From 718db470f6be9caa43283802ef9c543e17723fda Mon Sep 17 00:00:00 2001
From: sebastianbossert <143491427+sebastianbossert@users.noreply.github.com>
Date: Wed, 25 Oct 2023 14:12:40 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Boehring?=
 =?UTF-8?q?er-Ingelheim/BayesianMCPMod@10c652fc660ad93dfd495d267bbbc74b4cc?=
 =?UTF-8?q?2b095=20=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 404.html                                      |   2 +-
 LICENSE-text.html                             |   2 +-
 articles/Simulation_Example.html              |   5 +--
 articles/analysis_normal.html                 |  24 +++++---------
 .../figure-html/Model fitting-1.png           | Bin 108733 -> 109425 bytes
 .../figure-html/Model fitting-2.png           | Bin 103936 -> 104518 bytes
 articles/index.html                           |   2 +-
 authors.html                                  |   6 ++--
 index.html                                    |   4 +--
 pkgdown.yml                                   |   2 +-
 reference/assessDesign.html                   |   2 +-
 reference/getBootstrapBands.html              |   2 +-
 reference/getContrMat.html                    |  20 +++++++----
 reference/getCritProb.html                    |  18 ++++++----
 reference/getModelFits.html                   |  26 +++++++++++----
 reference/getPosterior.html                   |  15 +++++----
 reference/getPriorList.html                   |  22 +++++++++----
 reference/index.html                          |   2 +-
 reference/performBayesianMCP.html             |  18 ++++++----
 reference/performBayesianMCPMod.html          |  20 +++++++----
 reference/plot.modelFits.html                 |  31 ++++++++++--------
 reference/simulateData.html                   |  25 ++++++++++----
 search.json                                   |   2 +-
 23 files changed, 152 insertions(+), 98 deletions(-)

diff --git a/404.html b/404.html
index 8297f6f..20e6202 100644
--- a/404.html
+++ b/404.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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/LICENSE-text.html b/LICENSE-text.html
index 7b96ba3..60e6725 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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/articles/Simulation_Example.html b/articles/Simulation_Example.html
index b2e4954..ffbacad 100644
--- a/articles/Simulation_Example.html
+++ b/articles/Simulation_Example.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -141,7 +141,8 @@ <h2 id="calculation-of-a-map-prior">Calculation of a MAP prior<a class="anchor"
 <span></span>
 <span><span class="va">prior_list</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/getPriorList.html">getPriorList</a></span><span class="op">(</span></span>
 <span>  hist_data   <span class="op">=</span> <span class="va">hist_data</span>,</span>
-<span>  dose_levels <span class="op">=</span> <span class="va">dose_levels</span><span class="op">)</span></span></code></pre></div>
+<span>  dose_levels <span class="op">=</span> <span class="va">dose_levels</span>,</span>
+<span>  robustify_weight <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="specification-of-new-trial-design">Specification of new trial design<a class="anchor" aria-label="anchor" href="#specification-of-new-trial-design"></a>
diff --git a/articles/analysis_normal.html b/articles/analysis_normal.html
index 04b49d6..8849868 100644
--- a/articles/analysis_normal.html
+++ b/articles/analysis_normal.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -143,7 +143,7 @@ <h2 id="calculation-of-a-map-prior">Calculation of a MAP prior<a class="anchor"
 <span></span>
 <span><span class="va">prior_list</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/getPriorList.html">getPriorList</a></span><span class="op">(</span></span>
 <span>  hist_data   <span class="op">=</span> <span class="va">hist_data</span>,</span>
-<span>  dose_levels <span class="op">=</span> <span class="va">dose_levels</span><span class="op">)</span></span></code></pre></div>
+<span>  dose_levels <span class="op">=</span> <span class="va">dose_levels</span>,robustify_weight <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="specifications-new-trial">Specifications new trial<a class="anchor" aria-label="anchor" href="#specifications-new-trial"></a>
@@ -185,16 +185,7 @@ <h2 id="combination-of-prior-information-and-trial-results">Combination of prior
 combining the prior information with the estimated results of the new
 trial.</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co">#Simulation of new trial </span></span>
-<span><span class="co">##Note: This step should not be required, as we provide summary measures directly from the new trial</span></span>
-<span><span class="va">data_emax</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/simulateData.html">simulateData</a></span><span class="op">(</span></span>
-<span>  n_patients  <span class="op">=</span> <span class="va">n_patients</span>,</span>
-<span>  dose_levels <span class="op">=</span> <span class="va">dose_levels</span>,</span>
-<span>  sd          <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/attr.html" class="external-link">attr</a></span><span class="op">(</span><span class="va">prior_list</span>, <span class="st">"sd_tot"</span><span class="op">)</span>,</span>
-<span>  mods        <span class="op">=</span> <span class="va">mods</span>,</span>
-<span>  true_model  <span class="op">=</span> <span class="st">"emax"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">posterior</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/getPosterior.html">getPosterior</a></span><span class="op">(</span>prior<span class="op">=</span><span class="va">prior_list</span>,data<span class="op">=</span><span class="va">data_emax</span>,</span>
+<code class="sourceCode R"><span><span class="va">posterior</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/getPosterior.html">getPosterior</a></span><span class="op">(</span>prior<span class="op">=</span><span class="va">prior_list</span>,</span>
 <span>                               mu_hat <span class="op">=</span> <span class="va">new_trial</span><span class="op">$</span><span class="va">rslt</span>,</span>
 <span>                               sd_hat <span class="op">=</span> <span class="va">new_trial</span><span class="op">$</span><span class="va">se</span><span class="op">)</span></span></code></pre></div>
 </div>
@@ -236,10 +227,11 @@ <h2 id="execution-of-bayesian-mcpmod-test-step">Execution of Bayesian MCPMod Tes
 <span></span>
 <span><span class="co">#BMCP_result2</span></span>
 <span></span>
-<span><span class="va">BMCP_result</span></span>
-<span><span class="co">#&gt; Bayesian Multiple Comparison Procedure</span></span>
-<span><span class="co">#&gt;   Estimated Success Rate:  1 </span></span>
-<span><span class="co">#&gt;   N Simulations:           1</span></span></code></pre></div>
+<span><span class="va">BMCP_result</span><span class="op">[</span><span class="fl">1</span>,<span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span>
+<span><span class="co">#&gt;                   sign                  p_val      post_probs.linear </span></span>
+<span><span class="co">#&gt;              1.0000000              0.9692346              0.9182047 </span></span>
+<span><span class="co">#&gt;        post_probs.emax post_probs.exponential </span></span>
+<span><span class="co">#&gt;              0.9692346              0.8254516</span></span></code></pre></div>
 <p>The testing step is significant indicating a non-flat dose-response
 shape. In detail the p-values for the emax model is the most significant
 one.</p>
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diff --git a/articles/index.html b/articles/index.html
index 41d00ee..e104d54 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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/authors.html b/authors.html
index 0475d9d..979b4a9 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -74,13 +74,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Bossert S, Wojciekowski S, Andersen L (2023).
 <em>BayesianMCPMod: Bayesian MCPMod</em>.
-R package version 0.1.3-2, <a href="https://github.com/Boehringer-Ingelheim/BayesianMCPMod" class="external-link">https://github.com/Boehringer-Ingelheim/BayesianMCPMod</a>. 
+R package version 0.1.3-3, <a href="https://github.com/Boehringer-Ingelheim/BayesianMCPMod" class="external-link">https://github.com/Boehringer-Ingelheim/BayesianMCPMod</a>. 
 </p>
       <pre>@Manual{,
   title = {BayesianMCPMod: Bayesian MCPMod},
   author = {Sebastian Bossert and Stephan Wojciekowski and Lars Andersen},
   year = {2023},
-  note = {R package version 0.1.3-2},
+  note = {R package version 0.1.3-3},
   url = {https://github.com/Boehringer-Ingelheim/BayesianMCPMod},
 }</pre>
     </div>
diff --git a/index.html b/index.html
index 7211588..824a15d 100644
--- a/index.html
+++ b/index.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -74,7 +74,7 @@ <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#i
 </h2>
 <p>The development version can be installed from this repository.</p>
 <pre class="{r}"><code><span><span class="co"># install.packages("remotes")</span></span>
-<span><span class="fu">remotes</span><span class="fu">::</span><span class="fu">install_github</span><span class="op">(</span><span class="st">"https://github.com/Boehringer-Ingelheim/BayesianMCPmod"</span><span class="op">)</span></span></code></pre>
+<span><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"https://github.com/Boehringer-Ingelheim/BayesianMCPmod"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a>
diff --git a/pkgdown.yml b/pkgdown.yml
index 4df7355..8d9fa90 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   Simulation_Example: Simulation_Example.html
   analysis_normal: analysis_normal.html
-last_built: 2023-10-20T10:15Z
+last_built: 2023-10-25T14:05Z
 urls:
   reference: https://boehringer-ingelheim.github.io/BayesianMCPMod/reference
   article: https://boehringer-ingelheim.github.io/BayesianMCPMod/articles
diff --git a/reference/assessDesign.html b/reference/assessDesign.html
index e0a3dd2..0ac2d88 100644
--- a/reference/assessDesign.html
+++ b/reference/assessDesign.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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/reference/getBootstrapBands.html b/reference/getBootstrapBands.html
index c8c19d4..51fa6e2 100644
--- a/reference/getBootstrapBands.html
+++ b/reference/getBootstrapBands.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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/reference/getContrMat.html b/reference/getContrMat.html
index 3b297a5..73d593e 100644
--- a/reference/getContrMat.html
+++ b/reference/getContrMat.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="getContrMat"><title>getContrMat — getContrMat • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="getContrMat — getContrMat"><meta property="og:description" content="getContrMat"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function calculates contrast vectors that are optimal for detecting certain alternatives. More information and link to publication will be added."><title>getContrMat — getContrMat • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="getContrMat — getContrMat"><meta property="og:description" content="This function calculates contrast vectors that are optimal for detecting certain alternatives. More information and link to publication will be added."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -50,7 +50,7 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>getContrMat</p>
+    <p>This function calculates contrast vectors that are optimal for detecting certain alternatives. More information and link to publication will be added.</p>
     </div>
 
     <div class="section level2">
@@ -61,21 +61,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>mods</dt>
-<dd><p>tbd</p></dd>
+<dd><p>An object of class "Mods" as specified in the Dosefinding package.</p></dd>
 
 
 <dt>dose_levels</dt>
-<dd><p>tbd</p></dd>
+<dd><p>vector containing the different doseage levels.</p></dd>
 
 
 <dt>dose_weights</dt>
-<dd><p>tbd</p></dd>
+<dd><p>Vector specifying weights for the different doses</p></dd>
 
 
 <dt>prior_list</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a prior_list object</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>contr_mat Object of class ‘⁠optContr⁠’. A list containing entries contMat and muMat, and CorrMat. Specified in the Dosefinding package.</p>
+    </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
diff --git a/reference/getCritProb.html b/reference/getCritProb.html
index 9ab9a96..3656191 100644
--- a/reference/getCritProb.html
+++ b/reference/getCritProb.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -55,27 +55,33 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">getCritProb</span><span class="op">(</span><span class="va">mods</span>, <span class="va">dose_levels</span>, <span class="va">dose_weights</span>, <span class="va">alpha_crit_val</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">getCritProb</span><span class="op">(</span><span class="va">mods</span>, <span class="va">dose_levels</span>, <span class="va">dose_weights</span>, alpha_crit_val <span class="op">=</span> <span class="fl">0.025</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>mods</dt>
-<dd><p>tbd</p></dd>
+<dd><p>An object of class "Mods" as specified in the Dosefinding package.</p></dd>
 
 
 <dt>dose_levels</dt>
-<dd><p>tbd</p></dd>
+<dd><p>vector containing the different doseage levels.</p></dd>
 
 
 <dt>dose_weights</dt>
-<dd><p>tbd</p></dd>
+<dd><p>Vector specifying weights for the different doses</p></dd>
 
 
 <dt>alpha_crit_val</dt>
-<dd><p>tbd</p></dd>
+<dd><p>significance level. Default set to 0.025.</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>crit_pval multiplicity adjusted critical value on the probability scale.</p>
+    </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
diff --git a/reference/getModelFits.html b/reference/getModelFits.html
index 17afd8a..74c1842 100644
--- a/reference/getModelFits.html
+++ b/reference/getModelFits.html
@@ -1,5 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="getModelFits"><title>getModelFits — getModelFits • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="getModelFits — getModelFits"><meta property="og:description" content="getModelFits"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fits dose-response curves for the specified dose-repsonse models, based on the posterior distributions.
+For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions.
+For the default case, the Nelder-Mead algorithm is used. Will be further updated and links to publication as well as references will be added."><title>getModelFits — getModelFits • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="getModelFits — getModelFits"><meta property="og:description" content="Fits dose-response curves for the specified dose-repsonse models, based on the posterior distributions.
+For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions.
+For the default case, the Nelder-Mead algorithm is used. Will be further updated and links to publication as well as references will be added."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -50,7 +54,9 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>getModelFits</p>
+    <p>Fits dose-response curves for the specified dose-repsonse models, based on the posterior distributions.
+For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions.
+For the default case, the Nelder-Mead algorithm is used. Will be further updated and links to publication as well as references will be added.</p>
     </div>
 
     <div class="section level2">
@@ -61,21 +67,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>models</dt>
-<dd><p>tbd</p></dd>
+<dd><p>list of model names for which a fit will be performed.</p></dd>
 
 
 <dt>dose_levels</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a vector containing the different dosage levels.</p></dd>
 
 
 <dt>posterior</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a getPosterior object, containing the (multivariate) posterior distribution per dosage level.</p></dd>
 
 
 <dt>simple</dt>
-<dd><p>tbd</p></dd>
+<dd><p>boolean variable, defining whether simplified fit will be applied. Default FALSE.</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>model_fits returns a list, containing information about the fitted model coefficients, the prediction per dose group as well as maximum effect and generalized AIC per model.</p>
+    </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
diff --git a/reference/getPosterior.html b/reference/getPosterior.html
index af5b7c7..a049054 100644
--- a/reference/getPosterior.html
+++ b/reference/getPosterior.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="getPosterior"><title>getPosterior — getPosterior • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="getPosterior — getPosterior"><meta property="og:description" content="getPosterior"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Either the patient level data or both the mu_hat as well as the sd_hat must to be provided."><title>getPosterior — getPosterior • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="getPosterior — getPosterior"><meta property="og:description" content="Either the patient level data or both the mu_hat as well as the sd_hat must to be provided."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -50,7 +50,7 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>getPosterior</p>
+    <p>Either the patient level data or both the mu_hat as well as the sd_hat must to be provided.</p>
     </div>
 
     <div class="section level2">
@@ -61,19 +61,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>data</dt>
-<dd><p>tbd</p></dd>
+<dd><p>dataframe containing the information of dose and response.
+Also a simulateData object can be provided.</p></dd>
 
 
 <dt>prior_list</dt>
-<dd><p>prior_list</p></dd>
+<dd><p>prior_list object</p></dd>
 
 
 <dt>mu_hat</dt>
-<dd><p>tbd</p></dd>
+<dd><p>vector of estimated mean values</p></dd>
 
 
 <dt>sd_hat</dt>
-<dd><p>tbd</p></dd>
+<dd><p>vector of estimated standard deviations.</p></dd>
 
 </dl></div>
 
diff --git a/reference/getPriorList.html b/reference/getPriorList.html
index 1cbd49c..f545210 100644
--- a/reference/getPriorList.html
+++ b/reference/getPriorList.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -55,25 +55,35 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">getPriorList</span><span class="op">(</span><span class="va">hist_data</span>, <span class="va">dose_levels</span>, dose_names <span class="op">=</span> <span class="cn">NULL</span>, robustify_weight <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">getPriorList</span><span class="op">(</span></span>
+<span>  <span class="va">hist_data</span>,</span>
+<span>  <span class="va">dose_levels</span>,</span>
+<span>  dose_names <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  robustify_weight <span class="op">=</span> <span class="cn">NULL</span></span>
+<span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>hist_data</dt>
-<dd><p>tbd</p></dd>
+<dd><p>historical trial summary level data,
+needs to be provided as a dataframe. Including information of the
+estimates and variability.</p></dd>
 
 
 <dt>dose_levels</dt>
-<dd><p>tbd</p></dd>
+<dd><p>vector of the different doseage levels</p></dd>
 
 
 <dt>dose_names</dt>
-<dd><p>prior_list</p></dd>
+<dd><p>character vector of dose levels,
+default NULL and will be automatically created
+based on the dose levels parameter.</p></dd>
 
 
 <dt>robustify_weight</dt>
-<dd><p>tbd</p></dd>
+<dd><p>Null needs to be provided as a numeric
+value for the weight of the robustification component</p></dd>
 
 </dl></div>
 
diff --git a/reference/index.html b/reference/index.html
index 7c1ff5c..add77e6 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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/reference/performBayesianMCP.html b/reference/performBayesianMCP.html
index 7601e03..86a47c6 100644
--- a/reference/performBayesianMCP.html
+++ b/reference/performBayesianMCP.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="BayesianMCP"><title>BayesianMCP — performBayesianMCP • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="BayesianMCP — performBayesianMCP"><meta property="og:description" content="BayesianMCP"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="performs bayesian MCP Test step."><title>BayesianMCP — performBayesianMCP • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="BayesianMCP — performBayesianMCP"><meta property="og:description" content="performs bayesian MCP Test step."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -50,7 +50,7 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>BayesianMCP</p>
+    <p>performs bayesian MCP Test step.</p>
     </div>
 
     <div class="section level2">
@@ -61,17 +61,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>posteriors_list</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a getPosterior object</p></dd>
 
 
 <dt>contr_mat</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a getContrMat object, contrast matrix to be used for the testing step.</p></dd>
 
 
 <dt>crit_prob</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a getCritProb object</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>b_mcp test result</p>
+    </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
diff --git a/reference/performBayesianMCPMod.html b/reference/performBayesianMCPMod.html
index c450f60..7f6ab73 100644
--- a/reference/performBayesianMCPMod.html
+++ b/reference/performBayesianMCPMod.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="performBayesianMCPMod"><title>performBayesianMCPMod — performBayesianMCPMod • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="performBayesianMCPMod — performBayesianMCPMod"><meta property="og:description" content="performBayesianMCPMod"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="performs bayesian MCP Test step and modelling."><title>performBayesianMCPMod — performBayesianMCPMod • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="performBayesianMCPMod — performBayesianMCPMod"><meta property="og:description" content="performs bayesian MCP Test step and modelling."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -50,7 +50,7 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>performBayesianMCPMod</p>
+    <p>performs bayesian MCP Test step and modelling.</p>
     </div>
 
     <div class="section level2">
@@ -61,21 +61,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>posteriors_list</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a getPosterior object</p></dd>
 
 
 <dt>contr_mat</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a getContrMat object, contrast matrix to be used for the testing step.</p></dd>
 
 
 <dt>crit_prob</dt>
-<dd><p>tbd</p></dd>
+<dd><p>a getCritProb object</p></dd>
 
 
 <dt>simple</dt>
-<dd><p>tbd</p></dd>
+<dd><p>boolean variable, defining whether simplified fit will be applied. Passed to the getModelFits function. Default FALSE.</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>bmcpmod test result as well as modelling result.</p>
+    </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
diff --git a/reference/plot.modelFits.html b/reference/plot.modelFits.html
index c23357e..7dba165 100644
--- a/reference/plot.modelFits.html
+++ b/reference/plot.modelFits.html
@@ -1,5 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="plot.modelFits"><title>plot.modelFits — plot.modelFits • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="plot.modelFits — plot.modelFits"><meta property="og:description" content="plot.modelFits"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit.
+More details to be added, as well as references."><title>plot.modelFits — plot.modelFits • BayesianMCPMod</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="plot.modelFits — plot.modelFits"><meta property="og:description" content="plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit.
+More details to be added, as well as references."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -50,7 +52,8 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>plot.modelFits</p>
+    <p>plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit.
+More details to be added, as well as references.</p>
     </div>
 
     <div class="section level2">
@@ -73,50 +76,50 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>x</dt>
-<dd><p>tbd An object of type modelFits</p></dd>
+<dd><p>an object of type getModelFits</p></dd>
 
 
 <dt>gAIC</dt>
-<dd><p>tbd</p></dd>
+<dd><p>logical value indicating whether gAIC values are shown in the plot. Default TRUE</p></dd>
 
 
 <dt>avg_fit</dt>
-<dd><p>tbd</p></dd>
+<dd><p>logical value indicating whether average fit is presented in the plot. Default TRUE</p></dd>
 
 
 <dt>cr_intv</dt>
-<dd><p>tbd</p></dd>
+<dd><p>logical value indicating whether credible intervals are included in the plot. Default TRUE</p></dd>
 
 
 <dt>alpha_CrI</dt>
-<dd><p>tbd</p></dd>
+<dd><p>numerical value of the width of the credible intervals. Default is set to 0.05 (i.e 95% CI are shown).</p></dd>
 
 
 <dt>cr_bands</dt>
-<dd><p>tbd</p></dd>
+<dd><p>logical value indicating whether bootstrapped based credible bands are shown in the plot. Default FALSE</p></dd>
 
 
 <dt>alpha_CrB</dt>
-<dd><p>tbd</p></dd>
+<dd><p>numerical vector of the width of the credible bands. Default is set to 0.05 and 0.5 (i.e 95% CB and median are shown).</p></dd>
 
 
 <dt>n_bs_smpl</dt>
-<dd><p>tbd</p></dd>
+<dd><p>number of bootstrap samples being used. Default set to 1000.</p></dd>
 
 
 <dt>acc_color</dt>
-<dd><p>tbd</p></dd>
+<dd><p>color of the credible bands. Default set to "orange"</p></dd>
 
 
 <dt>...</dt>
-<dd><p>tbd</p></dd>
+<dd><p>optional parameter to be passed.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     
 
-<p>tbd</p>
+<p>plts returns a ggplot2 object</p>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/simulateData.html b/reference/simulateData.html
index 06beea8..595a655 100644
--- a/reference/simulateData.html
+++ b/reference/simulateData.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">BayesianMCPMod</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3-3</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">
@@ -68,29 +68,40 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>n_patients</dt>
-<dd><p>tbd</p></dd>
+<dd><p>vector containing number of patients as a numerical
+value per dose-group.</p></dd>
 
 
 <dt>dose_levels</dt>
-<dd><p>tbd</p></dd>
+<dd><p>vector containing the different doseage levels.</p></dd>
 
 
 <dt>sd</dt>
-<dd><p>tbd</p></dd>
+<dd><p>standard deviation on patient level.</p></dd>
 
 
 <dt>mods</dt>
-<dd><p>tbd</p></dd>
+<dd><p>An object of class "Mods" as specified in the Dosefinding package.</p></dd>
 
 
 <dt>n_sim</dt>
-<dd><p>tbd</p></dd>
+<dd><p>number of simulations to be performed,
+Default is 1000</p></dd>
 
 
 <dt>true_model</dt>
-<dd><p>tbd</p></dd>
+<dd><p>Default value is NULL.
+Assumed true underlying model. Provided via a String. e.g. "emax".
+In case of NULL, all dose-response models, included in the mods input parameter will be used.</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>sim_data one list object, containing patient level simulated data for all assumed true models.
+Also providing information about simulation iteration, patient number as well as dosage levels.</p>
+    </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside></div>
diff --git a/search.json b/search.json
index 1231fac..1ebdd15 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"background-and-data","dir":"Articles","previous_headings":"","what":"Background and data","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"vignette show use Bayesian MCPMod package trial planning continuous distributed data. [link vignette] focus indication MDD make use historical data included clinDR package. specifically trial results BRINTELLIX utilized establish informative prior control group.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"calculation-of-a-map-prior","dir":"Articles","previous_headings":"","what":"Calculation of a MAP prior","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"first step meta analytic prior calculated using historical data 5 trials (main endpoint CfB MADRS score 8 weeks). Please note information control group integrated (leading informative multicomponent prior control group), active groups non-informative prior specified.","code":"data(\"metaData\") testdata    <- as.data.frame(metaData) dataset     <- filter(testdata, bname == \"BRINTELLIX\") histcontrol <- filter(dataset, dose == 0, primtime == 8, indication == \"MAJOR DEPRESSIVE DISORDER\")  ##Create MAP Prior hist_data <- data.frame(   trial = histcontrol$nctno,   est   = histcontrol$rslt,   se    = histcontrol$se,   sd    = histcontrol$sd,   n     = histcontrol$sampsize)  dose_levels <- c(0, 2.5, 5, 10, 20)  prior_list <- getPriorList(   hist_data   = hist_data,   dose_levels = dose_levels)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"specification-of-new-trial-design","dir":"Articles","previous_headings":"","what":"Specification of new trial design","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"hypothetical new trial plan 4 active dose levels \\(2.5, 5, 10, 20\\) specify broad set potential dose-response relationships, including linear, exponential, emax sigEMax model. Furthermore assume maximum effect -3 top control (.e. assuming active treatment can reduce MADRS score 8 weeks 15.8) plan trial 80 patients active groups 60 patients control.","code":"#Pre-Specification (B)MCPMod   ## candidate models for MCPMod # linear function - no guestimates needed exp     <- DoseFinding::guesst(d     = 5,                                p     = c(0.2),                                model = \"exponential\",                                Maxd  = max(dose_levels)) emax    <- DoseFinding::guesst(d     = 2.5,                                p     = c(0.9),                                model = \"emax\") sigemax<-  DoseFinding::guesst(d     = c(2.5,5),                                p     = c(0.1,0.6),                                model = \"sigEmax\") #beta <- DoseFinding::guesst(d=5, p=0.8, model=\"betaMod\", dMax=1, scal=1.2, Maxd=20)  mods <- DoseFinding::Mods(   linear      = NULL,   emax        = emax,   exponential = exp,   sigEmax     = sigemax,   #betaMod     = beta,   doses       = dose_levels,   maxEff      = -3,   placEff     = -12.8)  n_patients=c(60,80,80,80,80)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"calculation-of-success-probabilities","dir":"Articles","previous_headings":"","what":"Calculation of success probabilities","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"calculate success probabilities different assumed dose-response models specified trial design apply assessDesign function. alternative design evaluate design overall sample size putting patients highest dose group (control). specific trial setting adapted allocation ratio leads increased success probabilities assumed dose response relationships.","code":"success_probabilities<-assessDesign (n_patients=n_patients,   mods=mods,   prior_list=prior_list) success_probabilities #> $linear #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.669  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.597       0.230       0.588       0.475  #>  #> $emax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.704  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.246       0.665       0.205       0.322  #>  #> $exponential #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.666  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.605       0.208       0.605       0.437  #>  #> $sigEmax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.8  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.607       0.430       0.558       0.765 success_probabilities_uneq<-assessDesign (n_patients=c(80,60,60,60,120),   mods=mods,   prior_list=prior_list) success_probabilities_uneq #> $linear #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.799  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.743       0.432       0.735       0.670  #>  #> $emax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.844  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.399       0.805       0.369       0.538  #>  #> $exponential #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.807  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.756       0.394       0.755       0.656  #>  #> $sigEmax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.864  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.717       0.638       0.684       0.824"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"background-and-data","dir":"Articles","previous_headings":"","what":"Background and data","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"vignette show use Bayesian MCPMod package continuous distributed data. Hereby focus utilization informative prior BayesianMCPMod evaluation single trial. use data included clinDR package. specifically trial results BRINTELLIX used illustrate specification informative prior usage prior bayesian evaluation (hypothetical) new trial. information around BRINTELLIX added…","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"calculation-of-a-map-prior","dir":"Articles","previous_headings":"","what":"Calculation of a MAP prior","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"first step meta analytic prior calculated using historical data 4 trials (main endpoint CfB MADRS score 8 weeks). Please note information control group integrated (leading informative multicomponent prior control group), active groups non-informative prior specified.","code":"data(\"metaData\") testdata    <- as.data.frame(metaData) dataset     <- filter(testdata, bname == \"BRINTELLIX\") histcontrol <- filter(dataset, dose == 0, primtime == 8, indication == \"MAJOR DEPRESSIVE DISORDER\",protid!=6)  ##Create MAP Prior hist_data <- data.frame(   trial = histcontrol$nctno,   est   = histcontrol$rslt,   se    = histcontrol$se,   sd    = histcontrol$sd,   n     = histcontrol$sampsize)  dose_levels <- c(0, 2.5, 5, 10)  prior_list <- getPriorList(   hist_data   = hist_data,   dose_levels = dose_levels)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"specifications-new-trial","dir":"Articles","previous_headings":"","what":"Specifications new trial","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"use trial ct.gov number NCT00635219 exemplary new trial. able apply bayesian MCPMod approach, candidate models need specified. Since 3 active dose levels limit set candidate models linear, exponential emax model.","code":"#Pre-Specification (B)MCPMod   ## candidate models for MCPMod # linear function - no guestimates needed exp     <- DoseFinding::guesst(d     = 5,                                p     = c(0.2),                                model = \"exponential\",                                Maxd  = max(dose_levels)) emax    <- DoseFinding::guesst(d     = 2.5,                                p     = c(0.9),                                model = \"emax\")   mods <- DoseFinding::Mods(   linear      = NULL,   emax        = emax,   exponential = exp,   doses       = dose_levels,   maxEff      = -1,   placEff     = -12.8)  new_trial <- filter(dataset, primtime == 8, indication == \"MAJOR DEPRESSIVE DISORDER\",protid==6) n_patients <- c(150, 142, 147, 149)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"combination-of-prior-information-and-trial-results","dir":"Articles","previous_headings":"","what":"Combination of prior information and trial results","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"outlined citePaper, first step posterior calculated combining prior information estimated results new trial.","code":"#Simulation of new trial  ##Note: This step should not be required, as we provide summary measures directly from the new trial data_emax <- simulateData(   n_patients  = n_patients,   dose_levels = dose_levels,   sd          = attr(prior_list, \"sd_tot\"),   mods        = mods,   true_model  = \"emax\")  posterior <- getPosterior(prior=prior_list,data=data_emax,                                mu_hat = new_trial$rslt,                                sd_hat = new_trial$se)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"execution-of-bayesian-mcpmod-test-step","dir":"Articles","previous_headings":"","what":"Execution of Bayesian MCPMod Test step","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"execution testing step bayesian MCPMod critical value (probability scale) determined given alpha level. addition contrast matrix created. Please note different possibilities generate contrasts. information used input bayesian MCP testing function. testing step significant indicating non-flat dose-response shape. detail p-values emax model significant one.","code":"crit_pval <- getCritProb(   mods           = mods,   dose_levels    = dose_levels,   dose_weights   = n_patients,   alpha_crit_val = 0.1)  contr_mat_prior <- getContrMat(   mods           = mods,   dose_levels    = dose_levels,   dose_weights   = n_patients,   prior_list     = prior_list)  #This would be the most reasonable output, but since it is not exported it is currently not usable. #BMCP_result<-BayesMCPi(posterior_i = posterior,      #     contr_mat       = contr_mat_prior,        #    crit_prob       = crit_pval)  BMCP_result <- performBayesianMCP(   posteriors_list = posterior,   contr_mat       = contr_mat_prior,   crit_prob       = crit_pval)  #BMCP_result2 <- performBayesianMCPMod( #    posteriors_list = posterior_emax, #    contr_mat       = contr_mat_prior,  #    crit_prob       = crit_pval)  #BMCP_result2  BMCP_result #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  1  #>   N Simulations:           1"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"model-fitting-and-visualization-of-results","dir":"Articles","previous_headings":"","what":"Model fitting and visualization of results","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"model fitting step posterior distribution used basis. simplified full fitting performed. Please note models fitted data even though significant testing step. plotting bootstrap based credible intervals (50% 95%) shown well.  # Additional notes TBD, whether certain wrapper functions mentioned.","code":"#Model fit post_observed <- posterior model_shapes  <- c(\"linear\", \"emax\", \"exponential\")  # Option a) Simplified approach by using frequentist idea fit_simple <- getModelFits(   models      = model_shapes,   dose_levels = dose_levels,   posterior   = post_observed,   simple      = TRUE)  # Option b) Making use of the complete posterior distribution fit <- getModelFits(   models      = model_shapes,   dose_levels = dose_levels,   posterior   = post_observed,   simple      = FALSE)  plot(fit,cr_bands=TRUE) plot(fit_simple, cr_bands = TRUE)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Sebastian Bossert. Author. Stephan Wojciekowski. Author, maintainer. Lars Andersen. Author. Boehringer Ingelheim Pharma GmbH & Co. KG. Copyright holder, funder.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Bossert S, Wojciekowski S, Andersen L (2023). BayesianMCPMod: Bayesian MCPMod. R package version 0.1.3-2, https://github.com/Boehringer-Ingelheim/BayesianMCPMod.","code":"@Manual{,   title = {BayesianMCPMod: Bayesian MCPMod},   author = {Sebastian Bossert and Stephan Wojciekowski and Lars Andersen},   year = {2023},   note = {R package version 0.1.3-2},   url = {https://github.com/Boehringer-Ingelheim/BayesianMCPMod}, }"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"the-bayesianmcpmod-package","dir":"","previous_headings":"","what":"Bayesian MCPMod","title":"Bayesian MCPMod","text":"…","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bayesian MCPMod","text":"development version can installed repository.","code":"# install.packages(\"remotes\") remotes::install_github(\"https://github.com/Boehringer-Ingelheim/BayesianMCPmod\")"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Bayesian MCPMod","text":"package documentation hosted .","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Bayesian MCPMod","text":"…","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/assessDesign.html","id":null,"dir":"Reference","previous_headings":"","what":"assessDesign — assessDesign","title":"assessDesign — assessDesign","text":"assessDesign","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/assessDesign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"assessDesign — assessDesign","text":"","code":"assessDesign(   n_patients,   mods,   prior_list,   n_sim = 1000,   alpha_crit_val = 0.05,   simple = TRUE )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/assessDesign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"assessDesign — assessDesign","text":"n_patients tbd mods tbd prior_list tbd n_sim tbd alpha_crit_val tbd simple tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":null,"dir":"Reference","previous_headings":"","what":"getBootstrapBands — getBootstrapBands","title":"getBootstrapBands — getBootstrapBands","text":"getBootstrapBands","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getBootstrapBands — getBootstrapBands","text":"","code":"getBootstrapBands(   model_fits,   n_samples = 1000,   alpha = c(0.05, 0.5),   avg_fit = TRUE,   dose_seq = NULL )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getBootstrapBands — getBootstrapBands","text":"model_fits tbd n_samples tbd alpha tbd avg_fit tbd dose_seq tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"getBootstrapBands — getBootstrapBands","text":"tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getContrMat.html","id":null,"dir":"Reference","previous_headings":"","what":"getContrMat — getContrMat","title":"getContrMat — getContrMat","text":"getContrMat","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getContrMat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getContrMat — getContrMat","text":"","code":"getContrMat(mods, dose_levels, dose_weights, prior_list)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getContrMat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getContrMat — getContrMat","text":"mods tbd dose_levels tbd dose_weights tbd prior_list tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getCritProb.html","id":null,"dir":"Reference","previous_headings":"","what":"getCritProb — getCritProb","title":"getCritProb — getCritProb","text":"getCritProb","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getCritProb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getCritProb — getCritProb","text":"","code":"getCritProb(mods, dose_levels, dose_weights, alpha_crit_val)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getCritProb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getCritProb — getCritProb","text":"mods tbd dose_levels tbd dose_weights tbd alpha_crit_val tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getModelFits.html","id":null,"dir":"Reference","previous_headings":"","what":"getModelFits — getModelFits","title":"getModelFits — getModelFits","text":"getModelFits","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getModelFits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getModelFits — getModelFits","text":"","code":"getModelFits(models, dose_levels, posterior, simple = FALSE)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getModelFits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getModelFits — getModelFits","text":"models tbd dose_levels tbd posterior tbd simple tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPosterior.html","id":null,"dir":"Reference","previous_headings":"","what":"getPosterior — getPosterior","title":"getPosterior — getPosterior","text":"getPosterior","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPosterior.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getPosterior — getPosterior","text":"","code":"getPosterior(data, prior_list, mu_hat = NULL, sd_hat = NULL)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPosterior.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getPosterior — getPosterior","text":"data tbd prior_list prior_list mu_hat tbd sd_hat tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPriorList.html","id":null,"dir":"Reference","previous_headings":"","what":"getPriorList — getPriorList","title":"getPriorList — getPriorList","text":"getPriorList","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPriorList.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getPriorList — getPriorList","text":"","code":"getPriorList(hist_data, dose_levels, dose_names = NULL, robustify_weight = 0.5)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPriorList.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getPriorList — getPriorList","text":"hist_data tbd dose_levels tbd dose_names prior_list robustify_weight tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCP.html","id":null,"dir":"Reference","previous_headings":"","what":"BayesianMCP — performBayesianMCP","title":"BayesianMCP — performBayesianMCP","text":"BayesianMCP","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCP.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BayesianMCP — performBayesianMCP","text":"","code":"performBayesianMCP(posteriors_list, contr_mat, crit_prob)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCP.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BayesianMCP — performBayesianMCP","text":"posteriors_list tbd contr_mat tbd crit_prob tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCPMod.html","id":null,"dir":"Reference","previous_headings":"","what":"performBayesianMCPMod — performBayesianMCPMod","title":"performBayesianMCPMod — performBayesianMCPMod","text":"performBayesianMCPMod","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCPMod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"performBayesianMCPMod — performBayesianMCPMod","text":"","code":"performBayesianMCPMod(posteriors_list, contr_mat, crit_prob, simple = FALSE)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCPMod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"performBayesianMCPMod — performBayesianMCPMod","text":"posteriors_list tbd contr_mat tbd crit_prob tbd simple tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":null,"dir":"Reference","previous_headings":"","what":"plot.modelFits — plot.modelFits","title":"plot.modelFits — plot.modelFits","text":"plot.modelFits","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot.modelFits — plot.modelFits","text":"","code":"# S3 method for modelFits plot(   x,   gAIC = TRUE,   avg_fit = TRUE,   cr_intv = TRUE,   alpha_CrI = 0.05,   cr_bands = FALSE,   alpha_CrB = c(0.05, 0.5),   n_bs_smpl = 1000,   acc_color = \"orange\",   ... )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot.modelFits — plot.modelFits","text":"x tbd object type modelFits gAIC tbd avg_fit tbd cr_intv tbd alpha_CrI tbd cr_bands tbd alpha_CrB tbd n_bs_smpl tbd acc_color tbd ... tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot.modelFits — plot.modelFits","text":"tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/simulateData.html","id":null,"dir":"Reference","previous_headings":"","what":"simulateData — simulateData","title":"simulateData — simulateData","text":"simulateData","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/simulateData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"simulateData — simulateData","text":"","code":"simulateData(   n_patients,   dose_levels,   sd,   mods,   n_sim = 1000,   true_model = NULL )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/simulateData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"simulateData — simulateData","text":"n_patients tbd dose_levels tbd sd tbd mods tbd n_sim tbd true_model tbd","code":""}]
+[{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"background-and-data","dir":"Articles","previous_headings":"","what":"Background and data","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"vignette show use Bayesian MCPMod package trial planning continuous distributed data. [link vignette] focus indication MDD make use historical data included clinDR package. specifically trial results BRINTELLIX utilized establish informative prior control group.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"calculation-of-a-map-prior","dir":"Articles","previous_headings":"","what":"Calculation of a MAP prior","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"first step meta analytic prior calculated using historical data 5 trials (main endpoint CfB MADRS score 8 weeks). Please note information control group integrated (leading informative multicomponent prior control group), active groups non-informative prior specified.","code":"data(\"metaData\") testdata    <- as.data.frame(metaData) dataset     <- filter(testdata, bname == \"BRINTELLIX\") histcontrol <- filter(dataset, dose == 0, primtime == 8, indication == \"MAJOR DEPRESSIVE DISORDER\")  ##Create MAP Prior hist_data <- data.frame(   trial = histcontrol$nctno,   est   = histcontrol$rslt,   se    = histcontrol$se,   sd    = histcontrol$sd,   n     = histcontrol$sampsize)  dose_levels <- c(0, 2.5, 5, 10, 20)  prior_list <- getPriorList(   hist_data   = hist_data,   dose_levels = dose_levels,   robustify_weight = 0.5)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"specification-of-new-trial-design","dir":"Articles","previous_headings":"","what":"Specification of new trial design","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"hypothetical new trial plan 4 active dose levels \\(2.5, 5, 10, 20\\) specify broad set potential dose-response relationships, including linear, exponential, emax sigEMax model. Furthermore assume maximum effect -3 top control (.e. assuming active treatment can reduce MADRS score 8 weeks 15.8) plan trial 80 patients active groups 60 patients control.","code":"#Pre-Specification (B)MCPMod   ## candidate models for MCPMod # linear function - no guestimates needed exp     <- DoseFinding::guesst(d     = 5,                                p     = c(0.2),                                model = \"exponential\",                                Maxd  = max(dose_levels)) emax    <- DoseFinding::guesst(d     = 2.5,                                p     = c(0.9),                                model = \"emax\") sigemax<-  DoseFinding::guesst(d     = c(2.5,5),                                p     = c(0.1,0.6),                                model = \"sigEmax\") #beta <- DoseFinding::guesst(d=5, p=0.8, model=\"betaMod\", dMax=1, scal=1.2, Maxd=20)  mods <- DoseFinding::Mods(   linear      = NULL,   emax        = emax,   exponential = exp,   sigEmax     = sigemax,   #betaMod     = beta,   doses       = dose_levels,   maxEff      = -3,   placEff     = -12.8)  n_patients=c(60,80,80,80,80)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/Simulation_Example.html","id":"calculation-of-success-probabilities","dir":"Articles","previous_headings":"","what":"Calculation of success probabilities","title":"Simulation Example of Bayesian MCPMod for Continuous Data","text":"calculate success probabilities different assumed dose-response models specified trial design apply assessDesign function. alternative design evaluate design overall sample size putting patients highest dose group (control). specific trial setting adapted allocation ratio leads increased success probabilities assumed dose response relationships.","code":"success_probabilities<-assessDesign (n_patients=n_patients,   mods=mods,   prior_list=prior_list) success_probabilities #> $linear #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.669  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.597       0.230       0.588       0.475  #>  #> $emax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.704  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.246       0.665       0.205       0.322  #>  #> $exponential #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.666  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.605       0.208       0.605       0.437  #>  #> $sigEmax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.8  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.607       0.430       0.558       0.765 success_probabilities_uneq<-assessDesign (n_patients=c(80,60,60,60,120),   mods=mods,   prior_list=prior_list) success_probabilities_uneq #> $linear #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.799  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.743       0.432       0.735       0.670  #>  #> $emax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.844  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.399       0.805       0.369       0.538  #>  #> $exponential #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.807  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.756       0.394       0.755       0.656  #>  #> $sigEmax #> Bayesian Multiple Comparison Procedure #>   Estimated Success Rate:  0.864  #>   N Simulations:           1000 #> Model Significance Frequencies #>      linear        emax exponential     sigEmax  #>       0.717       0.638       0.684       0.824"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"background-and-data","dir":"Articles","previous_headings":"","what":"Background and data","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"vignette show use Bayesian MCPMod package continuous distributed data. Hereby focus utilization informative prior BayesianMCPMod evaluation single trial. use data included clinDR package. specifically trial results BRINTELLIX used illustrate specification informative prior usage prior bayesian evaluation (hypothetical) new trial. information around BRINTELLIX added…","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"calculation-of-a-map-prior","dir":"Articles","previous_headings":"","what":"Calculation of a MAP prior","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"first step meta analytic prior calculated using historical data 4 trials (main endpoint CfB MADRS score 8 weeks). Please note information control group integrated (leading informative multicomponent prior control group), active groups non-informative prior specified.","code":"data(\"metaData\") testdata    <- as.data.frame(metaData) dataset     <- filter(testdata, bname == \"BRINTELLIX\") histcontrol <- filter(dataset, dose == 0, primtime == 8, indication == \"MAJOR DEPRESSIVE DISORDER\",protid!=6)  ##Create MAP Prior hist_data <- data.frame(   trial = histcontrol$nctno,   est   = histcontrol$rslt,   se    = histcontrol$se,   sd    = histcontrol$sd,   n     = histcontrol$sampsize)  dose_levels <- c(0, 2.5, 5, 10)  prior_list <- getPriorList(   hist_data   = hist_data,   dose_levels = dose_levels,robustify_weight = 0.5)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"specifications-new-trial","dir":"Articles","previous_headings":"","what":"Specifications new trial","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"use trial ct.gov number NCT00635219 exemplary new trial. able apply bayesian MCPMod approach, candidate models need specified. Since 3 active dose levels limit set candidate models linear, exponential emax model.","code":"#Pre-Specification (B)MCPMod   ## candidate models for MCPMod # linear function - no guestimates needed exp     <- DoseFinding::guesst(d     = 5,                                p     = c(0.2),                                model = \"exponential\",                                Maxd  = max(dose_levels)) emax    <- DoseFinding::guesst(d     = 2.5,                                p     = c(0.9),                                model = \"emax\")   mods <- DoseFinding::Mods(   linear      = NULL,   emax        = emax,   exponential = exp,   doses       = dose_levels,   maxEff      = -1,   placEff     = -12.8)  new_trial <- filter(dataset, primtime == 8, indication == \"MAJOR DEPRESSIVE DISORDER\",protid==6) n_patients <- c(150, 142, 147, 149)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"combination-of-prior-information-and-trial-results","dir":"Articles","previous_headings":"","what":"Combination of prior information and trial results","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"outlined citePaper, first step posterior calculated combining prior information estimated results new trial.","code":"posterior <- getPosterior(prior=prior_list,                                mu_hat = new_trial$rslt,                                sd_hat = new_trial$se)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"execution-of-bayesian-mcpmod-test-step","dir":"Articles","previous_headings":"","what":"Execution of Bayesian MCPMod Test step","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"execution testing step bayesian MCPMod critical value (probability scale) determined given alpha level. addition contrast matrix created. Please note different possibilities generate contrasts. information used input bayesian MCP testing function. testing step significant indicating non-flat dose-response shape. detail p-values emax model significant one.","code":"crit_pval <- getCritProb(   mods           = mods,   dose_levels    = dose_levels,   dose_weights   = n_patients,   alpha_crit_val = 0.1)  contr_mat_prior <- getContrMat(   mods           = mods,   dose_levels    = dose_levels,   dose_weights   = n_patients,   prior_list     = prior_list)  #This would be the most reasonable output, but since it is not exported it is currently not usable. #BMCP_result<-BayesMCPi(posterior_i = posterior,      #     contr_mat       = contr_mat_prior,        #    crit_prob       = crit_pval)  BMCP_result <- performBayesianMCP(   posteriors_list = posterior,   contr_mat       = contr_mat_prior,   crit_prob       = crit_pval)  #BMCP_result2 <- performBayesianMCPMod( #    posteriors_list = posterior_emax, #    contr_mat       = contr_mat_prior,  #    crit_prob       = crit_pval)  #BMCP_result2  BMCP_result[1,1:5] #>                   sign                  p_val      post_probs.linear  #>              1.0000000              0.9692346              0.9182047  #>        post_probs.emax post_probs.exponential  #>              0.9692346              0.8254516"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/articles/analysis_normal.html","id":"model-fitting-and-visualization-of-results","dir":"Articles","previous_headings":"","what":"Model fitting and visualization of results","title":"Analysis Example of Bayesian MCPMod for Continuous Data","text":"model fitting step posterior distribution used basis. simplified full fitting performed. Please note models fitted data even though significant testing step. plotting bootstrap based credible intervals (50% 95%) shown well.  # Additional notes TBD, whether certain wrapper functions mentioned.","code":"#Model fit post_observed <- posterior model_shapes  <- c(\"linear\", \"emax\", \"exponential\")  # Option a) Simplified approach by using frequentist idea fit_simple <- getModelFits(   models      = model_shapes,   dose_levels = dose_levels,   posterior   = post_observed,   simple      = TRUE)  # Option b) Making use of the complete posterior distribution fit <- getModelFits(   models      = model_shapes,   dose_levels = dose_levels,   posterior   = post_observed,   simple      = FALSE)  plot(fit,cr_bands=TRUE) plot(fit_simple, cr_bands = TRUE)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Sebastian Bossert. Author. Stephan Wojciekowski. Author, maintainer. Lars Andersen. Author. Boehringer Ingelheim Pharma GmbH & Co. KG. Copyright holder, funder.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Bossert S, Wojciekowski S, Andersen L (2023). BayesianMCPMod: Bayesian MCPMod. R package version 0.1.3-3, https://github.com/Boehringer-Ingelheim/BayesianMCPMod.","code":"@Manual{,   title = {BayesianMCPMod: Bayesian MCPMod},   author = {Sebastian Bossert and Stephan Wojciekowski and Lars Andersen},   year = {2023},   note = {R package version 0.1.3-3},   url = {https://github.com/Boehringer-Ingelheim/BayesianMCPMod}, }"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"the-bayesianmcpmod-package","dir":"","previous_headings":"","what":"Bayesian MCPMod","title":"Bayesian MCPMod","text":"…","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bayesian MCPMod","text":"development version can installed repository.","code":"# install.packages(\"remotes\") remotes::install_github(\"https://github.com/Boehringer-Ingelheim/BayesianMCPmod\")"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Bayesian MCPMod","text":"package documentation hosted .","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Bayesian MCPMod","text":"…","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/assessDesign.html","id":null,"dir":"Reference","previous_headings":"","what":"assessDesign — assessDesign","title":"assessDesign — assessDesign","text":"assessDesign","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/assessDesign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"assessDesign — assessDesign","text":"","code":"assessDesign(   n_patients,   mods,   prior_list,   n_sim = 1000,   alpha_crit_val = 0.05,   simple = TRUE )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/assessDesign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"assessDesign — assessDesign","text":"n_patients tbd mods tbd prior_list tbd n_sim tbd alpha_crit_val tbd simple tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":null,"dir":"Reference","previous_headings":"","what":"getBootstrapBands — getBootstrapBands","title":"getBootstrapBands — getBootstrapBands","text":"getBootstrapBands","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getBootstrapBands — getBootstrapBands","text":"","code":"getBootstrapBands(   model_fits,   n_samples = 1000,   alpha = c(0.05, 0.5),   avg_fit = TRUE,   dose_seq = NULL )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getBootstrapBands — getBootstrapBands","text":"model_fits tbd n_samples tbd alpha tbd avg_fit tbd dose_seq tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getBootstrapBands.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"getBootstrapBands — getBootstrapBands","text":"tbd","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getContrMat.html","id":null,"dir":"Reference","previous_headings":"","what":"getContrMat — getContrMat","title":"getContrMat — getContrMat","text":"function calculates contrast vectors optimal detecting certain alternatives. information link publication added.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getContrMat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getContrMat — getContrMat","text":"","code":"getContrMat(mods, dose_levels, dose_weights, prior_list)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getContrMat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getContrMat — getContrMat","text":"mods object class \"Mods\" specified Dosefinding package. dose_levels vector containing different doseage levels. dose_weights Vector specifying weights different doses prior_list prior_list object","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getContrMat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"getContrMat — getContrMat","text":"contr_mat Object class ‘⁠optContr⁠’. list containing entries contMat muMat, CorrMat. Specified Dosefinding package.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getCritProb.html","id":null,"dir":"Reference","previous_headings":"","what":"getCritProb — getCritProb","title":"getCritProb — getCritProb","text":"getCritProb","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getCritProb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getCritProb — getCritProb","text":"","code":"getCritProb(mods, dose_levels, dose_weights, alpha_crit_val = 0.025)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getCritProb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getCritProb — getCritProb","text":"mods object class \"Mods\" specified Dosefinding package. dose_levels vector containing different doseage levels. dose_weights Vector specifying weights different doses alpha_crit_val significance level. Default set 0.025.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getCritProb.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"getCritProb — getCritProb","text":"crit_pval multiplicity adjusted critical value probability scale.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getModelFits.html","id":null,"dir":"Reference","previous_headings":"","what":"getModelFits — getModelFits","title":"getModelFits — getModelFits","text":"Fits dose-response curves specified dose-repsonse models, based posterior distributions. simplified fit, multivariate normal distributions approximated reduced one-dimensional normal distributions. default case, Nelder-Mead algorithm used. updated links publication well references added.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getModelFits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getModelFits — getModelFits","text":"","code":"getModelFits(models, dose_levels, posterior, simple = FALSE)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getModelFits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getModelFits — getModelFits","text":"models list model names fit performed. dose_levels vector containing different dosage levels. posterior getPosterior object, containing (multivariate) posterior distribution per dosage level. simple boolean variable, defining whether simplified fit applied. Default FALSE.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getModelFits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"getModelFits — getModelFits","text":"model_fits returns list, containing information fitted model coefficients, prediction per dose group well maximum effect generalized AIC per model.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPosterior.html","id":null,"dir":"Reference","previous_headings":"","what":"getPosterior — getPosterior","title":"getPosterior — getPosterior","text":"Either patient level data mu_hat well sd_hat must provided.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPosterior.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getPosterior — getPosterior","text":"","code":"getPosterior(data, prior_list, mu_hat = NULL, sd_hat = NULL)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPosterior.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getPosterior — getPosterior","text":"data dataframe containing information dose response. Also simulateData object can provided. prior_list prior_list object mu_hat vector estimated mean values sd_hat vector estimated standard deviations.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPriorList.html","id":null,"dir":"Reference","previous_headings":"","what":"getPriorList — getPriorList","title":"getPriorList — getPriorList","text":"getPriorList","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPriorList.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"getPriorList — getPriorList","text":"","code":"getPriorList(   hist_data,   dose_levels,   dose_names = NULL,   robustify_weight = NULL )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/getPriorList.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"getPriorList — getPriorList","text":"hist_data historical trial summary level data, needs provided dataframe. Including information estimates variability. dose_levels vector different doseage levels dose_names character vector dose levels, default NULL automatically created based dose levels parameter. robustify_weight Null needs provided numeric value weight robustification component","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCP.html","id":null,"dir":"Reference","previous_headings":"","what":"BayesianMCP — performBayesianMCP","title":"BayesianMCP — performBayesianMCP","text":"performs bayesian MCP Test step.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCP.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BayesianMCP — performBayesianMCP","text":"","code":"performBayesianMCP(posteriors_list, contr_mat, crit_prob)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCP.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BayesianMCP — performBayesianMCP","text":"posteriors_list getPosterior object contr_mat getContrMat object, contrast matrix used testing step. crit_prob getCritProb object","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCP.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"BayesianMCP — performBayesianMCP","text":"b_mcp test result","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCPMod.html","id":null,"dir":"Reference","previous_headings":"","what":"performBayesianMCPMod — performBayesianMCPMod","title":"performBayesianMCPMod — performBayesianMCPMod","text":"performs bayesian MCP Test step modelling.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCPMod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"performBayesianMCPMod — performBayesianMCPMod","text":"","code":"performBayesianMCPMod(posteriors_list, contr_mat, crit_prob, simple = FALSE)"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCPMod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"performBayesianMCPMod — performBayesianMCPMod","text":"posteriors_list getPosterior object contr_mat getContrMat object, contrast matrix used testing step. crit_prob getCritProb object simple boolean variable, defining whether simplified fit applied. Passed getModelFits function. Default FALSE.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/performBayesianMCPMod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"performBayesianMCPMod — performBayesianMCPMod","text":"bmcpmod test result well modelling result.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":null,"dir":"Reference","previous_headings":"","what":"plot.modelFits — plot.modelFits","title":"plot.modelFits — plot.modelFits","text":"plot function based ggplot2 package. Providing visualizations model average Fit. details added, well references.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot.modelFits — plot.modelFits","text":"","code":"# S3 method for modelFits plot(   x,   gAIC = TRUE,   avg_fit = TRUE,   cr_intv = TRUE,   alpha_CrI = 0.05,   cr_bands = FALSE,   alpha_CrB = c(0.05, 0.5),   n_bs_smpl = 1000,   acc_color = \"orange\",   ... )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot.modelFits — plot.modelFits","text":"x object type getModelFits gAIC logical value indicating whether gAIC values shown plot. Default TRUE avg_fit logical value indicating whether average fit presented plot. Default TRUE cr_intv logical value indicating whether credible intervals included plot. Default TRUE alpha_CrI numerical value width credible intervals. Default set 0.05 (.e 95% CI shown). cr_bands logical value indicating whether bootstrapped based credible bands shown plot. Default FALSE alpha_CrB numerical vector width credible bands. Default set 0.05 0.5 (.e 95% CB median shown). n_bs_smpl number bootstrap samples used. Default set 1000. acc_color color credible bands. Default set \"orange\" ... optional parameter passed.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/plot.modelFits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot.modelFits — plot.modelFits","text":"plts returns ggplot2 object","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/simulateData.html","id":null,"dir":"Reference","previous_headings":"","what":"simulateData — simulateData","title":"simulateData — simulateData","text":"simulateData","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/simulateData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"simulateData — simulateData","text":"","code":"simulateData(   n_patients,   dose_levels,   sd,   mods,   n_sim = 1000,   true_model = NULL )"},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/simulateData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"simulateData — simulateData","text":"n_patients vector containing number patients numerical value per dose-group. dose_levels vector containing different doseage levels. sd standard deviation patient level. mods object class \"Mods\" specified Dosefinding package. n_sim number simulations performed, Default 1000 true_model Default value NULL. Assumed true underlying model. Provided via String. e.g. \"emax\". case NULL, dose-response models, included mods input parameter used.","code":""},{"path":"https://boehringer-ingelheim.github.io/BayesianMCPMod/reference/simulateData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"simulateData — simulateData","text":"sim_data one list object, containing patient level simulated data assumed true models. Also providing information simulation iteration, patient number well dosage levels.","code":""}]