable_css_ul718h7rg5a6fl5g3ryd') })
+window.addEventListener('load', function () { styleCell_tinytable_g6mb45hq435wzbiijrk9(5, 0, 'tinytable_css_ul718h7rg5a6fl5g3ryd') })
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@@ -1372,18 +1372,18 @@ <h1 class="title">Getting started</h1>
   
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 <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+<title>tinytable_0oye7fg5i25ioctkqqb4</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_quyfgid783myjatx00rr" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_0oye7fg5i25ioctkqqb4" 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_quyfgid783myjatx00rr");
+      function styleCell_tinytable_8ldyz4nkkgmo9wlzh2d8(i, j, css_id) {
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       function insertSpanRow(i, colspan, content) {
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+        var table = document.getElementById('tinytable_0oye7fg5i25ioctkqqb4');
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+      function spanCell_tinytable_8ldyz4nkkgmo9wlzh2d8(i, j, rowspan, colspan) {
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     </script>
 </div>
 </div>
diff --git a/vignettes/modelplot.html b/vignettes/modelplot.html
index a0bb0848b..76afa3c14 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_q482m33kcr59753r4d4r</title>
+<title>tinytable_6rwe5wzzrdn8m4gd12sz</title>
 <style>
-.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; }
+.table td.tinytable_css_lv6bccxnsuxa8b5qehg1, .table th.tinytable_css_lv6bccxnsuxa8b5qehg1 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_32lgzmkzo3pgiij47afi, .table th.tinytable_css_32lgzmkzo3pgiij47afi {    text-align: left; }
+.table td.tinytable_css_05dnhwatqp0hfivlv4fq, .table th.tinytable_css_05dnhwatqp0hfivlv4fq {    text-align: center; }
+.table td.tinytable_css_zduu7xs0kmacc0qjzfad, .table th.tinytable_css_zduu7xs0kmacc0qjzfad {    text-align: center; }
+.table td.tinytable_css_3yo6vmbozvmbi18qbkrg, .table th.tinytable_css_3yo6vmbozvmbi18qbkrg {    text-align: center; }
+.table td.tinytable_css_ziqyeyxyzkp45mdcwzsd, .table th.tinytable_css_ziqyeyxyzkp45mdcwzsd {    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_q482m33kcr59753r4d4r" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_6rwe5wzzrdn8m4gd12sz" 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_dxbrybcrudycrqmzkrpw(i, j, css_id) {
-        var table = document.getElementById("tinytable_q482m33kcr59753r4d4r");
+      function styleCell_tinytable_3p8kfghsrpdnoo7477l9(i, j, css_id) {
+        var table = document.getElementById("tinytable_6rwe5wzzrdn8m4gd12sz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_q482m33kcr59753r4d4r');
+        var table = document.getElementById('tinytable_6rwe5wzzrdn8m4gd12sz');
         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_dxbrybcrudycrqmzkrpw(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_q482m33kcr59753r4d4r");
+      function spanCell_tinytable_3p8kfghsrpdnoo7477l9(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_6rwe5wzzrdn8m4gd12sz");
         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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 </div>
diff --git a/vignettes/modelsummary.html b/vignettes/modelsummary.html
index 63adbd5d3..29eddedd2 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_99h4l9rdsyut9f7et9pf</title>
+<title>tinytable_85prb6brv9r2i92zsojz</title>
 <style>
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-.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; }
+.table td.tinytable_css_npqh1613ehe1gko1cu09, .table th.tinytable_css_npqh1613ehe1gko1cu09 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_e44ag6m7msg0290fhcyf, .table th.tinytable_css_e44ag6m7msg0290fhcyf {    text-align: left; }
+.table td.tinytable_css_0dfaohfc8qktal0gi7pt, .table th.tinytable_css_0dfaohfc8qktal0gi7pt {    text-align: center; }
+.table td.tinytable_css_u4n5a98q7pxqete96d86, .table th.tinytable_css_u4n5a98q7pxqete96d86 {    text-align: center; }
+.table td.tinytable_css_okjfmbawb26y0fgdj0yx, .table th.tinytable_css_okjfmbawb26y0fgdj0yx {    text-align: center; }
+.table td.tinytable_css_79jmspkhmr4222o2vhs6, .table th.tinytable_css_79jmspkhmr4222o2vhs6 {    border-bottom: solid 0.05em black; }
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     MathJax = {
@@ -501,7 +501,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_99h4l9rdsyut9f7et9pf" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_85prb6brv9r2i92zsojz" 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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-        var table = document.getElementById("tinytable_99h4l9rdsyut9f7et9pf");
+      function styleCell_tinytable_4uvq8dp3nk9wq5otpams(i, j, css_id) {
+        var table = document.getElementById("tinytable_85prb6brv9r2i92zsojz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_99h4l9rdsyut9f7et9pf');
+        var table = document.getElementById('tinytable_85prb6brv9r2i92zsojz');
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@@ -623,8 +623,8 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_99h4l9rdsyut9f7et9pf");
+      function spanCell_tinytable_4uvq8dp3nk9wq5otpams(i, j, rowspan, colspan) {
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@@ -651,82 +651,82 @@ <h1 class="title">Model Summaries</h1>
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@@ -961,7 +961,7 @@ <h1 class="title">Model Summaries</h1>
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+      <table class="table table-borderless" id="tinytable_k1lsveagj329uqoocvce" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -1040,12 +1040,12 @@ <h1 class="title">Model Summaries</h1>
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@@ -1154,12 +1154,12 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
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+<title>tinytable_rcaipjc1xzb54s94tazd</title>
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+.table td.tinytable_css_6ujbwxi3us5qlytwf3rt, .table th.tinytable_css_6ujbwxi3us5qlytwf3rt {    text-align: left; }
+.table td.tinytable_css_th1fj7q8uj5ucu01nprd, .table th.tinytable_css_th1fj7q8uj5ucu01nprd {    text-align: center; }
+.table td.tinytable_css_9uplcks9kdd6ru8bpod3, .table th.tinytable_css_9uplcks9kdd6ru8bpod3 {    border-bottom: solid 0.05em black; }
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_dxxyqjn6le9wlx5095rz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_rcaipjc1xzb54s94tazd" 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_dxxyqjn6le9wlx5095rz");
+      function styleCell_tinytable_d9sw0vipnh6zotyi95yk(i, j, css_id) {
+        var table = document.getElementById("tinytable_rcaipjc1xzb54s94tazd");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_dxxyqjn6le9wlx5095rz');
+        var table = document.getElementById('tinytable_rcaipjc1xzb54s94tazd');
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-      function spanCell_tinytable_wyzmhtegtf8chvs34nux(i, j, rowspan, colspan) {
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+      function spanCell_tinytable_d9sw0vipnh6zotyi95yk(i, j, rowspan, colspan) {
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@@ -1308,14 +1308,14 @@ <h1 class="title">Model Summaries</h1>
   
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 <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+<title>tinytable_fs3dpzb08binnqf5vor0</title>
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-.table td.tinytable_css_zyaphs1g59yyhkiodcwt, .table th.tinytable_css_zyaphs1g59yyhkiodcwt {    border-bottom: solid 0.05em black; }
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+.table td.tinytable_css_kcymei1y3ma8bhn1mvbh, .table th.tinytable_css_kcymei1y3ma8bhn1mvbh {    text-align: left; }
+.table td.tinytable_css_4k2i2aq6zjt0cdccawdq, .table th.tinytable_css_4k2i2aq6zjt0cdccawdq {    text-align: center; }
+.table td.tinytable_css_q5v3qub22jr3db98f3a7, .table th.tinytable_css_q5v3qub22jr3db98f3a7 {    text-align: center; }
+.table td.tinytable_css_ss130ef05rmkrpgxs82a, .table th.tinytable_css_ss130ef05rmkrpgxs82a {    text-align: center; }
+.table td.tinytable_css_8x9ns6yas5ibwjzg36ul, .table th.tinytable_css_8x9ns6yas5ibwjzg36ul {    border-bottom: solid 0.05em black; }
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     MathJax = {
@@ -1327,7 +1327,7 @@ <h1 class="title">Model Summaries</h1>
       }
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     </script><div class="container">
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+      <table class="table table-borderless" id="tinytable_fs3dpzb08binnqf5vor0" 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_2us8mde8hmx62g7kpd5f");
+      function styleCell_tinytable_yj3vi6jrdjjjeyw3op39(i, j, css_id) {
+        var table = document.getElementById("tinytable_fs3dpzb08binnqf5vor0");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_2us8mde8hmx62g7kpd5f');
+        var table = document.getElementById('tinytable_fs3dpzb08binnqf5vor0');
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         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -1419,8 +1419,8 @@ <h1 class="title">Model Summaries</h1>
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         newCell.innerHTML = content;
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-        var table = document.getElementById("tinytable_2us8mde8hmx62g7kpd5f");
+      function spanCell_tinytable_yj3vi6jrdjjjeyw3op39(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_86moeycncdqrycp33a8h" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -2403,12 +2403,12 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_wcbil11z8fusk24kgxkq");
+      function styleCell_tinytable_asgwhpy0w76howk4j8bx(i, j, css_id) {
+        var table = document.getElementById("tinytable_86moeycncdqrycp33a8h");
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         const targetCell = targetRow.cells[j];
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 </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_cffkbcjlluhfm08hj8ua</title>
+<title>tinytable_i7htbq5z2g6vih7eneci</title>
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@@ -2537,7 +2537,7 @@ <h1 class="title">Model Summaries</h1>
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-      <table class="table table-borderless" id="tinytable_cffkbcjlluhfm08hj8ua" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_i7htbq5z2g6vih7eneci" 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>
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-        var table = document.getElementById("tinytable_cffkbcjlluhfm08hj8ua");
+      function styleCell_tinytable_eoc440n95w2dexgw5qvm(i, j, css_id) {
+        var table = document.getElementById("tinytable_i7htbq5z2g6vih7eneci");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_cffkbcjlluhfm08hj8ua');
+        var table = document.getElementById('tinytable_i7htbq5z2g6vih7eneci');
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@@ -2611,8 +2611,8 @@ <h1 class="title">Model Summaries</h1>
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@@ -2639,46 +2639,46 @@ <h1 class="title">Model Summaries</h1>
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@@ -2693,13 +2693,13 @@ <h1 class="title">Model Summaries</h1>
   
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+<title>tinytable_ol0haxcslo9szrr1bwg0</title>
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     MathJax = {
@@ -2711,7 +2711,7 @@ <h1 class="title">Model Summaries</h1>
       }
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_jrgcs1byppk7fgap0vpy" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ol0haxcslo9szrr1bwg0" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -2748,12 +2748,12 @@ <h1 class="title">Model Summaries</h1>
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-      function styleCell_tinytable_hoay8kntxeoh9t197seu(i, j, css_id) {
-        var table = document.getElementById("tinytable_jrgcs1byppk7fgap0vpy");
+      function styleCell_tinytable_uq2vtkygeuftwt40fqza(i, j, css_id) {
+        var table = document.getElementById("tinytable_ol0haxcslo9szrr1bwg0");
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       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_jrgcs1byppk7fgap0vpy');
+        var table = document.getElementById('tinytable_ol0haxcslo9szrr1bwg0');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
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@@ -2761,8 +2761,8 @@ <h1 class="title">Model Summaries</h1>
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-      function spanCell_tinytable_hoay8kntxeoh9t197seu(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_jrgcs1byppk7fgap0vpy");
+      function spanCell_tinytable_uq2vtkygeuftwt40fqza(i, j, rowspan, colspan) {
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+window.addEventListener('load', function () { styleCell_tinytable_uq2vtkygeuftwt40fqza(4, 3, 'tinytable_css_4uhl1yr2al6nfg5tmgld') })
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@@ -2825,13 +2825,13 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
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+.table td.tinytable_css_4jfco35b7v9rl3sum0fl, .table th.tinytable_css_4jfco35b7v9rl3sum0fl {    text-align: left; }
+.table td.tinytable_css_8ufftrqv5fe0b2abfp0r, .table th.tinytable_css_8ufftrqv5fe0b2abfp0r {    text-align: center; }
+.table td.tinytable_css_w26zkg6zbcorkpe8apkh, .table th.tinytable_css_w26zkg6zbcorkpe8apkh {    text-align: center; }
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     MathJax = {
@@ -2843,7 +2843,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_cpvgfqjkp7tgmet3t0gs" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_9pd8yz92tzlvj8lrlcrn" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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                 <th scope="col">OLS 1</th>
@@ -2880,12 +2880,12 @@ <h1 class="title">Model Summaries</h1>
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-      function styleCell_tinytable_crt0f5yogxcf0dgxq1re(i, j, css_id) {
-        var table = document.getElementById("tinytable_cpvgfqjkp7tgmet3t0gs");
+      function styleCell_tinytable_g8af4vnticozbyhkxmmv(i, j, css_id) {
+        var table = document.getElementById("tinytable_9pd8yz92tzlvj8lrlcrn");
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       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_cpvgfqjkp7tgmet3t0gs');
+        var table = document.getElementById('tinytable_9pd8yz92tzlvj8lrlcrn');
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         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -2893,8 +2893,8 @@ <h1 class="title">Model Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_crt0f5yogxcf0dgxq1re(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_cpvgfqjkp7tgmet3t0gs");
+      function spanCell_tinytable_g8af4vnticozbyhkxmmv(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_9pd8yz92tzlvj8lrlcrn");
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@@ -2921,30 +2921,30 @@ <h1 class="title">Model Summaries</h1>
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@@ -2957,13 +2957,13 @@ <h1 class="title">Model Summaries</h1>
   
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+<title>tinytable_p4exnhbowx10ezakvoei</title>
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+      <table class="table table-borderless" id="tinytable_p4exnhbowx10ezakvoei" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -3012,12 +3012,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_vbkejrz9xq0w5cdzbkpu(i, j, css_id) {
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-        var table = document.getElementById('tinytable_6143uaceet8375v5es4v');
+        var table = document.getElementById('tinytable_p4exnhbowx10ezakvoei');
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+      function spanCell_tinytable_vbkejrz9xq0w5cdzbkpu(i, j, rowspan, colspan) {
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@@ -3089,13 +3089,13 @@ <h1 class="title">Model Summaries</h1>
   
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@@ -3107,7 +3107,7 @@ <h1 class="title">Model Summaries</h1>
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-      <table class="table table-borderless" id="tinytable_tyw740p8uhqyomtirxcz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_4ng1xzp8qlfvdbetl7zp" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -3132,12 +3132,12 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById('tinytable_tyw740p8uhqyomtirxcz');
+        var table = document.getElementById('tinytable_4ng1xzp8qlfvdbetl7zp');
         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_hc2kwqcw2evkd25641tw(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_tyw740p8uhqyomtirxcz");
+      function spanCell_tinytable_8pks1m2vw3f9du8hsfgq(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_4ng1xzp8qlfvdbetl7zp");
         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_8pks1m2vw3f9du8hsfgq(1, 2, 'tinytable_css_d1kw2iptr2ytuei1dllx') })
+window.addEventListener('load', function () { styleCell_tinytable_8pks1m2vw3f9du8hsfgq(2, 2, 'tinytable_css_d1kw2iptr2ytuei1dllx') })
+window.addEventListener('load', function () { styleCell_tinytable_8pks1m2vw3f9du8hsfgq(0, 3, 'tinytable_css_p1cewy0k9nx5czr7x4fo') })
+window.addEventListener('load', function () { styleCell_tinytable_8pks1m2vw3f9du8hsfgq(1, 3, 'tinytable_css_p1cewy0k9nx5czr7x4fo') })
+window.addEventListener('load', function () { styleCell_tinytable_8pks1m2vw3f9du8hsfgq(2, 3, 'tinytable_css_p1cewy0k9nx5czr7x4fo') })
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 </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_k87n3hf4xcuoi14wofjs</title>
+<title>tinytable_kkzidiuenrxbxosq5w1i</title>
 <style>
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-.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; }
+.table td.tinytable_css_l7f64cxk36s0wz4jczi1, .table th.tinytable_css_l7f64cxk36s0wz4jczi1 {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_q34suozlpzjwuqhn905d, .table th.tinytable_css_q34suozlpzjwuqhn905d {    text-align: left; }
+.table td.tinytable_css_z991u90h13p0lyyxc5ew, .table th.tinytable_css_z991u90h13p0lyyxc5ew {    text-align: center; }
+.table td.tinytable_css_nkf1yzrr6h9gksmp5wql, .table th.tinytable_css_nkf1yzrr6h9gksmp5wql {    text-align: center; }
+.table td.tinytable_css_anj5y8qnwu5y88aavene, .table th.tinytable_css_anj5y8qnwu5y88aavene {    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 = {
@@ -3219,7 +3219,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_k87n3hf4xcuoi14wofjs" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_kkzidiuenrxbxosq5w1i" 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>
 
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-      function styleCell_tinytable_gv68iyyolbi01g8i5b5x(i, j, css_id) {
-        var table = document.getElementById("tinytable_k87n3hf4xcuoi14wofjs");
+      function styleCell_tinytable_h7ovo086kdtwjj8uoh2l(i, j, css_id) {
+        var table = document.getElementById("tinytable_kkzidiuenrxbxosq5w1i");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_k87n3hf4xcuoi14wofjs');
+        var table = document.getElementById('tinytable_kkzidiuenrxbxosq5w1i');
         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_gv68iyyolbi01g8i5b5x(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_k87n3hf4xcuoi14wofjs");
+      function spanCell_tinytable_h7ovo086kdtwjj8uoh2l(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_kkzidiuenrxbxosq5w1i");
         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>
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+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(1, 1, 'tinytable_css_z991u90h13p0lyyxc5ew') })
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+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(3, 1, 'tinytable_css_z991u90h13p0lyyxc5ew') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(4, 1, 'tinytable_css_z991u90h13p0lyyxc5ew') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(0, 2, 'tinytable_css_nkf1yzrr6h9gksmp5wql') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(1, 2, 'tinytable_css_nkf1yzrr6h9gksmp5wql') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(2, 2, 'tinytable_css_nkf1yzrr6h9gksmp5wql') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(3, 2, 'tinytable_css_nkf1yzrr6h9gksmp5wql') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(4, 2, 'tinytable_css_nkf1yzrr6h9gksmp5wql') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(0, 3, 'tinytable_css_anj5y8qnwu5y88aavene') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(1, 3, 'tinytable_css_anj5y8qnwu5y88aavene') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(2, 3, 'tinytable_css_anj5y8qnwu5y88aavene') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(3, 3, 'tinytable_css_anj5y8qnwu5y88aavene') })
+window.addEventListener('load', function () { styleCell_tinytable_h7ovo086kdtwjj8uoh2l(4, 3, 'tinytable_css_anj5y8qnwu5y88aavene') })
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@@ -3333,13 +3333,13 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
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-<title>tinytable_ohscx6fjfyu6kxwz9r55</title>
+<title>tinytable_lp9znvg4qsacloeuh2rn</title>
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+.table td.tinytable_css_c5ssc9ev37f7nx5nr7bd, .table th.tinytable_css_c5ssc9ev37f7nx5nr7bd {    text-align: left; }
+.table td.tinytable_css_m3jmqec26rn29wl9y2ew, .table th.tinytable_css_m3jmqec26rn29wl9y2ew {    text-align: center; }
+.table td.tinytable_css_806i2iwtxkjsvxvejpaq, .table th.tinytable_css_806i2iwtxkjsvxvejpaq {    text-align: center; }
+.table td.tinytable_css_tay62mrsm2sn0ums0yab, .table th.tinytable_css_tay62mrsm2sn0ums0yab {    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_ohscx6fjfyu6kxwz9r55" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_lp9znvg4qsacloeuh2rn" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -3400,12 +3400,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_1t4iebdawj1ffa65tm3n(i, j, css_id) {
+        var table = document.getElementById("tinytable_lp9znvg4qsacloeuh2rn");
         table.rows[i].cells[j].classList.add(css_id);
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_ohscx6fjfyu6kxwz9r55');
+        var table = document.getElementById('tinytable_lp9znvg4qsacloeuh2rn');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
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@@ -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_7rgyzpwika0g4qqzrg6r(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_ohscx6fjfyu6kxwz9r55");
+      function spanCell_tinytable_1t4iebdawj1ffa65tm3n(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_lp9znvg4qsacloeuh2rn");
         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>
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+window.addEventListener('load', function () { styleCell_tinytable_1t4iebdawj1ffa65tm3n(1, 3, 'tinytable_css_tay62mrsm2sn0ums0yab') })
+window.addEventListener('load', function () { styleCell_tinytable_1t4iebdawj1ffa65tm3n(2, 3, 'tinytable_css_tay62mrsm2sn0ums0yab') })
+window.addEventListener('load', function () { styleCell_tinytable_1t4iebdawj1ffa65tm3n(3, 3, 'tinytable_css_tay62mrsm2sn0ums0yab') })
+window.addEventListener('load', function () { styleCell_tinytable_1t4iebdawj1ffa65tm3n(4, 3, 'tinytable_css_tay62mrsm2sn0ums0yab') })
+window.addEventListener('load', function () { styleCell_tinytable_1t4iebdawj1ffa65tm3n(5, 3, 'tinytable_css_tay62mrsm2sn0ums0yab') })
+window.addEventListener('load', function () { styleCell_tinytable_1t4iebdawj1ffa65tm3n(6, 3, 'tinytable_css_tay62mrsm2sn0ums0yab') })
     </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_k698if5lz3ipydsdyooo</title>
+<title>tinytable_9m60ph48n11c1hv28smz</title>
 <style>
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-.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; }
+.table td.tinytable_css_qzgj6xcotq2bioqjnntt, .table th.tinytable_css_qzgj6xcotq2bioqjnntt {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_28tpzzktzsumokekk0js, .table th.tinytable_css_28tpzzktzsumokekk0js {    text-align: left; }
+.table td.tinytable_css_4rd3xl068wdnqmmw53dk, .table th.tinytable_css_4rd3xl068wdnqmmw53dk {    text-align: center; }
+.table td.tinytable_css_jp1x61bu4x69gsd0p9au, .table th.tinytable_css_jp1x61bu4x69gsd0p9au {    text-align: center; }
+.table td.tinytable_css_t3o3i9wxt2o6e7mmqxl7, .table th.tinytable_css_t3o3i9wxt2o6e7mmqxl7 {    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 = {
@@ -3508,7 +3508,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_k698if5lz3ipydsdyooo" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_9m60ph48n11c1hv28smz" 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_eln2ukuv4aq7q3oitbeg(i, j, css_id) {
-        var table = document.getElementById("tinytable_k698if5lz3ipydsdyooo");
+      function styleCell_tinytable_42sufz7r06yymyqsolxq(i, j, css_id) {
+        var table = document.getElementById("tinytable_9m60ph48n11c1hv28smz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_k698if5lz3ipydsdyooo');
+        var table = document.getElementById('tinytable_9m60ph48n11c1hv28smz');
         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_eln2ukuv4aq7q3oitbeg(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_k698if5lz3ipydsdyooo");
+      function spanCell_tinytable_42sufz7r06yymyqsolxq(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_9m60ph48n11c1hv28smz");
         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>
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@@ -3684,13 +3684,13 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_zi7hiyfnn6zaq5u7y4j3" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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@@ -3804,12 +3804,12 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_wuop2pcek1m6l5flgd7a");
+      function styleCell_tinytable_gds711e36g6wff0yeebj(i, j, css_id) {
+        var table = document.getElementById("tinytable_zi7hiyfnn6zaq5u7y4j3");
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-        var table = document.getElementById('tinytable_wuop2pcek1m6l5flgd7a');
+        var table = document.getElementById('tinytable_zi7hiyfnn6zaq5u7y4j3');
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@@ -3930,13 +3930,13 @@ <h1 class="title">Model Summaries</h1>
   
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+      <table class="table table-borderless" id="tinytable_gri94lragg0xnixalrnk" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead><tr>
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@@ -4030,12 +4030,12 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_d70sxpfvztc2wa8kgvcf");
+      function styleCell_tinytable_jqwbpxargxpmbntm53uq(i, j, css_id) {
+        var table = document.getElementById("tinytable_gri94lragg0xnixalrnk");
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_d70sxpfvztc2wa8kgvcf');
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 </div>
@@ -4140,12 +4140,12 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_n2ffwqg0pwp931eisxtr</title>
+<title>tinytable_hy6ctwrhu4yjogd11hhv</title>
 <style>
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+.table td.tinytable_css_7p2hyzg5uq30we87t8ty, .table th.tinytable_css_7p2hyzg5uq30we87t8ty {    text-align: center; }
+.table td.tinytable_css_v55yyxhc2btet73bfv20, .table th.tinytable_css_v55yyxhc2btet73bfv20 {    border-bottom: solid 0.05em black; }
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     MathJax = {
@@ -4157,7 +4157,7 @@ <h1 class="title">Model Summaries</h1>
       }
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     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_n2ffwqg0pwp931eisxtr" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_hy6ctwrhu4yjogd11hhv" 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>
@@ -4232,12 +4232,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_n2ffwqg0pwp931eisxtr");
+      function styleCell_tinytable_6h1byd8w99cwhctg5d9t(i, j, css_id) {
+        var table = document.getElementById("tinytable_hy6ctwrhu4yjogd11hhv");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_n2ffwqg0pwp931eisxtr');
+        var table = document.getElementById('tinytable_hy6ctwrhu4yjogd11hhv');
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@@ -4245,8 +4245,8 @@ <h1 class="title">Model Summaries</h1>
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         newCell.innerHTML = content;
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-      function spanCell_tinytable_ngaq2zf2a457usa917w4(i, j, rowspan, colspan) {
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+      function spanCell_tinytable_6h1byd8w99cwhctg5d9t(i, j, rowspan, colspan) {
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         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
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@@ -4273,44 +4273,44 @@ <h1 class="title">Model Summaries</h1>
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-        var table = document.getElementById("tinytable_6wq9xs0pmvbqoj2c9917");
+      function styleCell_tinytable_vegpmsbrpx1mepelk4pj(i, j, css_id) {
+        var table = document.getElementById("tinytable_era8a74xfo7uu80e5pmz");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_6wq9xs0pmvbqoj2c9917');
+        var table = document.getElementById('tinytable_era8a74xfo7uu80e5pmz');
         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_1th7y2e9zjgc4t7xijyt(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_6wq9xs0pmvbqoj2c9917");
+      function spanCell_tinytable_vegpmsbrpx1mepelk4pj(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_era8a74xfo7uu80e5pmz");
         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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     </script>
 </div>
 </div>
@@ -5732,19 +5732,19 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_y8gy4u2z9y6uu125wr19</title>
+<title>tinytable_33cqm5etwh6wf9vstami</title>
 <style>
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@@ -6038,12 +6038,12 @@ <h1 class="title">Model Summaries</h1>
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@@ -6507,14 +6507,14 @@ <h1 class="title">Model Summaries</h1>
   
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 <thead><tr>
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@@ -6608,12 +6608,12 @@ <h1 class="title">Model Summaries</h1>
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+      function styleCell_tinytable_mryqtnvrt4emvsdfp6r3(i, j, css_id) {
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+      function insertSpanRow_g3y6bfe0xgygztbjzupb(i, colspan, content) {
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-      <table class="table table-borderless" id="tinytable_mh0rh4x36zji6idjis27" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_db6m43uvrfmvm9agdguz" 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>
 
     <script>
-      function styleCell_tinytable_c5ohesk90quwmm5kocu8(i, j, css_id) {
-        var table = document.getElementById("tinytable_mh0rh4x36zji6idjis27");
+      function styleCell_tinytable_oz6p1m36cxt34mr4ist7(i, j, css_id) {
+        var table = document.getElementById("tinytable_db6m43uvrfmvm9agdguz");
         table.rows[i].cells[j].classList.add(css_id);
       }
-      function insertSpanRow_tonolendb7ipzi3l1gga(i, colspan, content) {
-        var table = document.getElementById('tinytable_mh0rh4x36zji6idjis27');
+      function insertSpanRow_vqc46hm0r2249gy95nu3(i, colspan, content) {
+        var table = document.getElementById('tinytable_db6m43uvrfmvm9agdguz');
         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_c5ohesk90quwmm5kocu8(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_mh0rh4x36zji6idjis27");
+      function spanCell_tinytable_oz6p1m36cxt34mr4ist7(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_db6m43uvrfmvm9agdguz");
         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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+      function styleCell_tinytable_z09wepfm7di9zvoa7oyn(i, j, css_id) {
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+      <table class="table table-borderless" id="tinytable_oqucziaa2f5rxye5oddz" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
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-        var table = document.getElementById("tinytable_rx0se4nak144lxodtco6");
+      function styleCell_tinytable_welt0gih2q6q6yt5hfsk(i, j, css_id) {
+        var table = document.getElementById("tinytable_oqucziaa2f5rxye5oddz");
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+      function insertSpanRow_igiroewful8r7jvmptue(i, colspan, content) {
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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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+      <table class="table table-borderless" id="tinytable_b7lpy6o8nvigde8rkmkp" 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>
@@ -8619,12 +8619,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
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-        var table = document.getElementById("tinytable_ilm86p0fvjfgpjpfr2ms");
+      function styleCell_tinytable_a72kkngpd3q6hcfppjl0(i, j, css_id) {
+        var table = document.getElementById("tinytable_b7lpy6o8nvigde8rkmkp");
         table.rows[i].cells[j].classList.add(css_id);
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       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_ilm86p0fvjfgpjpfr2ms');
+        var table = document.getElementById('tinytable_b7lpy6o8nvigde8rkmkp');
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+      function spanCell_tinytable_a72kkngpd3q6hcfppjl0(i, j, rowspan, colspan) {
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+window.addEventListener('load', function () { styleCell_tinytable_a72kkngpd3q6hcfppjl0(15, 1, 'tinytable_css_y5111fadr356uijxtk8o') })
+window.addEventListener('load', function () { styleCell_tinytable_a72kkngpd3q6hcfppjl0(7, 0, 'tinytable_css_7hwd961m6e2pdbo6px0p') })
+window.addEventListener('load', function () { styleCell_tinytable_a72kkngpd3q6hcfppjl0(7, 1, 'tinytable_css_7hwd961m6e2pdbo6px0p') })
     </script>
 </div>
 </div>
@@ -8739,12 +8739,12 @@ <h1 class="title">Model Summaries</h1>
   
     <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>tinytable_ydt55vodj93gkxcu138j</title>
+<title>tinytable_ik4q684zuzkm1p8epj9p</title>
 <style>
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-.table td.tinytable_css_ffbcuwlp7ovazqzbu90v, .table th.tinytable_css_ffbcuwlp7ovazqzbu90v {    text-align: left; }
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-.table td.tinytable_css_2asa2jhaa31mxqco7n96, .table th.tinytable_css_2asa2jhaa31mxqco7n96 {    border-bottom: solid 0.05em black; }
+.table td.tinytable_css_wkkadlz30vu66m4vhz1k, .table th.tinytable_css_wkkadlz30vu66m4vhz1k {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_chwojkj2jei9wxejr8pg, .table th.tinytable_css_chwojkj2jei9wxejr8pg {    text-align: left; }
+.table td.tinytable_css_8dz7f976bo2orfkrgnly, .table th.tinytable_css_8dz7f976bo2orfkrgnly {    text-align: center; }
+.table td.tinytable_css_h7n0z72g7xk0nga5atux, .table th.tinytable_css_h7n0z72g7xk0nga5atux {    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 = {
@@ -8756,7 +8756,7 @@ <h1 class="title">Model Summaries</h1>
       }
     };
     </script><div class="container">
-      <table class="table table-borderless" id="tinytable_ydt55vodj93gkxcu138j" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ik4q684zuzkm1p8epj9p" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
 <thead>
 </thead><caption>Another math expression: $\sum_{i=1}^n x^-i$</caption>
               <tbody><tr>
@@ -8826,12 +8826,12 @@ <h1 class="title">Model Summaries</h1>
 </div>
 
     <script>
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-        var table = document.getElementById("tinytable_ydt55vodj93gkxcu138j");
+      function styleCell_tinytable_h68oewgooktell7b3bmh(i, j, css_id) {
+        var table = document.getElementById("tinytable_ik4q684zuzkm1p8epj9p");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_ydt55vodj93gkxcu138j');
+        var table = document.getElementById('tinytable_ik4q684zuzkm1p8epj9p');
         var newRow = table.insertRow(i);
         var newCell = newRow.insertCell(0);
         newCell.setAttribute("colspan", colspan);
@@ -8839,8 +8839,8 @@ <h1 class="title">Model Summaries</h1>
         // this may be unsafe, but innerText does not interpret <br>
         newCell.innerHTML = content;
       }
-      function spanCell_tinytable_l11r7lj6zkeeyij8km3b(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_ydt55vodj93gkxcu138j");
+      function spanCell_tinytable_h68oewgooktell7b3bmh(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ik4q684zuzkm1p8epj9p");
         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -8867,40 +8867,40 @@ <h1 class="title">Model Summaries</h1>
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@@ -9733,33 +9733,33 @@ <h1 class="title">Model Summaries</h1>
 <tbody>
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-                  <td>281.967 </td>
+                  <td>326.362 </td>
+                  <td>283.790 </td>
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-                  <td>(41.371)</td>
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+                  <td>(41.181)</td>
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-                  <td>-10.205 </td>
+                  <td>-8.991  </td>
+                  <td>-10.225 </td>
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-                  <td>(2.339) </td>
+                  <td>(1.410) </td>
+                  <td>(2.207) </td>
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+                  <td>18.796  </td>
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@@ -9806,12 +9806,12 @@ <h1 class="title">Model Summaries</h1>
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@@ -9958,27 +9958,27 @@ <h1 class="title">Model Summaries</h1>
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-                  <td>-0.174           </td>
+                  <td>13.669           </td>
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+                  <td>(39.671)         </td>
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-                  <td>3.060            </td>
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+                  <td>(190.021)        </td>
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@@ -9986,31 +9986,31 @@ <h1 class="title">Model Summaries</h1>
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 <thead><tr>
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                 <th scope="col">Listwise deletion</th>
@@ -10194,43 +10194,43 @@ <h1 class="title">Model Summaries</h1>
 <tbody>
 <tr>
 <td>(Intercept)</td>
-                  <td>81.719  </td>
-                  <td>75.699  </td>
-                  <td>79.645  </td>
+                  <td>71.717  </td>
+                  <td>73.303  </td>
+                  <td>72.284  </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(14.584)</td>
-                  <td>(13.342)</td>
-                  <td>(13.468)</td>
+                  <td>(14.112)</td>
+                  <td>(14.664)</td>
+                  <td>(12.809)</td>
                 </tr>
 <tr>
 <td>Literacy   </td>
-                  <td>-0.638  </td>
-                  <td>-0.499  </td>
-                  <td>-0.541  </td>
+                  <td>-0.418  </td>
+                  <td>-0.470  </td>
+                  <td>-0.435  </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(0.234) </td>
-                  <td>(0.205) </td>
-                  <td>(0.216) </td>
+                  <td>(0.221) </td>
+                  <td>(0.228) </td>
+                  <td>(0.194) </td>
                 </tr>
 <tr>
 <td>Commerce   </td>
-                  <td>-0.305  </td>
-                  <td>-0.283  </td>
-                  <td>-0.331  </td>
+                  <td>-0.269  </td>
+                  <td>-0.266  </td>
+                  <td>-0.297  </td>
                 </tr>
 <tr>
 <td>           </td>
-                  <td>(0.168) </td>
-                  <td>(0.145) </td>
-                  <td>(0.148) </td>
+                  <td>(0.159) </td>
+                  <td>(0.157) </td>
+                  <td>(0.152) </td>
                 </tr>
 <tr>
 <td>Num.Obs.   </td>
-                  <td>59      </td>
+                  <td>60      </td>
                   <td>86      </td>
                   <td>86      </td>
                 </tr>
@@ -10242,43 +10242,43 @@ <h1 class="title">Model Summaries</h1>
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 <tr>
 <td>R2         </td>
-                  <td>0.121   </td>
-                  <td>0.078   </td>
-                  <td>0.102   </td>
+                  <td>0.068   </td>
+                  <td>0.072   </td>
+                  <td>0.077   </td>
                 </tr>
 <tr>
 <td>R2 Adj.    </td>
-                  <td>0.089   </td>
-                  <td>0.055   </td>
-                  <td>0.079   </td>
+                  <td>0.035   </td>
+                  <td>0.047   </td>
+                  <td>0.054   </td>
                 </tr>
 <tr>
 <td>AIC        </td>
-                  <td>557.0   </td>
+                  <td>561.8   </td>
                   <td>        </td>
                   <td>        </td>
                 </tr>
 <tr>
 <td>BIC        </td>
-                  <td>565.3   </td>
+                  <td>570.2   </td>
                   <td>        </td>
                   <td>        </td>
                 </tr>
 <tr>
 <td>Log.Lik.   </td>
-                  <td>-274.488</td>
+                  <td>-276.887</td>
                   <td>        </td>
                   <td>        </td>
                 </tr>
 <tr>
 <td>F          </td>
-                  <td>3.843   </td>
+                  <td>2.071   </td>
                   <td>        </td>
                   <td>        </td>
                 </tr>
 <tr>
 <td>RMSE       </td>
-                  <td>25.37   </td>
+                  <td>24.43   </td>
                   <td>        </td>
                   <td>        </td>
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@@ -10287,12 +10287,12 @@ <h1 class="title">Model Summaries</h1>
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         const targetRow = table.rows[i];
         const targetCell = targetRow.cells[j];
         for (let r = 0; r < rowspan; r++) {
@@ -10328,78 +10328,78 @@ <h1 class="title">Model Summaries</h1>
         targetCell.colSpan = colspan;
       }
 
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+<pre><code>tidy: 0.002 sec elapsed</code></pre>
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 <div class="cell-output cell-output-stdout">
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+<pre><code>parameters: 0.021 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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 <div class="cell-output cell-output-stdout">
-<pre><code>performance: 0.014 sec elapsed</code></pre>
+<pre><code>performance: 0.011 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) -2.2246277000 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>
+<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) -2.1971590667 0.57768137       0.95 -3.334876772 -1.105943165             normal        0.40625  1.24747729              NA        NA      NA
+2          hp  0.0006333653 0.00105191       0.95 -0.001422686  0.002819079             normal        0.00000  0.01819465              NA        NA      NA
+3        drat  0.6992566587 0.13402411       0.95  0.437680965  0.969048697             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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@@ -10905,9 +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) -2.2256516592 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>
+1 (Intercept) -2.2124440913 0.579236863       0.95 -3.334876772 -1.105943165             normal        0.40625  1.24747729              NA        NA      NA
+2          hp  0.0006557409 0.001073215       0.95 -0.001422686  0.002819079             normal        0.00000  0.01819465              NA        NA      NA
+3        drat  0.7008098831 0.136555046       0.95  0.437680965  0.969048697             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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 <tr>
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-                  <td>-2.226 </td>
+                  <td>-2.212 </td>
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 <tr>
 <td>           </td>
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+                  <td>(0.579)</td>
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 <tr>
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 <tr>
 <td>drat       </td>
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 <tr>
 <td>R2 Adj.    </td>
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+                  <td>0.437  </td>
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 <tr>
 <td>Log.Lik.   </td>
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 <tr>
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+                  <td>-15.1  </td>
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 <td>ELPD s.e.  </td>
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 <tr>
 <td>LOOIC      </td>
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@@ -11094,10 +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) -2.2246277000 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>
+<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) -2.1971590667 0.57768137       0.95 -3.334876772 -1.105943165 0.9995               0             normal        0.40625  1.24747729              NA        NA      NA
+2          hp  0.0006333653 0.00105191       0.95 -0.001422686  0.002819079 0.7270               1             normal        0.00000  0.01819465              NA        NA      NA
+3        drat  0.6992566587 0.13402411       0.95  0.437680965  0.969048697 1.0000               0             normal        0.00000  2.33313429              NA        NA      NA</code></pre>
 </div>
 </div>
 
diff --git a/vignettes/modelsummary_extension.html b/vignettes/modelsummary_extension.html
index 79c479ff5..982ec86fc 100644
--- a/vignettes/modelsummary_extension.html
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-        var table = document.getElementById("tinytable_jksl040r3pxtt6k384tx");
+      function spanCell_tinytable_dfottpdbffpp1w5b2tp6(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_urqnt8gm8dgtlm7xko2e");
         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_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') })
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-window.addEventListener('load', function () { styleCell_tinytable_w2ao64vjccgz2tyb7ye7(7, 0, 'tinytable_css_29nk40at2t6h3dqqnr04') })
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-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') })
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-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') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(0, 0, 'tinytable_css_iwnbqpse1ks8prvq5agv') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(0, 1, 'tinytable_css_iwnbqpse1ks8prvq5agv') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(0, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(1, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(2, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(3, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(4, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(5, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(6, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(7, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(8, 0, 'tinytable_css_smmf3xwwhzzjffiolopn') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(0, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(1, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(2, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(3, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(4, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(5, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(6, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(7, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(8, 1, 'tinytable_css_om9un87g815q171226qp') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(6, 0, 'tinytable_css_uxunh0jfufy7818yrsuz') })
+window.addEventListener('load', function () { styleCell_tinytable_dfottpdbffpp1w5b2tp6(6, 1, 'tinytable_css_uxunh0jfufy7818yrsuz') })
     </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_5twluwvpajnufzz0zw3n</title>
+<title>tinytable_ab02b4lvfzpm7ypayfvc</title>
 <style>
-.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; }
+.table td.tinytable_css_z0tp2gp0bl50cn2i1c9u, .table th.tinytable_css_z0tp2gp0bl50cn2i1c9u {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_9mjnwe29cidf47ftonel, .table th.tinytable_css_9mjnwe29cidf47ftonel {    text-align: left; }
+.table td.tinytable_css_m7vasjsu73hjtnc4i7kx, .table th.tinytable_css_m7vasjsu73hjtnc4i7kx {    text-align: center; }
+.table td.tinytable_css_mmch6fz48vme8v0nf6in, .table th.tinytable_css_mmch6fz48vme8v0nf6in {    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_5twluwvpajnufzz0zw3n" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ab02b4lvfzpm7ypayfvc" 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_qs19tv3lwc61ckh7se0f(i, j, css_id) {
-        var table = document.getElementById("tinytable_5twluwvpajnufzz0zw3n");
+      function styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(i, j, css_id) {
+        var table = document.getElementById("tinytable_ab02b4lvfzpm7ypayfvc");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_5twluwvpajnufzz0zw3n');
+        var table = document.getElementById('tinytable_ab02b4lvfzpm7ypayfvc');
         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_qs19tv3lwc61ckh7se0f(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_5twluwvpajnufzz0zw3n");
+      function spanCell_tinytable_6b0nnnuvlfvkv2og9iwo(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ab02b4lvfzpm7ypayfvc");
         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_qs19tv3lwc61ckh7se0f(0, 0, 'tinytable_css_cxy3ib2c7geuyzydemah') })
-window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(0, 1, 'tinytable_css_cxy3ib2c7geuyzydemah') })
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-window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(7, 0, 'tinytable_css_haue84cjt4ne791tysm4') })
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-window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(1, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
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-window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(3, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
-window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(4, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
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-window.addEventListener('load', function () { styleCell_tinytable_qs19tv3lwc61ckh7se0f(10, 1, 'tinytable_css_i02nd6mecwoxosrjbepd') })
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-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') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(0, 0, 'tinytable_css_z0tp2gp0bl50cn2i1c9u') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(0, 1, 'tinytable_css_z0tp2gp0bl50cn2i1c9u') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(0, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(1, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(2, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(3, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(4, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(5, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(6, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(7, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(8, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(9, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(10, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(11, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(12, 0, 'tinytable_css_9mjnwe29cidf47ftonel') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(0, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(1, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(2, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(3, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(4, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(5, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(6, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(7, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(8, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(9, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(10, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(11, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(12, 1, 'tinytable_css_m7vasjsu73hjtnc4i7kx') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(8, 0, 'tinytable_css_mmch6fz48vme8v0nf6in') })
+window.addEventListener('load', function () { styleCell_tinytable_6b0nnnuvlfvkv2og9iwo(8, 1, 'tinytable_css_mmch6fz48vme8v0nf6in') })
     </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_m5fjx7sp2ezy631papdo</title>
+<title>tinytable_sccr6fhs89g8wlpjbn8w</title>
 <style>
-.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; }
+.table td.tinytable_css_mdjrq1tffdbltxttwwaw, .table th.tinytable_css_mdjrq1tffdbltxttwwaw {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_zfcodkp1babf8m0a1lud, .table th.tinytable_css_zfcodkp1babf8m0a1lud {    text-align: left; }
+.table td.tinytable_css_5the8dhwaz3h1bv49vtx, .table th.tinytable_css_5the8dhwaz3h1bv49vtx {    text-align: center; }
+.table td.tinytable_css_mrfgnjixjg5fw9f7a0vx, .table th.tinytable_css_mrfgnjixjg5fw9f7a0vx {    text-align: center; }
+.table td.tinytable_css_7z1laqvoy6mjyau1ht3v, .table th.tinytable_css_7z1laqvoy6mjyau1ht3v {    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_m5fjx7sp2ezy631papdo" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_sccr6fhs89g8wlpjbn8w" 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_x952bwvicpt0gmg3srz3(i, j, css_id) {
-        var table = document.getElementById("tinytable_m5fjx7sp2ezy631papdo");
+      function styleCell_tinytable_lviyoi37mzymn4u4xsmw(i, j, css_id) {
+        var table = document.getElementById("tinytable_sccr6fhs89g8wlpjbn8w");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_m5fjx7sp2ezy631papdo');
+        var table = document.getElementById('tinytable_sccr6fhs89g8wlpjbn8w');
         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_x952bwvicpt0gmg3srz3(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_m5fjx7sp2ezy631papdo");
+      function spanCell_tinytable_lviyoi37mzymn4u4xsmw(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_sccr6fhs89g8wlpjbn8w");
         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_lviyoi37mzymn4u4xsmw(12, 2, 'tinytable_css_mrfgnjixjg5fw9f7a0vx') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(13, 2, 'tinytable_css_mrfgnjixjg5fw9f7a0vx') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(14, 2, 'tinytable_css_mrfgnjixjg5fw9f7a0vx') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(15, 2, 'tinytable_css_mrfgnjixjg5fw9f7a0vx') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(16, 2, 'tinytable_css_mrfgnjixjg5fw9f7a0vx') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(17, 2, 'tinytable_css_mrfgnjixjg5fw9f7a0vx') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(8, 0, 'tinytable_css_7z1laqvoy6mjyau1ht3v') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(8, 1, 'tinytable_css_7z1laqvoy6mjyau1ht3v') })
+window.addEventListener('load', function () { styleCell_tinytable_lviyoi37mzymn4u4xsmw(8, 2, 'tinytable_css_7z1laqvoy6mjyau1ht3v') })
     </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_h4l0flseibxj7c69crx7</title>
+<title>tinytable_v5tlkoopaevlgnaetdns</title>
 <style>
-.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; }
+.table td.tinytable_css_1hbccgr9p4gvpp1kr51w, .table th.tinytable_css_1hbccgr9p4gvpp1kr51w {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_wi9pt66mswj5jqgvgcqe, .table th.tinytable_css_wi9pt66mswj5jqgvgcqe {    text-align: left; }
+.table td.tinytable_css_ihixsv3sh74x18eskmnt, .table th.tinytable_css_ihixsv3sh74x18eskmnt {    text-align: center; }
+.table td.tinytable_css_uxe2x6qvqbo259nagrqz, .table th.tinytable_css_uxe2x6qvqbo259nagrqz {    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_h4l0flseibxj7c69crx7" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_v5tlkoopaevlgnaetdns" 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_tsl87hzyeieeglj3k2wi(i, j, css_id) {
-        var table = document.getElementById("tinytable_h4l0flseibxj7c69crx7");
+      function styleCell_tinytable_h4tqer23v4a9tvt3dyf4(i, j, css_id) {
+        var table = document.getElementById("tinytable_v5tlkoopaevlgnaetdns");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_h4l0flseibxj7c69crx7');
+        var table = document.getElementById('tinytable_v5tlkoopaevlgnaetdns');
         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_tsl87hzyeieeglj3k2wi(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_h4l0flseibxj7c69crx7");
+      function spanCell_tinytable_h4tqer23v4a9tvt3dyf4(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_v5tlkoopaevlgnaetdns");
         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_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') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(0, 0, 'tinytable_css_1hbccgr9p4gvpp1kr51w') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(0, 1, 'tinytable_css_1hbccgr9p4gvpp1kr51w') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(0, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(1, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(2, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(3, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(4, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(5, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(6, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(7, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(8, 0, 'tinytable_css_wi9pt66mswj5jqgvgcqe') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(0, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(1, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(2, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(3, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(4, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(5, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(6, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(7, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(8, 1, 'tinytable_css_ihixsv3sh74x18eskmnt') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(6, 0, 'tinytable_css_uxe2x6qvqbo259nagrqz') })
+window.addEventListener('load', function () { styleCell_tinytable_h4tqer23v4a9tvt3dyf4(6, 1, 'tinytable_css_uxe2x6qvqbo259nagrqz') })
     </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_utn1rzbcwfztkd0j2ikt</title>
+<title>tinytable_ua5gn2kfm4t1cyiowe8f</title>
 <style>
-.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; }
+.table td.tinytable_css_7dad8pghs5kx0r59pffu, .table th.tinytable_css_7dad8pghs5kx0r59pffu {    border-bottom: solid 0.1em #d3d8dc; }
+.table td.tinytable_css_iaaje1cpummtftf3a4ub, .table th.tinytable_css_iaaje1cpummtftf3a4ub {    text-align: left; }
+.table td.tinytable_css_cv3zzqvr1fr84h64ctof, .table th.tinytable_css_cv3zzqvr1fr84h64ctof {    text-align: center; }
+.table td.tinytable_css_9zka6zsyyjulk8hb148e, .table th.tinytable_css_9zka6zsyyjulk8hb148e {    text-align: center; }
+.table td.tinytable_css_ycg1k6ivqkimjc5cnr5y, .table th.tinytable_css_ycg1k6ivqkimjc5cnr5y {    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_utn1rzbcwfztkd0j2ikt" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
+      <table class="table table-borderless" id="tinytable_ua5gn2kfm4t1cyiowe8f" 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_xl3hbcrqmn71j60wq8cz(i, j, css_id) {
-        var table = document.getElementById("tinytable_utn1rzbcwfztkd0j2ikt");
+      function styleCell_tinytable_3tnztpni1slp17b0tmyw(i, j, css_id) {
+        var table = document.getElementById("tinytable_ua5gn2kfm4t1cyiowe8f");
         table.rows[i].cells[j].classList.add(css_id);
       }
       function insertSpanRow(i, colspan, content) {
-        var table = document.getElementById('tinytable_utn1rzbcwfztkd0j2ikt');
+        var table = document.getElementById('tinytable_ua5gn2kfm4t1cyiowe8f');
         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_xl3hbcrqmn71j60wq8cz(i, j, rowspan, colspan) {
-        var table = document.getElementById("tinytable_utn1rzbcwfztkd0j2ikt");
+      function spanCell_tinytable_3tnztpni1slp17b0tmyw(i, j, rowspan, colspan) {
+        var table = document.getElementById("tinytable_ua5gn2kfm4t1cyiowe8f");
         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_xl3hbcrqmn71j60wq8cz(0, 0, 'tinytable_css_xm53okcmtpcglphv9jfs') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(0, 1, 'tinytable_css_xm53okcmtpcglphv9jfs') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(0, 2, 'tinytable_css_xm53okcmtpcglphv9jfs') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(0, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(1, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(2, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(3, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(4, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(5, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(6, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(7, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(8, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(9, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(10, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(11, 0, 'tinytable_css_1h4fysyl4hbrvbd5xfme') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(0, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(1, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(2, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(3, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(4, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(5, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(6, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(7, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(8, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(9, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
-window.addEventListener('load', function () { styleCell_tinytable_xl3hbcrqmn71j60wq8cz(10, 1, 'tinytable_css_36vmwc99ooutbq1glkjr') })
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diff --git a/vignettes/tinytable_assets/ids456uxws03qlzan7a5xl.png b/vignettes/tinytable_assets/ids456uxws03qlzan7a5xl.png
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diff --git a/vignettes/tinytable_assets/idtik5javjkduihvbp61cr.png b/vignettes/tinytable_assets/idtik5javjkduihvbp61cr.png
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diff --git a/vignettes/tinytable_assets/iduaergfij55nl3egazed4.png b/vignettes/tinytable_assets/iduaergfij55nl3egazed4.png
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