From 4ea1be38d7abeb13b954bc7349dec4ce734759e4 Mon Sep 17 00:00:00 2001
From: naeemkh <khoshnevis.naeem@gmail.com>
Date: Tue, 14 Mar 2023 22:45:06 -0400
Subject: [PATCH] bump up version

---
 DESCRIPTION                                            | 2 +-
 docs/404.html                                          | 2 +-
 docs/LICENSE.html                                      | 2 +-
 docs/articles/GPCERF.html                              | 2 +-
 docs/articles/Nearest-neighbor-Gaussian-Processes.html | 2 +-
 docs/articles/Standard-Gaussian-Processes.html         | 2 +-
 docs/articles/index.html                               | 2 +-
 docs/authors.html                                      | 2 +-
 docs/index.html                                        | 2 +-
 docs/news/index.html                                   | 2 +-
 docs/pkgdown.yml                                       | 2 +-
 docs/reference/GPCERF-package.html                     | 2 +-
 docs/reference/autoplot.cerf_gp.html                   | 2 +-
 docs/reference/autoplot.cerf_nngp.html                 | 2 +-
 docs/reference/compute_deriv_nn.html                   | 4 ++--
 docs/reference/compute_deriv_weights_gp.html           | 2 +-
 docs/reference/compute_inverse.html                    | 2 +-
 docs/reference/compute_m_sigma.html                    | 2 +-
 docs/reference/compute_posterior_m_nn.html             | 2 +-
 docs/reference/compute_posterior_sd_nn.html            | 2 +-
 docs/reference/compute_rl_deriv_gp.html                | 2 +-
 docs/reference/compute_rl_deriv_nn.html                | 2 +-
 docs/reference/compute_sd_gp.html                      | 2 +-
 docs/reference/compute_w_corr.html                     | 2 +-
 docs/reference/compute_weight_gp.html                  | 2 +-
 docs/reference/estimate_cerf_gp.html                   | 2 +-
 docs/reference/estimate_cerf_nngp.html                 | 2 +-
 docs/reference/estimate_gps.html                       | 2 +-
 docs/reference/estimate_mean_sd_nn.html                | 2 +-
 docs/reference/estimate_noise_gp.html                  | 2 +-
 docs/reference/estimate_noise_nn.html                  | 2 +-
 docs/reference/find_optimal_nn.html                    | 2 +-
 docs/reference/generate_synthetic_data.html            | 2 +-
 docs/reference/get_logger.html                         | 2 +-
 docs/reference/index.html                              | 2 +-
 docs/reference/log_system_info.html                    | 2 +-
 docs/reference/plot.cerf_gp.html                       | 2 +-
 docs/reference/plot.cerf_nngp.html                     | 2 +-
 docs/reference/print.cerf_gp.html                      | 2 +-
 docs/reference/print.cerf_nngp.html                    | 2 +-
 docs/reference/set_logger.html                         | 2 +-
 docs/reference/summary.cerf_gp.html                    | 2 +-
 docs/reference/summary.cerf_nngp.html                  | 2 +-
 docs/search.json                                       | 2 +-
 man/compute_deriv_nn.Rd                                | 2 +-
 45 files changed, 46 insertions(+), 46 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 9d53a50..b440401 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: GPCERF
 Title: Gaussian Processes for Estimating Causal Exposure Response Curves
-Version: 0.2.0.9000
+Version: 0.2.1
 Authors@R: c(
     person("Naeem", "Khoshnevis", email = "nkhoshnevis@g.harvard.edu",
            role=c("aut","cre"), 
diff --git a/docs/404.html b/docs/404.html
index f072b11..420c7b8 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://NSAPH-Software.github.io/GPCERF/index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index 5ee15b5..2000bb9 100644
--- a/docs/LICENSE.html
+++ b/docs/LICENSE.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/GPCERF.html b/docs/articles/GPCERF.html
index b116b38..d8bae00 100644
--- a/docs/articles/GPCERF.html
+++ b/docs/articles/GPCERF.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/Nearest-neighbor-Gaussian-Processes.html b/docs/articles/Nearest-neighbor-Gaussian-Processes.html
index 1548f38..8e8e87d 100644
--- a/docs/articles/Nearest-neighbor-Gaussian-Processes.html
+++ b/docs/articles/Nearest-neighbor-Gaussian-Processes.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/Standard-Gaussian-Processes.html b/docs/articles/Standard-Gaussian-Processes.html
index c37a5dc..f152048 100644
--- a/docs/articles/Standard-Gaussian-Processes.html
+++ b/docs/articles/Standard-Gaussian-Processes.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/index.html b/docs/articles/index.html
index a32ce33..3d6acda 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/authors.html b/docs/authors.html
index c646990..f86bae9 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/index.html b/docs/index.html
index 1dbbc89..eee6df4 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/news/index.html b/docs/news/index.html
index c20c5b5..b24725b 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 1a27448..e197154 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   GPCERF: GPCERF.html
   Nearest-neighbor-Gaussian-Processes: Nearest-neighbor-Gaussian-Processes.html
   Standard-Gaussian-Processes: Standard-Gaussian-Processes.html
-last_built: 2023-03-15T02:28Z
+last_built: 2023-03-15T02:39Z
 urls:
   reference: https://NSAPH-Software.github.io/GPCERF/reference
   article: https://NSAPH-Software.github.io/GPCERF/articles
diff --git a/docs/reference/GPCERF-package.html b/docs/reference/GPCERF-package.html
index 71e0062..fca6570 100644
--- a/docs/reference/GPCERF-package.html
+++ b/docs/reference/GPCERF-package.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/autoplot.cerf_gp.html b/docs/reference/autoplot.cerf_gp.html
index 9c62569..7f5d1af 100644
--- a/docs/reference/autoplot.cerf_gp.html
+++ b/docs/reference/autoplot.cerf_gp.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/autoplot.cerf_nngp.html b/docs/reference/autoplot.cerf_nngp.html
index 10d16bc..ca8fb7f 100644
--- a/docs/reference/autoplot.cerf_nngp.html
+++ b/docs/reference/autoplot.cerf_nngp.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_deriv_nn.html b/docs/reference/compute_deriv_nn.html
index 0d39df4..9bdb81e 100644
--- a/docs/reference/compute_deriv_nn.html
+++ b/docs/reference/compute_deriv_nn.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -96,7 +96,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 (e_gps_pred).
 - Column 3: Standard deviation of  e_gps (e_gps_std)
 used_params:
-- dnorm_log: TRUE or FLASE</p></dd>
+- dnorm_log: TRUE or FALSE</p></dd>
 
 
 <dt>y_obs</dt>
diff --git a/docs/reference/compute_deriv_weights_gp.html b/docs/reference/compute_deriv_weights_gp.html
index 027cbef..733e550 100644
--- a/docs/reference/compute_deriv_weights_gp.html
+++ b/docs/reference/compute_deriv_weights_gp.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_inverse.html b/docs/reference/compute_inverse.html
index 3ef35f0..a49b1ca 100644
--- a/docs/reference/compute_inverse.html
+++ b/docs/reference/compute_inverse.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_m_sigma.html b/docs/reference/compute_m_sigma.html
index dee3298..8d02e03 100644
--- a/docs/reference/compute_m_sigma.html
+++ b/docs/reference/compute_m_sigma.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_posterior_m_nn.html b/docs/reference/compute_posterior_m_nn.html
index 59aef3b..47b8f3a 100644
--- a/docs/reference/compute_posterior_m_nn.html
+++ b/docs/reference/compute_posterior_m_nn.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_posterior_sd_nn.html b/docs/reference/compute_posterior_sd_nn.html
index 77e5455..68bd9fd 100644
--- a/docs/reference/compute_posterior_sd_nn.html
+++ b/docs/reference/compute_posterior_sd_nn.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_rl_deriv_gp.html b/docs/reference/compute_rl_deriv_gp.html
index a2c7dd6..6f0d6db 100644
--- a/docs/reference/compute_rl_deriv_gp.html
+++ b/docs/reference/compute_rl_deriv_gp.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_rl_deriv_nn.html b/docs/reference/compute_rl_deriv_nn.html
index 6ad7b7d..6c535c1 100644
--- a/docs/reference/compute_rl_deriv_nn.html
+++ b/docs/reference/compute_rl_deriv_nn.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_sd_gp.html b/docs/reference/compute_sd_gp.html
index a3d4508..c93ed54 100644
--- a/docs/reference/compute_sd_gp.html
+++ b/docs/reference/compute_sd_gp.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_w_corr.html b/docs/reference/compute_w_corr.html
index d0fa20c..91a3a46 100644
--- a/docs/reference/compute_w_corr.html
+++ b/docs/reference/compute_w_corr.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compute_weight_gp.html b/docs/reference/compute_weight_gp.html
index 15e5fc5..6b526b0 100644
--- a/docs/reference/compute_weight_gp.html
+++ b/docs/reference/compute_weight_gp.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_cerf_gp.html b/docs/reference/estimate_cerf_gp.html
index f5069c4..b87cb72 100644
--- a/docs/reference/estimate_cerf_gp.html
+++ b/docs/reference/estimate_cerf_gp.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_cerf_nngp.html b/docs/reference/estimate_cerf_nngp.html
index ccb1e12..d9faa92 100644
--- a/docs/reference/estimate_cerf_nngp.html
+++ b/docs/reference/estimate_cerf_nngp.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">GPCERF</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_gps.html b/docs/reference/estimate_gps.html
index 1a11a3c..3ac0201 100644
--- a/docs/reference/estimate_gps.html
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Therefore, modifying propagating covered work, indicate acceptance License .","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_10-automatic-licensing-of-downstream-recipients","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"10. Automatic Licensing of Downstream Recipients","title":"GNU General Public License","text":"time convey covered work, recipient automatically receives license original licensors, run, modify propagate work, subject License. responsible enforcing compliance third parties License. “entity transaction” transaction transferring control organization, substantially assets one, subdividing organization, merging organizations. propagation covered work results entity transaction, party transaction receives copy work also receives whatever licenses work party’s predecessor interest give previous paragraph, plus right possession Corresponding Source work predecessor interest, predecessor can get reasonable efforts. may impose restrictions exercise rights granted affirmed License. example, may impose license fee, royalty, charge exercise rights granted License, may initiate litigation (including cross-claim counterclaim lawsuit) alleging patent claim infringed making, using, selling, offering sale, importing Program portion .","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_11-patents","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"11. Patents","title":"GNU General Public License","text":"“contributor” copyright holder authorizes use License Program work Program based. work thus licensed called contributor’s “contributor version”. contributor’s “essential patent claims” patent claims owned controlled contributor, whether already acquired hereafter acquired, infringed manner, permitted License, making, using, selling contributor version, include claims infringed consequence modification contributor version. purposes definition, “control” includes right grant patent sublicenses manner consistent requirements License. contributor grants non-exclusive, worldwide, royalty-free patent license contributor’s essential patent claims, make, use, sell, offer sale, import otherwise run, modify propagate contents contributor version. following three paragraphs, “patent license” express agreement commitment, however denominated, enforce patent (express permission practice patent covenant sue patent infringement). “grant” patent license party means make agreement commitment enforce patent party. convey covered work, knowingly relying patent license, Corresponding Source work available anyone copy, free charge terms License, publicly available network server readily accessible means, must either (1) cause Corresponding Source available, (2) arrange deprive benefit patent license particular work, (3) arrange, manner consistent requirements License, extend patent license downstream recipients. “Knowingly relying” means actual knowledge , patent license, conveying covered work country, recipient’s use covered work country, infringe one identifiable patents country reason believe valid. , pursuant connection single transaction arrangement, convey, propagate procuring conveyance , covered work, grant patent license parties receiving covered work authorizing use, propagate, modify convey specific copy covered work, patent license grant automatically extended recipients covered work works based . patent license “discriminatory” include within scope coverage, prohibits exercise , conditioned non-exercise one rights specifically granted License. may convey covered work party arrangement third party business distributing software, make payment third party based extent activity conveying work, third party grants, parties receive covered work , discriminatory patent license () connection copies covered work conveyed (copies made copies), (b) primarily connection specific products compilations contain covered work, unless entered arrangement, patent license granted, prior 28 March 2007. Nothing License shall construed excluding limiting implied license defenses infringement may otherwise available applicable patent law.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_12-no-surrender-of-others-freedom","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"12. No Surrender of Others’ Freedom","title":"GNU General Public License","text":"conditions imposed (whether court order, agreement otherwise) contradict conditions License, excuse conditions License. convey covered work satisfy simultaneously obligations License pertinent obligations, consequence may convey . example, agree terms obligate collect royalty conveying convey Program, way satisfy terms License refrain entirely conveying Program.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_13-use-with-the-gnu-affero-general-public-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"13. Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. Program specifies certain numbered version GNU General Public License “later version” applies , option following terms conditions either numbered version later version published Free Software Foundation. Program specify version number GNU General Public License, may choose version ever published Free Software Foundation. Program specifies proxy can decide future versions GNU General Public License can used, proxy’s public statement acceptance version permanently authorizes choose version Program. Later license versions may give additional different permissions. However, additional obligations imposed author copyright holder result choosing follow later version.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_15-disclaimer-of-warranty","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"15. Disclaimer of Warranty","title":"GNU General Public License","text":"WARRANTY PROGRAM, EXTENT PERMITTED APPLICABLE LAW. EXCEPT OTHERWISE STATED WRITING COPYRIGHT HOLDERS /PARTIES PROVIDE PROGRAM “” WITHOUT WARRANTY KIND, EITHER EXPRESSED IMPLIED, INCLUDING, LIMITED , IMPLIED WARRANTIES MERCHANTABILITY FITNESS PARTICULAR PURPOSE. ENTIRE RISK QUALITY PERFORMANCE PROGRAM . PROGRAM PROVE DEFECTIVE, ASSUME COST NECESSARY SERVICING, REPAIR CORRECTION.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_16-limitation-of-liability","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"16. Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"generate-synthetic-data","dir":"Articles","previous_headings":"","what":"Generate Synthetic Data","title":"Standard Gaussian Processes","text":"first generate synthetic dataset six covariates, one continuous exposure one outcome. consider two types conditional distributions (normal student’s t) exposure given covariates. details synthetic data generation, see document. use function tru_R derive actual ERF population w=seq(0,20,0.1).","code":"set.seed(134) # Generate dataset with a normally distributed exposure given covariates data_sim_normal <- generate_synthetic_data(sample_size = 400,                                            outcome_sd = 10,                                            gps_spec = 1)  # Generate dataset with a t-distributed with 2df exposure given covariates data_sim_t <- generate_synthetic_data(sample_size = 400,                                       outcome_sd = 10,                                       gps_spec = 2)  tru_R <- function(w, sim_data) {   design_mt <- model.matrix(~cf1 + cf2 + cf3 + cf4 + cf5 + cf6 - 1,                             data = sim_data)   mean(apply(design_mt, 1, function(x) {     -10 - sum(c(2, 2, 3, -1, 2, 2) * x) -       w * (0.1 - 0.1 * x[1] + 0.1 * x[4] + 0.1 * x[5] + 0.1 * x[3] ^ 2) +       0.13 ^ 2 * w ^ 3   })) }  plot_fun <- function(object, ...) {     # extract data   tmp_data <- data.frame(w_vals = object$posterior$w,                          mean_vals = object$posterior$mean,                          sd_vals = object$posterior$sd)    g1 <- ggplot2::ggplot(tmp_data) +        ggplot2::geom_ribbon(ggplot2::aes(.data$w_vals,                                 y = .data$mean_vals,                                 ymin = .data$mean_vals - 1.96 * .data$sd_vals,                                 ymax = .data$mean_vals + 1.96 * .data$sd_vals),                                 fill = \"blue\", alpha = 0.25) +         ggplot2::geom_line(ggplot2::aes(.data$w_vals, .data$mean_vals),                            color = \"blue\", size = 1) +         ggplot2::theme_bw() +         ggplot2::ggtitle(\"Estimated CERF (gp) with credible band (1.96sd)\") +         ggplot2::xlab(\"Exposure level\") +         ggplot2::ylab(\"Population average counterfactual outcome\")    return(g1) }  erf_tru_normal <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_normal)) erf_tru_t <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_t))"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"train-gps-model","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Train GPS Model","title":"Standard Gaussian Processes","text":"GPCERF first convert covariate values single composite score (GPS) use fit Gaussian processes. use GPS estimation function CasaulGPS (see ) get GPS model maps covariates GPS. use estimate_cerf_gp estimate ERF exposure w. estimate ERF w = seq(0,20,0.1). estimated ERF well pointwise 95% credible band visualized call plot. also plot actual ERF top estimated ERF.","code":"gps_m_normal <- estimate_gps(cov_mt = data_sim_normal[, -(1:2)],                              w_all = data_sim_normal$treat,                              sl_lib = c(\"SL.xgboost\"),                              dnorm_log = FALSE) #> Loading required package: nnls  gps_m_t <- estimate_gps(cov_mt = data_sim_t[, -(1:2)],                         w_all = data_sim_t$treat,                         sl_lib = c(\"SL.xgboost\"),                         dnorm_log = FALSE)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"normally-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Normally Distributed Exposure","title":"Standard Gaussian Processes","text":"can see estimated curve follows true ERF closely 95% credible band completely covers true ERF.","code":"w_all <- seq(0, 20, 0.1)  gp_res_normal <- estimate_cerf_gp(data_sim_normal,                                   w_all,                                   gps_m_normal,                                   params = list(alpha = c(0.1),                                                 beta = 0.2,                                                 g_sigma = 1,                                                 tune_app = \"all\"),                                   nthread = 1) plot_fun(gp_res_normal) +   geom_line(data = data.frame(w = w_all, y = erf_tru_normal),             aes(x = w, y = y, color = \"True\"), size = 1.5) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"t-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"T-distributed Exposure","title":"Standard Gaussian Processes","text":"results look similar case exposure normally distributed. difference might exposure t-distributed, estimated curve tends less smooth.","code":"gp_res_t <- estimate_cerf_gp(data_sim_t,                              w_all,                              gps_m_t,                              params = list(alpha = c(0.1),                                            beta = 0.2,                                            g_sigma = 1,                                            tune_app = \"all\"),                              nthread = 1) plot_fun(gp_res_t) +   geom_line(data = data.frame(w = w_all, y = erf_tru_t),             aes(x = w, y = y, color = \"True\"), size = 1.5)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"GPCERF","text":"","code":"library(\"devtools\") install_github(\"NSAPH-Software/GPCERF\", ref = \"main\") library(\"GPCERF\")"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"estimating-gps-values","dir":"Articles","previous_headings":"Usage","what":"Estimating GPS values","title":"GPCERF","text":"Input parameters: cov_mt Covariate matrix containing covariates. row represents sample, column covariate.w_all vector observed exposure level.","code":"mydata <- generate_synthetic_data() gps_m <- estimate_gps(cov_mt = mydata[, c(\"cf1\", \"cf2\", \"cf3\", \"cf4\",                                          \"cf5\", \"cf6\")],                   w_all = mydata$treat,                   sl_lib = c(\"SL.xgboost\"),                   dnorm_log = FALSE) #> Loading required package: nnls"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"estimating-exposure-response-using-gaussian-processes","dir":"Articles","previous_headings":"Usage","what":"Estimating exposure response using Gaussian Processes","title":"GPCERF","text":"Input parameters: data data.frame observation data. - Column 1: Outcome (Y) - Column 2: Exposure treatment (w) - Column 3~m: Confounders (C)w vector exposure level compute CERF.gps_m data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred) - Column 3: Standard deviation e_gps (e_gps_std)params list parameters required run process. - alpha: scaling factor GPS value. - beta: scaling factor exposure value. - g_sigma: scaling factor kernel function (gamma/sigma). - tune_app: tuning approach. Available approaches: - : try combinations hyperparameters. alpha, beta, g_sigma can vector parameters. nthread integer value represents number threads used shared memory system.kernel_fn kernel function. default value Gaussian kernel.","code":"set.seed(129) sim_data <- generate_synthetic_data(sample_size = 400, gps_spec = 3)  # Estimate GPS function gps_m <- estimate_gps(cov_mt = sim_data[, -(1:2)],                       w_all = sim_data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = TRUE)  # exposure values q1 <- stats::quantile(sim_data$treat, 0.05) q2 <- stats::quantile(sim_data$treat, 0.95)  w_all <- seq(q1, q2, 1)  params_lst <- list(alpha = c(0.05),                    beta = c(12),                    g_sigma = c(0.1),                    tune_app = \"all\") cerf_gp_obj <- estimate_cerf_gp(sim_data,                                w_all,                                gps_m,                                params = params_lst,                                nthread = 1) summary(cerf_gp_obj) #> GPCERF standard Gaussian grocess exposure response function object #>  #> Optimal hyper parameters(#trial: 1):  #>   alpha = 0.05   beta = 12   g_sigma = 0.1 #>  #> Optimal covariate balance:  #>   cf1 = 0.080  #>   cf2 = 0.081  #>   cf3 = 0.111  #>   cf4 = 0.119  #>   cf5 = 0.084  #>   cf6 = 0.104 #>  #> Original covariate balance:  #>   cf1 = 0.185  #>   cf2 = 0.161  #>   cf3 = 0.106  #>   cf4 = 0.208  #>   cf5 = 0.246  #>   cf6 = 0.283 #>             ----***---- plot(cerf_gp_obj)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"estimating-exposure-response-using-nearest-neighbor-gaussian-processes","dir":"Articles","previous_headings":"Usage","what":"Estimating exposure response using nearest-neighbor Gaussian Processes","title":"GPCERF","text":"Input parameters: data data.frame observation data. - Column 1: Outcome (Y) - Column 2: Exposure treatment (w) - Column 3~m: Confounders (C)w vector exposure level compute CERF.gps_m data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred) - Column 3: Standard deviation e_gps (e_gps_std)params list parameters required run process. - alpha: scaling factor GPS value. - beta: scaling factor exposure value. - g_sigma: scaling factor kernel function (gamma/sigma). - tune_app: tuning approach. Available approaches: - : try combinations hyperparameters. - n_neighbors: Number nearest neighbors one side. - block_size: Number samples included computation block. Mainly used balance speed memory requirement. Larger faster, requires memory. alpha, beta, g_sigma can vector parameters. nthread integer value represents number threads used shared memory system.kernel_fn kernel function. default value Gaussian kernel.","code":"set.seed(19) sim_data <- generate_synthetic_data(sample_size = 1000, gps_spec = 3)  # Estimate GPS function gps_m <- estimate_gps(cov_mt = sim_data[, -(1:2)],                       w_all = sim_data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = TRUE) # exposure values q1 <- stats::quantile(sim_data$treat, 0.05) q2 <- stats::quantile(sim_data$treat, 0.95)  w_all <- seq(q1, q2, 1)  params_lst <- list(alpha = c(0.05),                    beta = c(12),                    g_sigma = c(0.1),                    tune_app = \"all\",                    n_neighbor = 20,                    block_size = 1e4) cerf_nngp_obj <- estimate_cerf_nngp(sim_data,                                     w_all,                                     gps_m,                                     params = params_lst,                                     nthread = 1)  summary(cerf_nngp_obj) #> GPCERF nearest neighbore Gaussian process exposure response function object summary #>  #> Optimal hyper parameters(#trial: 1):  #>   alpha = 0.05   beta = 12   g_sigma = 0.1 #>  #> Optimal covariate balance:  #>   cf1 = 0.118  #>   cf2 = 0.103  #>   cf3 = 0.105  #>   cf4 = 0.089  #>   cf5 = 0.159  #>   cf6 = 0.149 #>  #> Original covariate balance:  #>   cf1 = 0.136  #>   cf2 = 0.148  #>   cf3 = 0.105  #>   cf4 = 0.228  #>   cf5 = 0.184  #>   cf6 = 0.280 #>             ----***---- plot(cerf_nngp_obj)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"logging","dir":"Articles","previous_headings":"Usage","what":"Logging","title":"GPCERF","text":"GPCERF package logs internal activities screen GPCERF.log file. Users can change logging file name (path) logging threshold. logging mechanism different thresholds (see logger package). two important thresholds INFO DEBUG levels. former, default level, logs general information process. latter, activated, logs detailed information can used debugging purposes. log file located source file location appended.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"generate-synthetic-data","dir":"Articles","previous_headings":"","what":"Generate Synthetic Data","title":"Nearest-neighbor Gaussian Processes","text":"first generate synthetic dataset six covariates, one continuous exposure one outcome. consider two types conditional distributions (normal student’s t) exposure given covariates. details synthetic data generation, see document. use function tru_R derive actual ERF population w=seq(0,20,0.1).","code":"set.seed(1) # Generate dataset with a normally distributed exposure given covariates data_sim_normal <- generate_synthetic_data(sample_size = 500,                                            outcome_sd = 10,                                            gps_spec = 1) # Generate dataset with a t-distributed with 2df exposure given covariates data_sim_t <- generate_synthetic_data(sample_size = 500,                                       outcome_sd = 10,                                       gps_spec = 2) tru_R <- function(w, sim_data) {   design_mt <- model.matrix(~cf1 + cf2 + cf3 + cf4 + cf5 + cf6 - 1,                             data = sim_data)   mean(apply(design_mt, 1, function(x) {     -10 - sum(c(2, 2, 3, -1, 2, 2) * x) -      w * (0.1 - 0.1 * x[1] + 0.1 * x[4] + 0.1 * x[5] + 0.1 * x[3] ^ 2) +      0.13 ^ 2 * w ^ 3   })) }  plot_fun <- function(object, ...) {   tmp_data <- data.frame(w_vals = object$posterior$w,                          mean_vals = object$posterior$mean,                          sd_vals = object$posterior$sd)    g1 <- ggplot2::ggplot(tmp_data) +         ggplot2::geom_ribbon(ggplot2::aes(.data$w_vals,                                 y = .data$mean_vals,                                 ymin = .data$mean_vals - 1.96 * .data$sd_vals,                                 ymax = .data$mean_vals + 1.96 * .data$sd_vals),                                 fill = \"#FC4E07\", alpha = 0.25) +         ggplot2::geom_line(ggplot2::aes(.data$w_vals, .data$mean_vals),                            color = \"#FC4E07\",                            size = 1) +         ggplot2::theme_bw() +         ggplot2::ggtitle(\"Estimated CERF (nngp) with credible band (1.96sd)\") +         ggplot2::xlab(\"Exposure level\") +         ggplot2::ylab(\"Population average counterfactual outcome\")    return(g1) }   erf_tru_normal <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_normal)) erf_tru_t <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_t))"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"train-gps-model","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Train GPS Model","title":"Nearest-neighbor Gaussian Processes","text":"GPCERF first convert covariate values single composite score (GPS) use fit Gaussian processes. use GPS estimation function CausalGPS (see ) get GPS model maps covariates GPS. use estimate_cerf_nngp estimate ERF exposure w. estimate ERF w = seq(0,20,0.1). estimated ERF well pointwise 95% credible band visualized call plot. also plot actual ERF top estimated ERF.","code":"gps_m_normal <- estimate_gps(cov_mt = data_sim_normal[, -(1:2)],                              w_all = data_sim_normal$treat,                              sl_lib = c(\"SL.xgboost\"),                              dnorm_log = FALSE) #> Loading required package: nnls gps_m_t <- estimate_gps(cov_mt = data_sim_t[, -(1:2)],                         w_all = data_sim_t$treat,                         sl_lib = c(\"SL.xgboost\"),                         dnorm_log = FALSE)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"normally-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Normally Distributed Exposure","title":"Nearest-neighbor Gaussian Processes","text":"can see estimated curve follows true ERF closely 95% credible band completely covers true ERF. running time much smaller standard GP approach.","code":"w_all <-  seq(0, 20, 0.1) nngp_res_normal <- estimate_cerf_nngp(data_sim_normal,                                       w_all,                                       gps_m_normal,                                       params = list(alpha = c(0.1),                                                     beta = 0.2,                                                     g_sigma = 1,                                                     n_neighbor = 20,                                                     block_size = 50,                                                     tune_app = \"all\"),                                       nthread = 1) plot_fun(nngp_res_normal) +   geom_line(data = data.frame(w = w_all, y = erf_tru_normal),             aes(x = w, y = y, color = \"True\"), size = 1.5) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"t-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"T-distributed Exposure","title":"Nearest-neighbor Gaussian Processes","text":"results look similar case exposure normally distributed. difference might exposure t-distributed, estimated curve tends less smooth.","code":"nngp_res_t <- estimate_cerf_nngp(data_sim_t,                                  w_all,                                  gps_m_t,                                  params = list(alpha = c(0.1),                                                beta = 0.2,                                                g_sigma = 1,                                                n_neighbor = 20,                                                block_size = 50,                                                tune_app = \"all\"),                                 nthread = 1) plot_fun(nngp_res_t) +   geom_line(data = data.frame(w = w_all, y = erf_tru_t),             aes(x = w, y = y, color = \"True\"), size = 1.5)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"generate-synthetic-data","dir":"Articles","previous_headings":"","what":"Generate Synthetic Data","title":"Standard Gaussian Processes","text":"first generate synthetic dataset six covariates, one continuous exposure one outcome. consider two types conditional distributions (normal student’s t) exposure given covariates. details synthetic data generation, see document. use function tru_R derive actual ERF population w=seq(0,20,0.1).","code":"set.seed(134) # Generate dataset with a normally distributed exposure given covariates data_sim_normal <- generate_synthetic_data(sample_size = 400,                                            outcome_sd = 10,                                            gps_spec = 1)  # Generate dataset with a t-distributed with 2df exposure given covariates data_sim_t <- generate_synthetic_data(sample_size = 400,                                       outcome_sd = 10,                                       gps_spec = 2)  tru_R <- function(w, sim_data) {   design_mt <- model.matrix(~cf1 + cf2 + cf3 + cf4 + cf5 + cf6 - 1,                             data = sim_data)   mean(apply(design_mt, 1, function(x) {     -10 - sum(c(2, 2, 3, -1, 2, 2) * x) -       w * (0.1 - 0.1 * x[1] + 0.1 * x[4] + 0.1 * x[5] + 0.1 * x[3] ^ 2) +       0.13 ^ 2 * w ^ 3   })) }  plot_fun <- function(object, ...) {     # extract data   tmp_data <- data.frame(w_vals = object$posterior$w,                          mean_vals = object$posterior$mean,                          sd_vals = object$posterior$sd)    g1 <- ggplot2::ggplot(tmp_data) +        ggplot2::geom_ribbon(ggplot2::aes(.data$w_vals,                                 y = .data$mean_vals,                                 ymin = .data$mean_vals - 1.96 * .data$sd_vals,                                 ymax = .data$mean_vals + 1.96 * .data$sd_vals),                                 fill = \"blue\", alpha = 0.25) +         ggplot2::geom_line(ggplot2::aes(.data$w_vals, .data$mean_vals),                            color = \"blue\", size = 1) +         ggplot2::theme_bw() +         ggplot2::ggtitle(\"Estimated CERF (gp) with credible band (1.96sd)\") +         ggplot2::xlab(\"Exposure level\") +         ggplot2::ylab(\"Population average counterfactual outcome\")    return(g1) }  erf_tru_normal <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_normal)) erf_tru_t <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_t))"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"train-gps-model","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Train GPS Model","title":"Standard Gaussian Processes","text":"GPCERF first convert covariate values single composite score (GPS) use fit Gaussian processes. use GPS estimation function CausalGPS (see ) get GPS model maps covariates GPS. use estimate_cerf_gp estimate ERF exposure w. estimate ERF w = seq(0,20,0.1). estimated ERF well pointwise 95% credible band visualized call plot. also plot actual ERF top estimated ERF.","code":"gps_m_normal <- estimate_gps(cov_mt = data_sim_normal[, -(1:2)],                              w_all = data_sim_normal$treat,                              sl_lib = c(\"SL.xgboost\"),                              dnorm_log = FALSE) #> Loading required package: nnls  gps_m_t <- estimate_gps(cov_mt = data_sim_t[, -(1:2)],                         w_all = data_sim_t$treat,                         sl_lib = c(\"SL.xgboost\"),                         dnorm_log = FALSE)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"normally-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Normally Distributed Exposure","title":"Standard Gaussian Processes","text":"can see estimated curve follows true ERF closely 95% credible band completely covers true ERF.","code":"w_all <- seq(0, 20, 0.1)  gp_res_normal <- estimate_cerf_gp(data_sim_normal,                                   w_all,                                   gps_m_normal,                                   params = list(alpha = c(0.1),                                                 beta = 0.2,                                                 g_sigma = 1,                                                 tune_app = \"all\"),                                   nthread = 1) plot_fun(gp_res_normal) +   geom_line(data = data.frame(w = w_all, y = erf_tru_normal),             aes(x = w, y = y, color = \"True\"), size = 1.5) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"t-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"T-distributed Exposure","title":"Standard Gaussian Processes","text":"results look similar case exposure normally distributed. difference might exposure t-distributed, estimated curve tends less smooth.","code":"gp_res_t <- estimate_cerf_gp(data_sim_t,                              w_all,                              gps_m_t,                              params = list(alpha = c(0.1),                                            beta = 0.2,                                            g_sigma = 1,                                            tune_app = \"all\"),                              nthread = 1) plot_fun(gp_res_t) +   geom_line(data = data.frame(w = w_all, y = erf_tru_t),             aes(x = w, y = y, color = \"True\"), size = 1.5)"},{"path":"https://NSAPH-Software.github.io/GPCERF/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            HUIT Boyu Ren. Author.            McLean Hospital Tanujit Dey. Contributor.            HMS Danielle Braun. Author.            HSPH","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Khoshnevis N, Ren B, Braun D (2023). GPCERF: Gaussian Processes Estimating Causal Exposure Response Curves. R package version 0.2.0, https://CRAN.R-project.org/package=GPCERF.","code":"@Manual{,   title = {GPCERF: Gaussian Processes for Estimating Causal Exposure Response Curves},   author = {Naeem Khoshnevis and Boyu Ren and Danielle Braun},   year = {2023},   note = {R package version 0.2.0},   url = {https://CRAN.R-project.org/package=GPCERF}, }"},{"path":[]},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/index.html","id":"summary","dir":"","previous_headings":"","what":"Summary","title":"Gaussian Process (GP) approach for nonparametric modeling.","text":"Gaussian Process (GP) approach nonparametric modeling. Provides non-parametric Bayesian framework based Gaussian process priors estimating causal effects continuous exposure detecting change points causal exposure response curves using observational data.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Gaussian Process (GP) approach for nonparametric modeling.","text":"Ren, B., Wu, X., Braun, D., Pillai, N. Dominici, F., 2021. Bayesian modeling exposure response curve via gaussian processes: Causal effects exposure air pollution health outcomes. arXiv preprint arXiv:2105.03454.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/GPCERF-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The 'GPCERF' package. — GPCERF-package","title":"The 'GPCERF' package. — GPCERF-package","text":"Provides non-parametric Bayesian framework based Gaussian process priors estimating causal effects continuous exposure detecting change points causal exposure response curves using observational data.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/GPCERF-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The 'GPCERF' package. — GPCERF-package","text":"Ren, B., Wu, X., Braun, D., Pillai, N. Dominici, F., 2021. Bayesian modeling exposure response curve via gaussian processes: Causal effects exposure air pollution health outcomes. arXiv preprint arXiv:2105.03454.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/GPCERF-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The 'GPCERF' package. — GPCERF-package","text":"Naeem Khoshnevis Boyu Ren Danielle Braun","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cerf_gp object — autoplot.cerf_gp","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"helper function plot cerf_gp object using ggplot2 package.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"","code":"# S3 method for cerf_gp autoplot(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"object cerf_gp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"Returns ggplot object.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cerf_nngp object — autoplot.cerf_nngp","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"helper function plot cerf_nngp object using ggplot2 package.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"","code":"# S3 method for cerf_nngp autoplot(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"object cerf_nngp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"Returns ggplot object.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate covariate balance — calc_ac","title":"Calculate covariate balance — calc_ac","text":"Calculates weighted correlation list covariates exposure. covariance function GP defines weights.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate covariate balance — calc_ac","text":"","code":"calc_ac(w, X, weights)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate covariate balance — calc_ac","text":"w vector exposure values across subjects. X matrix covariate values. Subjects rows covariates columns. weights vector weights assigned subjects based trained GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate covariate balance — calc_ac","text":"vector correlations w column X.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"Calculates posterior mean derivative CERF given exposure level nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"","code":"compute_deriv_nn(   w,   w_obs,   gps_m,   y_obs,   hyperparam,   n_neighbor,   block_size,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE y_obs vector observed outcome values. hyperparam vector hyper-parameters GP model. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. kernel_fn covariance function. input square Euclidean distance. kernel_deriv_fn partial derivative covariance function. input square Euclidean distance.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"scalar estimated derivative CERF w nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate derivatives of CERF — compute_deriv_weights_gp","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"Calculates weights assigned observed outcome deriving posterior mean first derivative CERF given exposure level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"","code":"compute_deriv_weights_gp(   w,   w_obs,   gps_m,   hyperparam,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE hyperparam vector hyper-parameters GP model. kernel_fn covariance function. kernel_deriv_fn partial derivative covariance function.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"vector weights samples, based posterior mean derivative CERF exposure level interest calculated.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute matrix inverse for a covariate matrix — compute_inverse","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"Computes inverse covariate matrix using Choleski decomposition.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"","code":"compute_inverse(mtrx)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"mtrx n * n covariate matrix","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"matrix represent inverse input matrix.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"Calculates induced covariate balance associated one hyper-parameter configuration standard GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"","code":"compute_m_sigma(   hyperparam,   data,   w,   gps_m,   tuning,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"hyperparam vector values hyper-parameters. First element: alpha Second element: beta Third element: g_sigma (gamma / sigma) data  data.frame containing data including outcome, exposure covariates. following order: Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) w vector exposure levels CERF estimated. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE tuning function used parameter tuning (default = TRUE) estimation (FALSE) kernel_fn covariance function GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"list containing two elements: vector absolute weighted correlation covariate exposure, metric covariate balance estimated CERF w_all based hyper-parameter values param.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate posterior means for nnGP model — compute_posterior_m_nn","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"Calculates posterior mean point CERF based nnGP model. function also returns weights assigned nearest neighbors calculating posterior mean.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"","code":"compute_posterior_m_nn(   hyperparam,   w,   gps_w,   obs_ord,   y_obs_ord,   kernel_fn = function(x) exp(-x^2),   n_neighbor = 10,   block_size = 10000 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"hyperparam set hyperparameters GP model. w scaler representing exposure level point interest CERF. gps_w GPS samples exposure levels set w. obs_ord matrix two columns. First column observed exposure levels samples; second GPS observed exposure levels. rows ascending order first column. y_obs_ord vector observed outcome values. vector ordered obs_ord. kernel_fn covariance function GP. n_neighbor number nearest neighbors one side. block_size Number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"TODO: first column selected index second column weight. two-column matrix. first column weights assigned nearest neighbor. second column corresponding observed outcome value. weight last row matrix NA observed outcome value estimated posterior mean CERF point w, weighted sum observed outcome values neighbors.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"Calculates posterior standard deviation point CERF based nnGP model.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"","code":"compute_posterior_sd_nn(   hyperparam,   w,   gps_w,   obs_ord,   sigma2,   kernel_fn = function(x) exp(-x^2),   n_neighbor = 10,   block_size = 10000 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"hyperparam values hyperparameters GP model. w exposure level point interest CERF. gps_w GPS samples exposure levels set w. obs_ord matrix two columns. first column observed exposure levels samples; second GPS observed exposure levels. rows ascending order first column. sigma2 scaler representing sigma^2. kernel_fn covariance function GP. n_neighbor Number nearest neighbors one side. block_size Number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"posterior standard deviation estimated CERF w.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect change-point in standard GP — compute_rl_deriv_gp","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"Calculates posterior mean difference left- right-derivatives exposure level detection change points.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"","code":"compute_rl_deriv_gp(   w,   w_obs,   y_obs,   gps_m,   hyperparam,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. y_obs vector observed outcome values samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE hyperparam vector hyper-parameters GP model. kernel_fn covariance function. kernel_deriv_fn partial derivative covariance function.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"numeric value posterior mean difference two one-sided derivatives.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"","code":"# \\donttest{ set.seed(847) data <- generate_synthetic_data(sample_size = 100) gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE) #> Loading required package: nnls  wi <- 8.6  val <- compute_rl_deriv_gp(w = wi,                            w_obs = data$treat,                            y_obs = data$Y,                            gps_m = gps_m,                            hyperparam = c(1,1,2)) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"Calculates posterior mean difference left- right-derivatives exposure level detection change points. nnGP approximation used.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"","code":"compute_rl_deriv_nn(   w,   w_obs,   gps_m,   y_obs,   hyperparam,   n_neighbor,   block_size,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE y_obs vector observed outcome values. hyperparam vector hyper-parameters GP model. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. kernel_fn covariance function. input square Euclidean distance. kernel_deriv_fn partial derivative covariance function. input square Euclidean distance.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"numeric value posterior mean difference two one-sided derivatives.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"","code":"# \\donttest{ set.seed(325) data <- generate_synthetic_data(sample_size = 200) gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE)  wi <- 12.2  deriv_val <- compute_rl_deriv_nn(w = wi,                                  w_obs = data$treat,                                  gps_m = gps_m,                                  y_obs = data$Y,                                  hyperparam = c(0.2,0.4,1.2),                                  n_neighbor = 20,                                  block_size = 10) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute posterior credible interval — compute_sd_gp","title":"Compute posterior credible interval — compute_sd_gp","text":"Computes posterior credible interval requested exposure level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute posterior credible interval — compute_sd_gp","text":"","code":"compute_sd_gp(   w,   scaled_obs,   hyperparam,   sigma,   gps_m,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute posterior credible interval — compute_sd_gp","text":"w scalar exposure level interest. scaled_obs matrix two columns. First column scaled GPS value samples (GPS * 1/sqrt(alpha)) Second column scaled exposure value samples (w * 1/sqrt(beta)) hyperparam vector hyper-parameters GP. First element: alpha Second element: beta Third element: gamma/sigma sigma scaler represents noise. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE kernel_fn covariance function GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute posterior credible interval — compute_sd_gp","text":"Posterior credible interval (scaler) requested exposure level (w).","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute  weighted covariate balance — compute_w_corr","title":"Compute  weighted covariate balance — compute_w_corr","text":"Computes weighted covariate balance given data sets.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute  weighted covariate balance — compute_w_corr","text":"","code":"compute_w_corr(w, covariate, weight)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute  weighted covariate balance — compute_w_corr","text":"w vector observed continuous exposure variable. covariate data frame observed covariates variable. weight vector weights.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute  weighted covariate balance — compute_w_corr","text":"function returns list saved measure related covariate balance absolute_corr: absolute correlations pre-exposure covairates; mean_absolute_corr: average absolute correlations pre-exposure covairates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute  weighted covariate balance — compute_w_corr","text":"","code":"set.seed(639) n <- 100 mydata <- generate_synthetic_data(sample_size=100) year <- sample(x=c(\"2001\",\"2002\",\"2003\",\"2004\",\"2005\"),size = n,  replace = TRUE) region <- sample(x=c(\"North\", \"South\", \"East\", \"West\"),size = n,  replace = TRUE) mydata$year <- as.factor(year) mydata$region <- as.factor(region) mydata$cf5 <- as.factor(mydata$cf5) cor_val <- compute_w_corr(mydata[,2],                           mydata[, 3:length(mydata)],                           runif(n))  print(cor_val$mean_absolute_corr) #> [1] 0.1461331"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate weights for estimation of a point on CERF — compute_weight_gp","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"Calculates weights observed outcomes used estimate posterior mean CERF given exposure level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"","code":"compute_weight_gp(   w,   w_obs,   scaled_obs,   hyperparam,   inv_sigma_obs,   gps_m,   est_sd = FALSE,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. scaled_obs matrix two columns. First column scaled GPS value samples (GPS * 1 / sqrt(alpha)) Second column scaled exposure value samples (w * 1/sqrt(beta)) hyperparam vector hyper-parameters GP. First element: alpha Second element: beta Third element: gamma/sigma inv_sigma_obs Inverse covariance matrix observed samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE est_sd posterior se computed (default=FALSE) kernel_fn covariance function GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"list two elements, weight standard deviation.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"Estimates conditional exposure response function (cerf) using Gaussian Process (gp). function tune best match (lowest covariate balance) provided set hyperparameters.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"","code":"estimate_cerf_gp(   data,   w,   gps_m,   params,   nthread = 1,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"data data.frame observation data. Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) w vector exposure level compute CERF. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE params list parameters required run process. parameters include: alpha: scaling factor GPS value. beta: scaling factor exposure value. g_sigma: scaling factor kernel function (gamma/sigma). tune_app: tuning approach. Available approaches: : try combinations hyperparameters. alpha, beta, g_sigma can vector parameters. nthread integer value represents number threads used internal packages. kernel_fn kernel function. default value Gaussian Kernel.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"cerf_gp object includes following values: w, vector exposure levels. pst_mean, Computed mean w vector. pst_sd, Computed credible interval w vector.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"","code":"# \\donttest{ set.seed(129) data <- generate_synthetic_data(sample_size = 100, gps_spec = 3)   # Estimate GPS function gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE)  # exposure values w_all <- seq(0,10,1)   cerf_gp_obj <- estimate_cerf_gp(data,                                 w_all,                                 gps_m,                                 params = list(alpha = c(0.1),                                               beta=0.2,                                               g_sigma = 1,                                               tune_app = \"all\"),                                 nthread = 1) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"Estimates conditional exposure response function (cerf) using nearest neighbor (nn) Gaussian Process (gp). function tune best match (lowest covariate balance) provided set hyperparameters.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"","code":"estimate_cerf_nngp(   data,   w,   gps_m,   params,   kernel_fn = function(x) exp(-x^2),   nthread = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"data data.frame observation data. Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) w vector exposure level compute CERF. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE params list parameters required run process. parameters include: alpha: scaling factor GPS value. beta: scaling factor exposure value. g_sigma: scaling factor kernel function (gamma/sigma). tune_app: tuning approach. Available approaches: : try combinations hyperparameters. n_neighbor: Number nearest neighbors one side. block_size: Number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. alpha, beta, g_sigma can vector parameters. kernel_fn kernel function. default value Gaussian Kernel. nthread integer value represents number threads used internal packages.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"cerf_nngp object includes following values: w, vector exposure levels. pst_mean, computed mean w vector. pst_sd, computed credible interval w vector.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"","code":"# \\donttest{ set.seed(19) data <- generate_synthetic_data(sample_size = 120, gps_spec = 3) # Estimate GPS function gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE) # exposure values w.all <- seq(0,20,2) cerf_nngp_obj <- estimate_cerf_nngp(data,                                     w.all,                                     gps_m,                                     params = list(alpha = c(0.1),                                                   beta = 0.2,                                                   g_sigma = 1,                                                   tune_app = \"all\",                                                   n_neighbor = 20,                                                   block_size = 1e4),                                     nthread = 1) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a model for generalized propensity score — estimate_gps","title":"Estimate a model for generalized propensity score — estimate_gps","text":"Estimates model generalized propensity score (GPS) using parametric approach.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a model for generalized propensity score — estimate_gps","text":"","code":"estimate_gps(cov_mt, w_all, sl_lib, dnorm_log)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a model for generalized propensity score — estimate_gps","text":"cov_mt covariate matrix containing covariates. row data sample column covariate. w_all vector observed exposure levels. sl_lib vector SuperLearner's package libraries. dnorm_log Logical, TRUE, probabilities p given log(p).","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a model for generalized propensity score — estimate_gps","text":"data.frame includes: vector estimated GPS observed exposure levels; vector estimated conditional means exposure levels covariates fixed observed values; estimated standard deviation exposure levels vector observed exposure levels.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a model for generalized propensity score — estimate_gps","text":"","code":"# \\donttest{ data <- generate_synthetic_data(sample_size = 200) gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"Estimates posterior mean conditional exposure response function specified exposure levels nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"","code":"estimate_mean_sd_nn(   hyperparam,   sigma2,   w_obs,   w,   y_obs,   gps_m,   kernel_fn = function(x) exp(-x^2),   n_neighbor = 50,   block_size = 2000,   nthread = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"hyperparam set hyperparameters nnGP. sigma2 scaler representing sigma^2. w_obs vector observed exposure levels. w vector exposure levels CERF estimated. y_obs vector observed outcome values. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE kernel_fn covariance function GP. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. nthread integer value represents number threads used internal packages.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"vector returned value compute_posterior_sd_nn.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"Estimates standard deviations nugget term standard GP calculating standard deviations residuals.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"","code":"estimate_noise_gp(data, sigma_obs, inv_sigma_obs)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"data data.frame observation data. Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) sigma_obs Covariance matrix observed covariates. inv_sigma_obs Inverse covariance matrix observed covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"scalar estimated standard deviation nugget term standard GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"Estimates standard deviations nugget term (noise) nnGP calculating standard deviations residuals.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"","code":"estimate_noise_nn(   hyperparam,   w_obs,   GPS_obs,   y_obs,   n_neighbor,   nthread,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"hyperparam vector hyper-parameter values. w_obs vector observed exposure levels. GPS_obs vector estimated GPS evaluated observed exposure levels. y_obs vector observed outcomes. n_neighbor number nearest neighbors one side. kernel_fn covariance function GP. n_thread number cores used estimation.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"scalar estimated standard deviation nugget term nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"Computes covariate balance combination provided hyper-parameters selects hyper-parameter values minimizes covariate balance.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"","code":"find_optimal_nn(   w_obs,   w,   y_obs,   gps_m,   design_mt,   hyperparams = expand.grid(seq(0.5, 4.5, 1), seq(0.5, 4.5, 1), seq(0.5, 4.5, 1)),   kernel_fn = function(x) exp(-x^2),   n_neighbor = 50,   block_size = 2000,   nthread = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"w_obs vector observed exposure levels. w vector exposure levels CERF estimated. y_obs vector observed outcomes gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE design_mt covariate matrix samples (intercept excluded). hyperparams matrix candidate values hyper-parameters, row contains set values hyper-parameters. kernel_fn covariance function GP. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. nthread integer value represents number threads used internal packages.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"Estimated covariate balance scores grid hyper-parameter values considered hyperparams.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate synthetic data for the GPCERF package — generate_synthetic_data","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"Generates synthetic data set based different GPS models covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"","code":"generate_synthetic_data(   sample_size = 1000,   outcome_sd = 10,   gps_spec = 1,   cova_spec = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"sample_size number data samples. outcome_sd Standard deviation used generate outcome synthetic data set. gps_spec numeric value (1-6) indicates GPS model used generate continuous exposure. cova_spec numeric value (1-2) modify covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"data frame synthetic data. Outcome labeled Y, exposure w, covariates cf1-6.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"","code":"set.seed(351) data <- generate_synthetic_data(sample_size = 200)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get logger settings — get_logger","title":"Get logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/log_system_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Log system information — log_system_info","title":"Log system information — log_system_info","text":"Logs system related information log file.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/log_system_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log system information — log_system_info","text":"","code":"log_system_info()"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/log_system_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log system information — log_system_info","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"wrapper function extend generic plot functions cerf_gp class.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"","code":"# S3 method for cerf_gp plot(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"x cerf_gp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"wrapper function extend generic plot functions cerf_nngp class.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"","code":"# S3 method for cerf_nngp plot(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"x cerf_nngp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for cerf_gp object — print.cerf_gp","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"Extend print function cerf_gp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"","code":"# S3 method for cerf_gp print(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"x cerf_gp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for cerf_nngp object — print.cerf_nngp","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"Extend print function cerf_nngp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"","code":"# S3 method for cerf_nngp print(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"x cerf_nngp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set logger settings — set_logger","title":"Set logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"GPCERF.log\", logger_level = \"INFO\")"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: GPCERF.log) logger_level log level. Available levels include: TRACE DEBUG INFO (Default) SUCCESS WARN ERROR FATAL","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set logger settings — set_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\")"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"print summary of cerf_gp object — summary.cerf_gp","title":"print summary of cerf_gp object — summary.cerf_gp","text":"print summary cerf_gp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print summary of cerf_gp object — summary.cerf_gp","text":"","code":"# S3 method for cerf_gp summary(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print summary of cerf_gp object — summary.cerf_gp","text":"object cerf_gp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print summary of cerf_gp object — summary.cerf_gp","text":"Returns summary data","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"print summary of cerf_nngp object — summary.cerf_nngp","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"print summary cerf_nngp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"","code":"# S3 method for cerf_nngp summary(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"object cerf_nngp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"Returns summary data.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model for generalized propensity score — train_gps","title":"Train a model for generalized propensity score — train_gps","text":"Estimates conditional mean sd exposure level function covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model for generalized propensity score — train_gps","text":"","code":"train_gps(cov_mt, w_all, sl_lib, dnorm_log)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model for generalized propensity score — train_gps","text":"cov_mt covariate matrix containing covariates. row data sample column covariate. w_all vector observed exposure levels. sl_lib vector SuperLearner's package libraries. dnorm_log Logical, TRUE, probabilities p given log(p).","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model for generalized propensity score — train_gps","text":"data.frame includes: vector estimated GPS observed exposure levels; vector estimated conditional means exposure levels covariates fixed observed values; estimated standard deviation exposure levels","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train a model for generalized propensity score — train_gps","text":"","code":"# \\donttest{ data <- generate_synthetic_data(sample_size = 200) GPS_m <- train_gps(cov_mt = data[,-(1:2)],                   w_all = data$treat,                   sl_lib = c(\"SL.xgboost\"),                   dnorm_log = FALSE) # }"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.2.1 (2023-01-15)","text":"full GP –> standard GP plots exposure response function objects include covariate balance. formula longer need nn functions. estimate_gps now returns used exposure level, . train_gps –> estimate_gps nearest neighbor approach get expand input parameter (n_neighbor * expand –> n_neighbor). weighted covariate balance now computed using wCorr package.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"gpcerf-020-2023-01-22","dir":"Changelog","previous_headings":"","what":"GPCERF 0.2.0 (2023-01-22)","title":"GPCERF 0.2.0 (2023-01-22)","text":"CRAN release: 2023-01-22","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.2.0 (2023-01-22)","text":"estimate_noise_nn now allows parallelization added argument nthread number CPUs used parallel. estimate_mean_sd_nn now computes posterior variance. find_optimal_nn now returns posterior mean covariate balance optimal hyper-parameter values. Add argument kernel_fn nn related functions allow user-defined kernel functions. Add argument formula nn related functions allow user-defined design matrix. find_optimal_nn becomes internal function. estimate_noise_gp estimate_noise_nn become internal functions. estimate_mean_sd_nn becomes internal function. compute_weight_gp becomes internal function. compute_w_corr accepts w confounders separately. also normalizes w internally. compute_posterior_sd_nn becomes internal function. compute_posterior_m_nn becomes internal function. compute_derive_weights_gp becomes internal function. compute_m_sigma becomes internal function. compute_inverse becomes internal function. compute_m_sigma, tuning option default value. train_gps default values. train_gps accepts vector SuperLearner package’s libraries. train_GPS -> train_gps","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"gpcerf-010-2022-07-02","dir":"Changelog","previous_headings":"","what":"GPCERF 0.1.0 (2022-07-02)","title":"GPCERF 0.1.0 (2022-07-02)","text":"CRAN release: 2022-07-02","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.1.0 (2022-07-02)","text":"nn_cp_calc -> compute_rl_deriv_nn deriv_nn_fast -> compute_deriv_nn get_nn_sd -> compute_posterior_sd_nn nn_sigma_est -> estimate_noise_nn idx.-> idx_select GPS.new -> GPS_w w.new -> w get.nn.fast -> compute_posterior_m_nn w.est -> w nn_balance -> best_nn_cb","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"GPCERF 0.1.0 (2022-07-02)","text":"Package website using pkgdown Logger functions compute_sd_gp function","code":""},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.0.1 (2022-03-31)","text":"Removed examples internal functions w.obs -> w_obs inv.Sigma.obs -> inv_sigma_obs obs.use -> scaled_obs tune.fn -> compute_m_sigma GP.weights.test -> compute_weight_gp data.generate -> generate_synthetic_data","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"GPCERF 0.0.1 (2022-03-31)","text":"estimate_noise function estimate_cerf_gp function compute_inverse function compute_w_corr function","code":""}]
+[{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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Definitions","title":"GNU General Public License","text":"“License” refers version 3 GNU General Public License. “Copyright” also means copyright-like laws apply kinds works, semiconductor masks. “Program” refers copyrightable work licensed License. licensee addressed “”. “Licensees” “recipients” may individuals organizations. “modify” work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called “modified version” earlier work work “based ” earlier work. “covered work” means either unmodified Program work based Program. “propagate” work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. “convey” work means kind propagation enables parties make receive copies. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. 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GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"generate-synthetic-data","dir":"Articles","previous_headings":"","what":"Generate Synthetic Data","title":"Standard Gaussian Processes","text":"first generate synthetic dataset six covariates, one continuous exposure one outcome. consider two types conditional distributions (normal student’s t) exposure given covariates. details synthetic data generation, see document. use function tru_R derive actual ERF population w=seq(0,20,0.1).","code":"set.seed(134) # Generate dataset with a normally distributed exposure given covariates data_sim_normal <- generate_synthetic_data(sample_size = 400,                                            outcome_sd = 10,                                            gps_spec = 1)  # Generate dataset with a t-distributed with 2df exposure given covariates data_sim_t <- generate_synthetic_data(sample_size = 400,                                       outcome_sd = 10,                                       gps_spec = 2)  tru_R <- function(w, sim_data) {   design_mt <- model.matrix(~cf1 + cf2 + cf3 + cf4 + cf5 + cf6 - 1,                             data = sim_data)   mean(apply(design_mt, 1, function(x) {     -10 - sum(c(2, 2, 3, -1, 2, 2) * x) -       w * (0.1 - 0.1 * x[1] + 0.1 * x[4] + 0.1 * x[5] + 0.1 * x[3] ^ 2) +       0.13 ^ 2 * w ^ 3   })) }  plot_fun <- function(object, ...) {     # extract data   tmp_data <- data.frame(w_vals = object$posterior$w,                          mean_vals = object$posterior$mean,                          sd_vals = object$posterior$sd)    g1 <- ggplot2::ggplot(tmp_data) +        ggplot2::geom_ribbon(ggplot2::aes(.data$w_vals,                                 y = .data$mean_vals,                                 ymin = .data$mean_vals - 1.96 * .data$sd_vals,                                 ymax = .data$mean_vals + 1.96 * .data$sd_vals),                                 fill = \"blue\", alpha = 0.25) +         ggplot2::geom_line(ggplot2::aes(.data$w_vals, .data$mean_vals),                            color = \"blue\", size = 1) +         ggplot2::theme_bw() +         ggplot2::ggtitle(\"Estimated CERF (gp) with credible band (1.96sd)\") +         ggplot2::xlab(\"Exposure level\") +         ggplot2::ylab(\"Population average counterfactual outcome\")    return(g1) }  erf_tru_normal <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_normal)) erf_tru_t <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_t))"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"train-gps-model","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Train GPS Model","title":"Standard Gaussian Processes","text":"GPCERF first convert covariate values single composite score (GPS) use fit Gaussian processes. use GPS estimation function CasaulGPS (see ) get GPS model maps covariates GPS. use estimate_cerf_gp estimate ERF exposure w. estimate ERF w = seq(0,20,0.1). estimated ERF well pointwise 95% credible band visualized call plot. also plot actual ERF top estimated ERF.","code":"gps_m_normal <- estimate_gps(cov_mt = data_sim_normal[, -(1:2)],                              w_all = data_sim_normal$treat,                              sl_lib = c(\"SL.xgboost\"),                              dnorm_log = FALSE) #> Loading required package: nnls  gps_m_t <- estimate_gps(cov_mt = data_sim_t[, -(1:2)],                         w_all = data_sim_t$treat,                         sl_lib = c(\"SL.xgboost\"),                         dnorm_log = FALSE)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"normally-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Normally Distributed Exposure","title":"Standard Gaussian Processes","text":"can see estimated curve follows true ERF closely 95% credible band completely covers true ERF.","code":"w_all <- seq(0, 20, 0.1)  gp_res_normal <- estimate_cerf_gp(data_sim_normal,                                   w_all,                                   gps_m_normal,                                   params = list(alpha = c(0.1),                                                 beta = 0.2,                                                 g_sigma = 1,                                                 tune_app = \"all\"),                                   nthread = 1) plot_fun(gp_res_normal) +   geom_line(data = data.frame(w = w_all, y = erf_tru_normal),             aes(x = w, y = y, color = \"True\"), size = 1.5) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Full-Gaussian-Processes.html","id":"t-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"T-distributed Exposure","title":"Standard Gaussian Processes","text":"results look similar case exposure normally distributed. difference might exposure t-distributed, estimated curve tends less smooth.","code":"gp_res_t <- estimate_cerf_gp(data_sim_t,                              w_all,                              gps_m_t,                              params = list(alpha = c(0.1),                                            beta = 0.2,                                            g_sigma = 1,                                            tune_app = \"all\"),                              nthread = 1) plot_fun(gp_res_t) +   geom_line(data = data.frame(w = w_all, y = erf_tru_t),             aes(x = w, y = y, color = \"True\"), size = 1.5)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"GPCERF","text":"","code":"library(\"devtools\") install_github(\"NSAPH-Software/GPCERF\", ref = \"main\") library(\"GPCERF\")"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"estimating-gps-values","dir":"Articles","previous_headings":"Usage","what":"Estimating GPS values","title":"GPCERF","text":"Input parameters: cov_mt Covariate matrix containing covariates. row represents sample, column covariate.w_all vector observed exposure level.","code":"mydata <- generate_synthetic_data() gps_m <- estimate_gps(cov_mt = mydata[, c(\"cf1\", \"cf2\", \"cf3\", \"cf4\",                                          \"cf5\", \"cf6\")],                   w_all = mydata$treat,                   sl_lib = c(\"SL.xgboost\"),                   dnorm_log = FALSE) #> Loading required package: nnls"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"estimating-exposure-response-using-gaussian-processes","dir":"Articles","previous_headings":"Usage","what":"Estimating exposure response using Gaussian Processes","title":"GPCERF","text":"Input parameters: data data.frame observation data. - Column 1: Outcome (Y) - Column 2: Exposure treatment (w) - Column 3~m: Confounders (C)w vector exposure level compute CERF.gps_m data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred) - Column 3: Standard deviation e_gps (e_gps_std)params list parameters required run process. - alpha: scaling factor GPS value. - beta: scaling factor exposure value. - g_sigma: scaling factor kernel function (gamma/sigma). - tune_app: tuning approach. Available approaches: - : try combinations hyperparameters. alpha, beta, g_sigma can vector parameters. nthread integer value represents number threads used shared memory system.kernel_fn kernel function. default value Gaussian kernel.","code":"set.seed(129) sim_data <- generate_synthetic_data(sample_size = 400, gps_spec = 3)  # Estimate GPS function gps_m <- estimate_gps(cov_mt = sim_data[, -(1:2)],                       w_all = sim_data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = TRUE)  # exposure values q1 <- stats::quantile(sim_data$treat, 0.05) q2 <- stats::quantile(sim_data$treat, 0.95)  w_all <- seq(q1, q2, 1)  params_lst <- list(alpha = c(0.05),                    beta = c(12),                    g_sigma = c(0.1),                    tune_app = \"all\") cerf_gp_obj <- estimate_cerf_gp(sim_data,                                w_all,                                gps_m,                                params = params_lst,                                nthread = 1) summary(cerf_gp_obj) #> GPCERF standard Gaussian grocess exposure response function object #>  #> Optimal hyper parameters(#trial: 1):  #>   alpha = 0.05   beta = 12   g_sigma = 0.1 #>  #> Optimal covariate balance:  #>   cf1 = 0.080  #>   cf2 = 0.081  #>   cf3 = 0.111  #>   cf4 = 0.119  #>   cf5 = 0.084  #>   cf6 = 0.104 #>  #> Original covariate balance:  #>   cf1 = 0.185  #>   cf2 = 0.161  #>   cf3 = 0.106  #>   cf4 = 0.208  #>   cf5 = 0.246  #>   cf6 = 0.283 #>             ----***---- plot(cerf_gp_obj)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"estimating-exposure-response-using-nearest-neighbor-gaussian-processes","dir":"Articles","previous_headings":"Usage","what":"Estimating exposure response using nearest-neighbor Gaussian Processes","title":"GPCERF","text":"Input parameters: data data.frame observation data. - Column 1: Outcome (Y) - Column 2: Exposure treatment (w) - Column 3~m: Confounders (C)w vector exposure level compute CERF.gps_m data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred) - Column 3: Standard deviation e_gps (e_gps_std)params list parameters required run process. - alpha: scaling factor GPS value. - beta: scaling factor exposure value. - g_sigma: scaling factor kernel function (gamma/sigma). - tune_app: tuning approach. Available approaches: - : try combinations hyperparameters. - n_neighbors: Number nearest neighbors one side. - block_size: Number samples included computation block. Mainly used balance speed memory requirement. Larger faster, requires memory. alpha, beta, g_sigma can vector parameters. nthread integer value represents number threads used shared memory system.kernel_fn kernel function. default value Gaussian kernel.","code":"set.seed(19) sim_data <- generate_synthetic_data(sample_size = 1000, gps_spec = 3)  # Estimate GPS function gps_m <- estimate_gps(cov_mt = sim_data[, -(1:2)],                       w_all = sim_data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = TRUE) # exposure values q1 <- stats::quantile(sim_data$treat, 0.05) q2 <- stats::quantile(sim_data$treat, 0.95)  w_all <- seq(q1, q2, 1)  params_lst <- list(alpha = c(0.05),                    beta = c(12),                    g_sigma = c(0.1),                    tune_app = \"all\",                    n_neighbor = 20,                    block_size = 1e4) cerf_nngp_obj <- estimate_cerf_nngp(sim_data,                                     w_all,                                     gps_m,                                     params = params_lst,                                     nthread = 1)  summary(cerf_nngp_obj) #> GPCERF nearest neighbore Gaussian process exposure response function object summary #>  #> Optimal hyper parameters(#trial: 1):  #>   alpha = 0.05   beta = 12   g_sigma = 0.1 #>  #> Optimal covariate balance:  #>   cf1 = 0.118  #>   cf2 = 0.103  #>   cf3 = 0.105  #>   cf4 = 0.089  #>   cf5 = 0.159  #>   cf6 = 0.149 #>  #> Original covariate balance:  #>   cf1 = 0.136  #>   cf2 = 0.148  #>   cf3 = 0.105  #>   cf4 = 0.228  #>   cf5 = 0.184  #>   cf6 = 0.280 #>             ----***---- plot(cerf_nngp_obj)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/GPCERF.html","id":"logging","dir":"Articles","previous_headings":"Usage","what":"Logging","title":"GPCERF","text":"GPCERF package logs internal activities screen GPCERF.log file. Users can change logging file name (path) logging threshold. logging mechanism different thresholds (see logger package). two important thresholds INFO DEBUG levels. former, default level, logs general information process. latter, activated, logs detailed information can used debugging purposes. log file located source file location appended.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"generate-synthetic-data","dir":"Articles","previous_headings":"","what":"Generate Synthetic Data","title":"Nearest-neighbor Gaussian Processes","text":"first generate synthetic dataset six covariates, one continuous exposure one outcome. consider two types conditional distributions (normal student’s t) exposure given covariates. details synthetic data generation, see document. use function tru_R derive actual ERF population w=seq(0,20,0.1).","code":"set.seed(1) # Generate dataset with a normally distributed exposure given covariates data_sim_normal <- generate_synthetic_data(sample_size = 500,                                            outcome_sd = 10,                                            gps_spec = 1) # Generate dataset with a t-distributed with 2df exposure given covariates data_sim_t <- generate_synthetic_data(sample_size = 500,                                       outcome_sd = 10,                                       gps_spec = 2) tru_R <- function(w, sim_data) {   design_mt <- model.matrix(~cf1 + cf2 + cf3 + cf4 + cf5 + cf6 - 1,                             data = sim_data)   mean(apply(design_mt, 1, function(x) {     -10 - sum(c(2, 2, 3, -1, 2, 2) * x) -      w * (0.1 - 0.1 * x[1] + 0.1 * x[4] + 0.1 * x[5] + 0.1 * x[3] ^ 2) +      0.13 ^ 2 * w ^ 3   })) }  plot_fun <- function(object, ...) {   tmp_data <- data.frame(w_vals = object$posterior$w,                          mean_vals = object$posterior$mean,                          sd_vals = object$posterior$sd)    g1 <- ggplot2::ggplot(tmp_data) +         ggplot2::geom_ribbon(ggplot2::aes(.data$w_vals,                                 y = .data$mean_vals,                                 ymin = .data$mean_vals - 1.96 * .data$sd_vals,                                 ymax = .data$mean_vals + 1.96 * .data$sd_vals),                                 fill = \"#FC4E07\", alpha = 0.25) +         ggplot2::geom_line(ggplot2::aes(.data$w_vals, .data$mean_vals),                            color = \"#FC4E07\",                            size = 1) +         ggplot2::theme_bw() +         ggplot2::ggtitle(\"Estimated CERF (nngp) with credible band (1.96sd)\") +         ggplot2::xlab(\"Exposure level\") +         ggplot2::ylab(\"Population average counterfactual outcome\")    return(g1) }   erf_tru_normal <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_normal)) erf_tru_t <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_t))"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"train-gps-model","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Train GPS Model","title":"Nearest-neighbor Gaussian Processes","text":"GPCERF first convert covariate values single composite score (GPS) use fit Gaussian processes. use GPS estimation function CausalGPS (see ) get GPS model maps covariates GPS. use estimate_cerf_nngp estimate ERF exposure w. estimate ERF w = seq(0,20,0.1). estimated ERF well pointwise 95% credible band visualized call plot. also plot actual ERF top estimated ERF.","code":"gps_m_normal <- estimate_gps(cov_mt = data_sim_normal[, -(1:2)],                              w_all = data_sim_normal$treat,                              sl_lib = c(\"SL.xgboost\"),                              dnorm_log = FALSE) #> Loading required package: nnls gps_m_t <- estimate_gps(cov_mt = data_sim_t[, -(1:2)],                         w_all = data_sim_t$treat,                         sl_lib = c(\"SL.xgboost\"),                         dnorm_log = FALSE)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"normally-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Normally Distributed Exposure","title":"Nearest-neighbor Gaussian Processes","text":"can see estimated curve follows true ERF closely 95% credible band completely covers true ERF. running time much smaller standard GP approach.","code":"w_all <-  seq(0, 20, 0.1) nngp_res_normal <- estimate_cerf_nngp(data_sim_normal,                                       w_all,                                       gps_m_normal,                                       params = list(alpha = c(0.1),                                                     beta = 0.2,                                                     g_sigma = 1,                                                     n_neighbor = 20,                                                     block_size = 50,                                                     tune_app = \"all\"),                                       nthread = 1) plot_fun(nngp_res_normal) +   geom_line(data = data.frame(w = w_all, y = erf_tru_normal),             aes(x = w, y = y, color = \"True\"), size = 1.5) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Nearest-neighbor-Gaussian-Processes.html","id":"t-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"T-distributed Exposure","title":"Nearest-neighbor Gaussian Processes","text":"results look similar case exposure normally distributed. difference might exposure t-distributed, estimated curve tends less smooth.","code":"nngp_res_t <- estimate_cerf_nngp(data_sim_t,                                  w_all,                                  gps_m_t,                                  params = list(alpha = c(0.1),                                                beta = 0.2,                                                g_sigma = 1,                                                n_neighbor = 20,                                                block_size = 50,                                                tune_app = \"all\"),                                 nthread = 1) plot_fun(nngp_res_t) +   geom_line(data = data.frame(w = w_all, y = erf_tru_t),             aes(x = w, y = y, color = \"True\"), size = 1.5)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"generate-synthetic-data","dir":"Articles","previous_headings":"","what":"Generate Synthetic Data","title":"Standard Gaussian Processes","text":"first generate synthetic dataset six covariates, one continuous exposure one outcome. consider two types conditional distributions (normal student’s t) exposure given covariates. details synthetic data generation, see document. use function tru_R derive actual ERF population w=seq(0,20,0.1).","code":"set.seed(134) # Generate dataset with a normally distributed exposure given covariates data_sim_normal <- generate_synthetic_data(sample_size = 400,                                            outcome_sd = 10,                                            gps_spec = 1)  # Generate dataset with a t-distributed with 2df exposure given covariates data_sim_t <- generate_synthetic_data(sample_size = 400,                                       outcome_sd = 10,                                       gps_spec = 2)  tru_R <- function(w, sim_data) {   design_mt <- model.matrix(~cf1 + cf2 + cf3 + cf4 + cf5 + cf6 - 1,                             data = sim_data)   mean(apply(design_mt, 1, function(x) {     -10 - sum(c(2, 2, 3, -1, 2, 2) * x) -       w * (0.1 - 0.1 * x[1] + 0.1 * x[4] + 0.1 * x[5] + 0.1 * x[3] ^ 2) +       0.13 ^ 2 * w ^ 3   })) }  plot_fun <- function(object, ...) {     # extract data   tmp_data <- data.frame(w_vals = object$posterior$w,                          mean_vals = object$posterior$mean,                          sd_vals = object$posterior$sd)    g1 <- ggplot2::ggplot(tmp_data) +        ggplot2::geom_ribbon(ggplot2::aes(.data$w_vals,                                 y = .data$mean_vals,                                 ymin = .data$mean_vals - 1.96 * .data$sd_vals,                                 ymax = .data$mean_vals + 1.96 * .data$sd_vals),                                 fill = \"blue\", alpha = 0.25) +         ggplot2::geom_line(ggplot2::aes(.data$w_vals, .data$mean_vals),                            color = \"blue\", size = 1) +         ggplot2::theme_bw() +         ggplot2::ggtitle(\"Estimated CERF (gp) with credible band (1.96sd)\") +         ggplot2::xlab(\"Exposure level\") +         ggplot2::ylab(\"Population average counterfactual outcome\")    return(g1) }  erf_tru_normal <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_normal)) erf_tru_t <- sapply(seq(0, 20, 0.1), function(w) tru_R(w, data_sim_t))"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"train-gps-model","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Train GPS Model","title":"Standard Gaussian Processes","text":"GPCERF first convert covariate values single composite score (GPS) use fit Gaussian processes. use GPS estimation function CausalGPS (see ) get GPS model maps covariates GPS. use estimate_cerf_gp estimate ERF exposure w. estimate ERF w = seq(0,20,0.1). estimated ERF well pointwise 95% credible band visualized call plot. also plot actual ERF top estimated ERF.","code":"gps_m_normal <- estimate_gps(cov_mt = data_sim_normal[, -(1:2)],                              w_all = data_sim_normal$treat,                              sl_lib = c(\"SL.xgboost\"),                              dnorm_log = FALSE) #> Loading required package: nnls  gps_m_t <- estimate_gps(cov_mt = data_sim_t[, -(1:2)],                         w_all = data_sim_t$treat,                         sl_lib = c(\"SL.xgboost\"),                         dnorm_log = FALSE)"},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"normally-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"Normally Distributed Exposure","title":"Standard Gaussian Processes","text":"can see estimated curve follows true ERF closely 95% credible band completely covers true ERF.","code":"w_all <- seq(0, 20, 0.1)  gp_res_normal <- estimate_cerf_gp(data_sim_normal,                                   w_all,                                   gps_m_normal,                                   params = list(alpha = c(0.1),                                                 beta = 0.2,                                                 g_sigma = 1,                                                 tune_app = \"all\"),                                   nthread = 1) plot_fun(gp_res_normal) +   geom_line(data = data.frame(w = w_all, y = erf_tru_normal),             aes(x = w, y = y, color = \"True\"), size = 1.5) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://NSAPH-Software.github.io/GPCERF/articles/Standard-Gaussian-Processes.html","id":"t-distributed-exposure","dir":"Articles","previous_headings":"Estimate ERF with standard Gaussian Processes","what":"T-distributed Exposure","title":"Standard Gaussian Processes","text":"results look similar case exposure normally distributed. difference might exposure t-distributed, estimated curve tends less smooth.","code":"gp_res_t <- estimate_cerf_gp(data_sim_t,                              w_all,                              gps_m_t,                              params = list(alpha = c(0.1),                                            beta = 0.2,                                            g_sigma = 1,                                            tune_app = \"all\"),                              nthread = 1) plot_fun(gp_res_t) +   geom_line(data = data.frame(w = w_all, y = erf_tru_t),             aes(x = w, y = y, color = \"True\"), size = 1.5)"},{"path":"https://NSAPH-Software.github.io/GPCERF/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            HUIT Boyu Ren. Author.            McLean Hospital Tanujit Dey. Contributor.            HMS Danielle Braun. Author.            HSPH","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Khoshnevis N, Ren B, Braun D (2023). GPCERF: Gaussian Processes Estimating Causal Exposure Response Curves. R package version 0.2.0, https://CRAN.R-project.org/package=GPCERF.","code":"@Manual{,   title = {GPCERF: Gaussian Processes for Estimating Causal Exposure Response Curves},   author = {Naeem Khoshnevis and Boyu Ren and Danielle Braun},   year = {2023},   note = {R package version 0.2.0},   url = {https://CRAN.R-project.org/package=GPCERF}, }"},{"path":[]},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/index.html","id":"summary","dir":"","previous_headings":"","what":"Summary","title":"Gaussian Process (GP) approach for nonparametric modeling.","text":"Gaussian Process (GP) approach nonparametric modeling. Provides non-parametric Bayesian framework based Gaussian process priors estimating causal effects continuous exposure detecting change points causal exposure response curves using observational data.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Gaussian Process (GP) approach for nonparametric modeling.","text":"Ren, B., Wu, X., Braun, D., Pillai, N. Dominici, F., 2021. Bayesian modeling exposure response curve via gaussian processes: Causal effects exposure air pollution health outcomes. arXiv preprint arXiv:2105.03454.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/GPCERF-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The 'GPCERF' package. — GPCERF-package","title":"The 'GPCERF' package. — GPCERF-package","text":"Provides non-parametric Bayesian framework based Gaussian process priors estimating causal effects continuous exposure detecting change points causal exposure response curves using observational data.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/GPCERF-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The 'GPCERF' package. — GPCERF-package","text":"Ren, B., Wu, X., Braun, D., Pillai, N. Dominici, F., 2021. Bayesian modeling exposure response curve via gaussian processes: Causal effects exposure air pollution health outcomes. arXiv preprint arXiv:2105.03454.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/GPCERF-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The 'GPCERF' package. — GPCERF-package","text":"Naeem Khoshnevis Boyu Ren Danielle Braun","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cerf_gp object — autoplot.cerf_gp","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"helper function plot cerf_gp object using ggplot2 package.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"","code":"# S3 method for cerf_gp autoplot(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"object cerf_gp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cerf_gp object — autoplot.cerf_gp","text":"Returns ggplot object.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cerf_nngp object — autoplot.cerf_nngp","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"helper function plot cerf_nngp object using ggplot2 package.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"","code":"# S3 method for cerf_nngp autoplot(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"object cerf_nngp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/autoplot.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cerf_nngp object — autoplot.cerf_nngp","text":"Returns ggplot object.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate covariate balance — calc_ac","title":"Calculate covariate balance — calc_ac","text":"Calculates weighted correlation list covariates exposure. covariance function GP defines weights.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate covariate balance — calc_ac","text":"","code":"calc_ac(w, X, weights)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate covariate balance — calc_ac","text":"w vector exposure values across subjects. X matrix covariate values. Subjects rows covariates columns. weights vector weights assigned subjects based trained GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/calc_ac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate covariate balance — calc_ac","text":"vector correlations w column X.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"Calculates posterior mean derivative CERF given exposure level nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"","code":"compute_deriv_nn(   w,   w_obs,   gps_m,   y_obs,   hyperparam,   n_neighbor,   block_size,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FALSE y_obs vector observed outcome values. hyperparam vector hyper-parameters GP model. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. kernel_fn covariance function. input square Euclidean distance. kernel_deriv_fn partial derivative covariance function. input square Euclidean distance.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate derivatives of CERF for nnGP — compute_deriv_nn","text":"scalar estimated derivative CERF w nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate derivatives of CERF — compute_deriv_weights_gp","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"Calculates weights assigned observed outcome deriving posterior mean first derivative CERF given exposure level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"","code":"compute_deriv_weights_gp(   w,   w_obs,   gps_m,   hyperparam,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE hyperparam vector hyper-parameters GP model. kernel_fn covariance function. kernel_deriv_fn partial derivative covariance function.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_deriv_weights_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate derivatives of CERF — compute_deriv_weights_gp","text":"vector weights samples, based posterior mean derivative CERF exposure level interest calculated.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute matrix inverse for a covariate matrix — compute_inverse","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"Computes inverse covariate matrix using Choleski decomposition.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"","code":"compute_inverse(mtrx)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"mtrx n * n covariate matrix","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute matrix inverse for a covariate matrix — compute_inverse","text":"matrix represent inverse input matrix.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"Calculates induced covariate balance associated one hyper-parameter configuration standard GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"","code":"compute_m_sigma(   hyperparam,   data,   w,   gps_m,   tuning,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"hyperparam vector values hyper-parameters. First element: alpha Second element: beta Third element: g_sigma (gamma / sigma) data  data.frame containing data including outcome, exposure covariates. following order: Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) w vector exposure levels CERF estimated. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE tuning function used parameter tuning (default = TRUE) estimation (FALSE) kernel_fn covariance function GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_m_sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute mean, credible interval, and covariate balance in standard Gaussian\nprocess (GP) — compute_m_sigma","text":"list containing two elements: vector absolute weighted correlation covariate exposure, metric covariate balance estimated CERF w_all based hyper-parameter values param.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate posterior means for nnGP model — compute_posterior_m_nn","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"Calculates posterior mean point CERF based nnGP model. function also returns weights assigned nearest neighbors calculating posterior mean.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"","code":"compute_posterior_m_nn(   hyperparam,   w,   gps_w,   obs_ord,   y_obs_ord,   kernel_fn = function(x) exp(-x^2),   n_neighbor = 10,   block_size = 10000 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"hyperparam set hyperparameters GP model. w scaler representing exposure level point interest CERF. gps_w GPS samples exposure levels set w. obs_ord matrix two columns. First column observed exposure levels samples; second GPS observed exposure levels. rows ascending order first column. y_obs_ord vector observed outcome values. vector ordered obs_ord. kernel_fn covariance function GP. n_neighbor number nearest neighbors one side. block_size Number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_m_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate posterior means for nnGP model — compute_posterior_m_nn","text":"TODO: first column selected index second column weight. two-column matrix. first column weights assigned nearest neighbor. second column corresponding observed outcome value. weight last row matrix NA observed outcome value estimated posterior mean CERF point w, weighted sum observed outcome values neighbors.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"Calculates posterior standard deviation point CERF based nnGP model.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"","code":"compute_posterior_sd_nn(   hyperparam,   w,   gps_w,   obs_ord,   sigma2,   kernel_fn = function(x) exp(-x^2),   n_neighbor = 10,   block_size = 10000 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"hyperparam values hyperparameters GP model. w exposure level point interest CERF. gps_w GPS samples exposure levels set w. obs_ord matrix two columns. first column observed exposure levels samples; second GPS observed exposure levels. rows ascending order first column. sigma2 scaler representing sigma^2. kernel_fn covariance function GP. n_neighbor Number nearest neighbors one side. block_size Number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_posterior_sd_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate posterior standard deviations for nnGP model — compute_posterior_sd_nn","text":"posterior standard deviation estimated CERF w.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect change-point in standard GP — compute_rl_deriv_gp","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"Calculates posterior mean difference left- right-derivatives exposure level detection change points.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"","code":"compute_rl_deriv_gp(   w,   w_obs,   y_obs,   gps_m,   hyperparam,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. y_obs vector observed outcome values samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE hyperparam vector hyper-parameters GP model. kernel_fn covariance function. kernel_deriv_fn partial derivative covariance function.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"numeric value posterior mean difference two one-sided derivatives.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_gp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect change-point in standard GP — compute_rl_deriv_gp","text":"","code":"# \\donttest{ set.seed(847) data <- generate_synthetic_data(sample_size = 100) gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE) #> Loading required package: nnls  wi <- 8.6  val <- compute_rl_deriv_gp(w = wi,                            w_obs = data$treat,                            y_obs = data$Y,                            gps_m = gps_m,                            hyperparam = c(1,1,2)) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"Calculates posterior mean difference left- right-derivatives exposure level detection change points. nnGP approximation used.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"","code":"compute_rl_deriv_nn(   w,   w_obs,   gps_m,   y_obs,   hyperparam,   n_neighbor,   block_size,   kernel_fn = function(x) exp(-x),   kernel_deriv_fn = function(x) -exp(-x) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE y_obs vector observed outcome values. hyperparam vector hyper-parameters GP model. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. kernel_fn covariance function. input square Euclidean distance. kernel_deriv_fn partial derivative covariance function. input square Euclidean distance.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"numeric value posterior mean difference two one-sided derivatives.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_rl_deriv_nn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate right minus left derivatives for change-point detection in nnGP — compute_rl_deriv_nn","text":"","code":"# \\donttest{ set.seed(325) data <- generate_synthetic_data(sample_size = 200) gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE)  wi <- 12.2  deriv_val <- compute_rl_deriv_nn(w = wi,                                  w_obs = data$treat,                                  gps_m = gps_m,                                  y_obs = data$Y,                                  hyperparam = c(0.2,0.4,1.2),                                  n_neighbor = 20,                                  block_size = 10) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute posterior credible interval — compute_sd_gp","title":"Compute posterior credible interval — compute_sd_gp","text":"Computes posterior credible interval requested exposure level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute posterior credible interval — compute_sd_gp","text":"","code":"compute_sd_gp(   w,   scaled_obs,   hyperparam,   sigma,   gps_m,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute posterior credible interval — compute_sd_gp","text":"w scalar exposure level interest. scaled_obs matrix two columns. First column scaled GPS value samples (GPS * 1/sqrt(alpha)) Second column scaled exposure value samples (w * 1/sqrt(beta)) hyperparam vector hyper-parameters GP. First element: alpha Second element: beta Third element: gamma/sigma sigma scaler represents noise. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE kernel_fn covariance function GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_sd_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute posterior credible interval — compute_sd_gp","text":"Posterior credible interval (scaler) requested exposure level (w).","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute  weighted covariate balance — compute_w_corr","title":"Compute  weighted covariate balance — compute_w_corr","text":"Computes weighted covariate balance given data sets.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute  weighted covariate balance — compute_w_corr","text":"","code":"compute_w_corr(w, covariate, weight)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute  weighted covariate balance — compute_w_corr","text":"w vector observed continuous exposure variable. covariate data frame observed covariates variable. weight vector weights.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute  weighted covariate balance — compute_w_corr","text":"function returns list saved measure related covariate balance absolute_corr: absolute correlations pre-exposure covairates; mean_absolute_corr: average absolute correlations pre-exposure covairates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_w_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute  weighted covariate balance — compute_w_corr","text":"","code":"set.seed(639) n <- 100 mydata <- generate_synthetic_data(sample_size=100) year <- sample(x=c(\"2001\",\"2002\",\"2003\",\"2004\",\"2005\"),size = n,  replace = TRUE) region <- sample(x=c(\"North\", \"South\", \"East\", \"West\"),size = n,  replace = TRUE) mydata$year <- as.factor(year) mydata$region <- as.factor(region) mydata$cf5 <- as.factor(mydata$cf5) cor_val <- compute_w_corr(mydata[,2],                           mydata[, 3:length(mydata)],                           runif(n))  print(cor_val$mean_absolute_corr) #> [1] 0.1461331"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate weights for estimation of a point on CERF — compute_weight_gp","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"Calculates weights observed outcomes used estimate posterior mean CERF given exposure level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"","code":"compute_weight_gp(   w,   w_obs,   scaled_obs,   hyperparam,   inv_sigma_obs,   gps_m,   est_sd = FALSE,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"w scalar exposure level interest. w_obs vector observed exposure levels samples. scaled_obs matrix two columns. First column scaled GPS value samples (GPS * 1 / sqrt(alpha)) Second column scaled exposure value samples (w * 1/sqrt(beta)) hyperparam vector hyper-parameters GP. First element: alpha Second element: beta Third element: gamma/sigma inv_sigma_obs Inverse covariance matrix observed samples. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE est_sd posterior se computed (default=FALSE) kernel_fn covariance function GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/compute_weight_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate weights for estimation of a point on CERF — compute_weight_gp","text":"list two elements, weight standard deviation.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"Estimates conditional exposure response function (cerf) using Gaussian Process (gp). function tune best match (lowest covariate balance) provided set hyperparameters.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"","code":"estimate_cerf_gp(   data,   w,   gps_m,   params,   nthread = 1,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"data data.frame observation data. Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) w vector exposure level compute CERF. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE params list parameters required run process. parameters include: alpha: scaling factor GPS value. beta: scaling factor exposure value. g_sigma: scaling factor kernel function (gamma/sigma). tune_app: tuning approach. Available approaches: : try combinations hyperparameters. alpha, beta, g_sigma can vector parameters. nthread integer value represents number threads used internal packages. kernel_fn kernel function. default value Gaussian Kernel.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"cerf_gp object includes following values: w, vector exposure levels. pst_mean, Computed mean w vector. pst_sd, Computed credible interval w vector.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_gp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the conditional exposure response function using Gaussian process — estimate_cerf_gp","text":"","code":"# \\donttest{ set.seed(129) data <- generate_synthetic_data(sample_size = 100, gps_spec = 3)   # Estimate GPS function gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE)  # exposure values w_all <- seq(0,10,1)   cerf_gp_obj <- estimate_cerf_gp(data,                                 w_all,                                 gps_m,                                 params = list(alpha = c(0.1),                                               beta=0.2,                                               g_sigma = 1,                                               tune_app = \"all\"),                                 nthread = 1) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"Estimates conditional exposure response function (cerf) using nearest neighbor (nn) Gaussian Process (gp). function tune best match (lowest covariate balance) provided set hyperparameters.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"","code":"estimate_cerf_nngp(   data,   w,   gps_m,   params,   kernel_fn = function(x) exp(-x^2),   nthread = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"data data.frame observation data. Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) w vector exposure level compute CERF. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE params list parameters required run process. parameters include: alpha: scaling factor GPS value. beta: scaling factor exposure value. g_sigma: scaling factor kernel function (gamma/sigma). tune_app: tuning approach. Available approaches: : try combinations hyperparameters. n_neighbor: Number nearest neighbors one side. block_size: Number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. alpha, beta, g_sigma can vector parameters. kernel_fn kernel function. default value Gaussian Kernel. nthread integer value represents number threads used internal packages.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"cerf_nngp object includes following values: w, vector exposure levels. pst_mean, computed mean w vector. pst_sd, computed credible interval w vector.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_cerf_nngp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the conditional exposure response function using nearest neighbor\nGaussian process — estimate_cerf_nngp","text":"","code":"# \\donttest{ set.seed(19) data <- generate_synthetic_data(sample_size = 120, gps_spec = 3) # Estimate GPS function gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE) # exposure values w.all <- seq(0,20,2) cerf_nngp_obj <- estimate_cerf_nngp(data,                                     w.all,                                     gps_m,                                     params = list(alpha = c(0.1),                                                   beta = 0.2,                                                   g_sigma = 1,                                                   tune_app = \"all\",                                                   n_neighbor = 20,                                                   block_size = 1e4),                                     nthread = 1) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a model for generalized propensity score — estimate_gps","title":"Estimate a model for generalized propensity score — estimate_gps","text":"Estimates model generalized propensity score (GPS) using parametric approach.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a model for generalized propensity score — estimate_gps","text":"","code":"estimate_gps(cov_mt, w_all, sl_lib, dnorm_log)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a model for generalized propensity score — estimate_gps","text":"cov_mt covariate matrix containing covariates. row data sample column covariate. w_all vector observed exposure levels. sl_lib vector SuperLearner's package libraries. dnorm_log Logical, TRUE, probabilities p given log(p).","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a model for generalized propensity score — estimate_gps","text":"data.frame includes: vector estimated GPS observed exposure levels; vector estimated conditional means exposure levels covariates fixed observed values; estimated standard deviation exposure levels vector observed exposure levels.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_gps.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a model for generalized propensity score — estimate_gps","text":"","code":"# \\donttest{ data <- generate_synthetic_data(sample_size = 200) gps_m <- estimate_gps(cov_mt = data[,-(1:2)],                       w_all = data$treat,                       sl_lib = c(\"SL.xgboost\"),                       dnorm_log = FALSE) # }"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"Estimates posterior mean conditional exposure response function specified exposure levels nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"","code":"estimate_mean_sd_nn(   hyperparam,   sigma2,   w_obs,   w,   y_obs,   gps_m,   kernel_fn = function(x) exp(-x^2),   n_neighbor = 50,   block_size = 2000,   nthread = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"hyperparam set hyperparameters nnGP. sigma2 scaler representing sigma^2. w_obs vector observed exposure levels. w vector exposure levels CERF estimated. y_obs vector observed outcome values. gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE kernel_fn covariance function GP. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. nthread integer value represents number threads used internal packages.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_mean_sd_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the CERF with the nnGP model — estimate_mean_sd_nn","text":"vector returned value compute_posterior_sd_nn.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"Estimates standard deviations nugget term standard GP calculating standard deviations residuals.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"","code":"estimate_noise_gp(data, sigma_obs, inv_sigma_obs)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"data data.frame observation data. Column 1: Outcome (Y) Column 2: Exposure treatment (w) Column 3~m: Confounders (C) sigma_obs Covariance matrix observed covariates. inv_sigma_obs Inverse covariance matrix observed covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the standard deviation of the nugget term in standard Gaussian\nprocess — estimate_noise_gp","text":"scalar estimated standard deviation nugget term standard GP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"Estimates standard deviations nugget term (noise) nnGP calculating standard deviations residuals.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"","code":"estimate_noise_nn(   hyperparam,   w_obs,   GPS_obs,   y_obs,   n_neighbor,   nthread,   kernel_fn = function(x) exp(-x^2) )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"hyperparam vector hyper-parameter values. w_obs vector observed exposure levels. GPS_obs vector estimated GPS evaluated observed exposure levels. y_obs vector observed outcomes. n_neighbor number nearest neighbors one side. kernel_fn covariance function GP. n_thread number cores used estimation.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/estimate_noise_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the standard deviation (noise) of the nugget term in nnGP — estimate_noise_nn","text":"scalar estimated standard deviation nugget term nnGP.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"Computes covariate balance combination provided hyper-parameters selects hyper-parameter values minimizes covariate balance.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"","code":"find_optimal_nn(   w_obs,   w,   y_obs,   gps_m,   design_mt,   hyperparams = expand.grid(seq(0.5, 4.5, 1), seq(0.5, 4.5, 1), seq(0.5, 4.5, 1)),   kernel_fn = function(x) exp(-x^2),   n_neighbor = 50,   block_size = 2000,   nthread = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"w_obs vector observed exposure levels. w vector exposure levels CERF estimated. y_obs vector observed outcomes gps_m S3 gps object including: gps: data.frame GPS vectors. - Column 1: GPS - Column 2: Prediction exposure covariate data sample (e_gps_pred). - Column 3: Standard deviation  e_gps (e_gps_std) used_params: - dnorm_log: TRUE FLASE design_mt covariate matrix samples (intercept excluded). hyperparams matrix candidate values hyper-parameters, row contains set values hyper-parameters. kernel_fn covariance function GP. n_neighbor number nearest neighbors one side. block_size number samples included computation block. Mainly used balance speed memory requirement. Larger block_size faster, requires memory. nthread integer value represents number threads used internal packages.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/find_optimal_nn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the optimal hyper-parameter for the nearest neighbor Gaussian process — find_optimal_nn","text":"Estimated covariate balance scores grid hyper-parameter values considered hyperparams.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate synthetic data for the GPCERF package — generate_synthetic_data","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"Generates synthetic data set based different GPS models covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"","code":"generate_synthetic_data(   sample_size = 1000,   outcome_sd = 10,   gps_spec = 1,   cova_spec = 1 )"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"sample_size number data samples. outcome_sd Standard deviation used generate outcome synthetic data set. gps_spec numeric value (1-6) indicates GPS model used generate continuous exposure. cova_spec numeric value (1-2) modify covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"data frame synthetic data. Outcome labeled Y, exposure w, covariates cf1-6.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/generate_synthetic_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate synthetic data for the GPCERF package — generate_synthetic_data","text":"","code":"set.seed(351) data <- generate_synthetic_data(sample_size = 200)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get logger settings — get_logger","title":"Get logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/log_system_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Log system information — log_system_info","title":"Log system information — log_system_info","text":"Logs system related information log file.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/log_system_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log system information — log_system_info","text":"","code":"log_system_info()"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/log_system_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log system information — log_system_info","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"wrapper function extend generic plot functions cerf_gp class.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"","code":"# S3 method for cerf_gp plot(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"x cerf_gp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cerf_gp class — plot.cerf_gp","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"wrapper function extend generic plot functions cerf_nngp class.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"","code":"# S3 method for cerf_nngp plot(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"x cerf_nngp object. ... Additional arguments passed customize plot.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/plot.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cerf_nngp class — plot.cerf_nngp","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for cerf_gp object — print.cerf_gp","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"Extend print function cerf_gp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"","code":"# S3 method for cerf_gp print(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"x cerf_gp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for cerf_gp object — print.cerf_gp","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for cerf_nngp object — print.cerf_nngp","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"Extend print function cerf_nngp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"","code":"# S3 method for cerf_nngp print(x, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"x cerf_nngp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/print.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for cerf_nngp object — print.cerf_nngp","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set logger settings — set_logger","title":"Set logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"GPCERF.log\", logger_level = \"INFO\")"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: GPCERF.log) logger_level log level. Available levels include: TRACE DEBUG INFO (Default) SUCCESS WARN ERROR FATAL","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set logger settings — set_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\")"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"print summary of cerf_gp object — summary.cerf_gp","title":"print summary of cerf_gp object — summary.cerf_gp","text":"print summary cerf_gp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print summary of cerf_gp object — summary.cerf_gp","text":"","code":"# S3 method for cerf_gp summary(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print summary of cerf_gp object — summary.cerf_gp","text":"object cerf_gp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print summary of cerf_gp object — summary.cerf_gp","text":"Returns summary data","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":null,"dir":"Reference","previous_headings":"","what":"print summary of cerf_nngp object — summary.cerf_nngp","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"print summary cerf_nngp object","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"","code":"# S3 method for cerf_nngp summary(object, ...)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"object cerf_nngp object. ... Additional arguments passed customize results.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/summary.cerf_nngp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print summary of cerf_nngp object — summary.cerf_nngp","text":"Returns summary data.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model for generalized propensity score — train_gps","title":"Train a model for generalized propensity score — train_gps","text":"Estimates conditional mean sd exposure level function covariates.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model for generalized propensity score — train_gps","text":"","code":"train_gps(cov_mt, w_all, sl_lib, dnorm_log)"},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model for generalized propensity score — train_gps","text":"cov_mt covariate matrix containing covariates. row data sample column covariate. w_all vector observed exposure levels. sl_lib vector SuperLearner's package libraries. dnorm_log Logical, TRUE, probabilities p given log(p).","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model for generalized propensity score — train_gps","text":"data.frame includes: vector estimated GPS observed exposure levels; vector estimated conditional means exposure levels covariates fixed observed values; estimated standard deviation exposure levels","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/reference/train_GPS.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train a model for generalized propensity score — train_gps","text":"","code":"# \\donttest{ data <- generate_synthetic_data(sample_size = 200) GPS_m <- train_gps(cov_mt = data[,-(1:2)],                   w_all = data$treat,                   sl_lib = c(\"SL.xgboost\"),                   dnorm_log = FALSE) # }"},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.2.1 (2023-01-15)","text":"full GP –> standard GP plots exposure response function objects include covariate balance. formula longer need nn functions. estimate_gps now returns used exposure level, . train_gps –> estimate_gps nearest neighbor approach get expand input parameter (n_neighbor * expand –> n_neighbor). weighted covariate balance now computed using wCorr package.","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"gpcerf-020-2023-01-22","dir":"Changelog","previous_headings":"","what":"GPCERF 0.2.0 (2023-01-22)","title":"GPCERF 0.2.0 (2023-01-22)","text":"CRAN release: 2023-01-22","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.2.0 (2023-01-22)","text":"estimate_noise_nn now allows parallelization added argument nthread number CPUs used parallel. estimate_mean_sd_nn now computes posterior variance. find_optimal_nn now returns posterior mean covariate balance optimal hyper-parameter values. Add argument kernel_fn nn related functions allow user-defined kernel functions. Add argument formula nn related functions allow user-defined design matrix. find_optimal_nn becomes internal function. estimate_noise_gp estimate_noise_nn become internal functions. estimate_mean_sd_nn becomes internal function. compute_weight_gp becomes internal function. compute_w_corr accepts w confounders separately. also normalizes w internally. compute_posterior_sd_nn becomes internal function. compute_posterior_m_nn becomes internal function. compute_derive_weights_gp becomes internal function. compute_m_sigma becomes internal function. compute_inverse becomes internal function. compute_m_sigma, tuning option default value. train_gps default values. train_gps accepts vector SuperLearner package’s libraries. train_GPS -> train_gps","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"gpcerf-010-2022-07-02","dir":"Changelog","previous_headings":"","what":"GPCERF 0.1.0 (2022-07-02)","title":"GPCERF 0.1.0 (2022-07-02)","text":"CRAN release: 2022-07-02","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.1.0 (2022-07-02)","text":"nn_cp_calc -> compute_rl_deriv_nn deriv_nn_fast -> compute_deriv_nn get_nn_sd -> compute_posterior_sd_nn nn_sigma_est -> estimate_noise_nn idx.-> idx_select GPS.new -> GPS_w w.new -> w get.nn.fast -> compute_posterior_m_nn w.est -> w nn_balance -> best_nn_cb","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"GPCERF 0.1.0 (2022-07-02)","text":"Package website using pkgdown Logger functions compute_sd_gp function","code":""},{"path":[]},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"GPCERF 0.0.1 (2022-03-31)","text":"Removed examples internal functions w.obs -> w_obs inv.Sigma.obs -> inv_sigma_obs obs.use -> scaled_obs tune.fn -> compute_m_sigma GP.weights.test -> compute_weight_gp data.generate -> generate_synthetic_data","code":""},{"path":"https://NSAPH-Software.github.io/GPCERF/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"GPCERF 0.0.1 (2022-03-31)","text":"estimate_noise function estimate_cerf_gp function compute_inverse function compute_w_corr function","code":""}]
diff --git a/man/compute_deriv_nn.Rd b/man/compute_deriv_nn.Rd
index 16126bb..da20642 100644
--- a/man/compute_deriv_nn.Rd
+++ b/man/compute_deriv_nn.Rd
@@ -28,7 +28,7 @@ gps: A data.frame of GPS vectors.
 (e_gps_pred).
 - Column 3: Standard deviation of  e_gps (e_gps_std)
 used_params:
-- dnorm_log: TRUE or FLASE}
+- dnorm_log: TRUE or FALSE}
 
 \item{y_obs}{A vector of observed outcome values.}