diff --git a/404.html b/404.html
index 3f08d88..9138499 100644
--- a/404.html
+++ b/404.html
@@ -7,8 +7,8 @@
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Page not found (404) • structToolbox</title>
 <script src="https://computational-metabolomics.github.io/structToolbox/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="https://computational-metabolomics.github.io/structToolbox/deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet">
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+<link href="https://computational-metabolomics.github.io/structToolbox/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="https://computational-metabolomics.github.io/structToolbox/deps/bootstrap-5.3.1/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">
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 <!-- 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]>
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://computational-metabolomics.github.io/structToolbox/index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index f88fb34..688e1c9 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.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"><title>License • structToolbox</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="License"><!-- 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"><title>License • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/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="License"><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/data_analysis_omics_using_the_structtoolbox.html b/articles/data_analysis_omics_using_the_structtoolbox.html
index bc9c86f..8c521e5 100644
--- a/articles/data_analysis_omics_using_the_structtoolbox.html
+++ b/articles/data_analysis_omics_using_the_structtoolbox.html
@@ -8,8 +8,8 @@
 <meta name="description" content="structToolbox">
 <title>Data analysis of metabolomics and other omics datasets using the structToolbox • structToolbox</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 href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/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="Data analysis of metabolomics and other omics datasets using the structToolbox">
 <meta property="og:description" content="structToolbox">
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -621,7 +621,7 @@ <h3 id="introduction-2">Introduction<a class="anchor" aria-label="anchor" href="
 workflow implemented to process (e.g. filter features, signal drift and
 batch correction, normalise and missing value imputation) mass
 spectrometry data. The workflow exists of methods that are part of the
-Peak Matrix Processing (<em><a href="https://bioconductor.org/packages/3.17/pmp" class="external-link">pmp</a></em>) package,
+Peak Matrix Processing (<em><a href="https://bioconductor.org/packages/3.18/pmp" class="external-link">pmp</a></em>) package,
 including a range of additional filters that are described in Kirwan et
 al., <a href="https://link.springer.com/article/10.1007/s00216-013-6856-7" class="external-link">2013</a>,
 <a href="https://doi.org/10.1038/sdata.2014.12" class="external-link">2014</a>.</p>
@@ -642,7 +642,7 @@ <h3 id="dataset-1">Dataset<a class="anchor" aria-label="anchor" href="#dataset-1
 are available through MetaboLights (<a href="https://www.ebi.ac.uk/metabolights/MTBLS79" class="external-link uri">https://www.ebi.ac.uk/metabolights/MTBLS79</a>).</p>
 <p>The <code>MTBLS79_DatasetExperiment</code> object included in the
 <code>structToolbox</code> package is a processed version of the MTBLS79
-dataset available in peak matrix processing (<em><a href="https://bioconductor.org/packages/3.17/pmp" class="external-link">pmp</a></em>) package.
+dataset available in peak matrix processing (<em><a href="https://bioconductor.org/packages/3.18/pmp" class="external-link">pmp</a></em>) package.
 This vignette describes step by step how the <code>structToolbox</code>
 version was created from the <code>pmp</code> version (i.e. ‘Dataset
 7:SFPM’ from the Scientific Data publication - <a href="https://doi.org/10.1038/sdata.2014.12" class="external-link uri">https://doi.org/10.1038/sdata.2014.12</a>).</p>
@@ -906,7 +906,7 @@ <h3 id="dataset-2">Dataset<a class="anchor" aria-label="anchor" href="#dataset-2
 <p><a href="https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00354" class="external-link">Thevenot
 et al. (2015)</a></p>
 <p>The “Sacurine” dataset needs to be converted to a
-<code>DatasetExperiment</code> object. The <em><a href="https://bioconductor.org/packages/3.17/ropls" class="external-link">ropls</a></em>
+<code>DatasetExperiment</code> object. The <em><a href="https://bioconductor.org/packages/3.18/ropls" class="external-link">ropls</a></em>
 package provides the data as a list containing a
 <code>dataMatrix</code>, <code>sampleMetadata</code> and
 <code>variableMetadata</code>.</p>
@@ -971,7 +971,7 @@ <h3 id="exploratory-data-analysis">Exploratory data analysis<a class="anchor" ar
 <span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/plot_grid.html" class="external-link">plot_grid</a></span><span class="op">(</span>plotlist<span class="op">=</span><span class="va">g</span>, nrow<span class="op">=</span><span class="fl">1</span>, align<span class="op">=</span><span class="st">'vh'</span>, labels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'A'</span>,<span class="st">'B'</span>,<span class="st">'C'</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="data_analysis_omics_using_the_structtoolbox_files/figure-html/unnamed-chunk-40-1.png"><!-- --></p>
 <p>The third plot coloured by gender (C) is identical to Figure 2 of the
-<em><a href="https://bioconductor.org/packages/3.17/ropls" class="external-link">ropls</a></em>
+<em><a href="https://bioconductor.org/packages/3.18/ropls" class="external-link">ropls</a></em>
 package vignette. The <code>structToolbox</code> package provides a
 range of PCA-related diagnostic plots, including D-statistic, scree, and
 loadings plots. These plots can be used to further explore the variance
@@ -1006,7 +1006,7 @@ <h3 id="partial-least-squares-pls-analysis">Partial Least Squares (PLS) analysis
 <span><span class="va">C</span> <span class="op">=</span> <span class="fu"><a href="../reference/pls_scores_plot.html">pls_scores_plot</a></span><span class="op">(</span>factor_name <span class="op">=</span> <span class="st">'gender'</span><span class="op">)</span></span>
 <span><span class="fu"><a href="../reference/chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">M</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <p><img src="data_analysis_omics_using_the_structtoolbox_files/figure-html/unnamed-chunk-42-1.png"><!-- --></p>
-<p>The plot is similar to fig.3 of the <em><a href="https://bioconductor.org/packages/3.17/ropls" class="external-link">ropls</a></em>
+<p>The plot is similar to fig.3 of the <em><a href="https://bioconductor.org/packages/3.18/ropls" class="external-link">ropls</a></em>
 vignette. Differences are due to inverted LV axes, a common occurrence
 with the NIPALS algorithm (used by both <code>structToolbox</code> and
 <code>ropls</code>) which depends on how the algorithm is
@@ -2413,7 +2413,7 @@ <h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#s
 </h2>
 <div class="sourceCode" id="cb127"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## R version 4.3.1 (2023-06-16)</span></span>
+<pre><code><span><span class="co">## R version 4.3.2 (2023-10-31)</span></span>
 <span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span>
 <span><span class="co">## Running under: Ubuntu 22.04.3 LTS</span></span>
 <span><span class="co">## </span></span>
@@ -2434,69 +2434,70 @@ <h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#s
 <span><span class="co">## [1] stats     graphics  grDevices utils     datasets  methods   base     </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## other attached packages:</span></span>
-<span><span class="co">##  [1] openxlsx_4.2.5.2     cowplot_1.1.1        gridExtra_2.3       </span></span>
-<span><span class="co">##  [4] ggplot2_3.4.4        BiocFileCache_2.8.0  dbplyr_2.3.4        </span></span>
-<span><span class="co">##  [7] ropls_1.32.0         pmp_1.12.0           structToolbox_1.13.5</span></span>
-<span><span class="co">## [10] struct_1.12.0        BiocStyle_2.28.1    </span></span>
+<span><span class="co">##  [1] openxlsx_4.2.5.2     cowplot_1.1.3        gridExtra_2.3       </span></span>
+<span><span class="co">##  [4] ggplot2_3.4.4        BiocFileCache_2.10.1 dbplyr_2.4.0        </span></span>
+<span><span class="co">##  [7] ropls_1.34.0         pmp_1.14.0           structToolbox_1.15.1</span></span>
+<span><span class="co">## [10] struct_1.14.0        BiocStyle_2.30.0    </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## loaded via a namespace (and not attached):</span></span>
-<span><span class="co">##   [1] DBI_1.1.3                   bitops_1.0-7               </span></span>
-<span><span class="co">##   [3] MultiDataSet_1.28.0         rlang_1.1.1                </span></span>
-<span><span class="co">##   [5] magrittr_2.0.3              e1071_1.7-13               </span></span>
-<span><span class="co">##   [7] matrixStats_1.0.0           compiler_4.3.1             </span></span>
-<span><span class="co">##   [9] RSQLite_2.3.1               systemfonts_1.0.5          </span></span>
-<span><span class="co">##  [11] vctrs_0.6.4                 reshape2_1.4.4             </span></span>
-<span><span class="co">##  [13] stringr_1.5.0               pkgconfig_2.0.3            </span></span>
+<span><span class="co">##   [1] DBI_1.2.1                   bitops_1.0-7               </span></span>
+<span><span class="co">##   [3] MultiDataSet_1.30.0         rlang_1.1.3                </span></span>
+<span><span class="co">##   [5] magrittr_2.0.3              e1071_1.7-14               </span></span>
+<span><span class="co">##   [7] matrixStats_1.2.0           compiler_4.3.2             </span></span>
+<span><span class="co">##   [9] RSQLite_2.3.5               systemfonts_1.0.5          </span></span>
+<span><span class="co">##  [11] vctrs_0.6.5                 reshape2_1.4.4             </span></span>
+<span><span class="co">##  [13] stringr_1.5.1               pkgconfig_2.0.3            </span></span>
 <span><span class="co">##  [15] crayon_1.5.2                fastmap_1.1.1              </span></span>
-<span><span class="co">##  [17] XVector_0.40.0              labeling_0.4.3             </span></span>
-<span><span class="co">##  [19] utf8_1.2.3                  rmarkdown_2.25             </span></span>
-<span><span class="co">##  [21] itertools_0.1-3             ragg_1.2.6                 </span></span>
+<span><span class="co">##  [17] XVector_0.42.0              labeling_0.4.3             </span></span>
+<span><span class="co">##  [19] utf8_1.2.4                  rmarkdown_2.25             </span></span>
+<span><span class="co">##  [21] itertools_0.1-3             ragg_1.2.7                 </span></span>
 <span><span class="co">##  [23] bit_4.0.5                   purrr_1.0.2                </span></span>
-<span><span class="co">##  [25] xfun_0.40                   MultiAssayExperiment_1.26.0</span></span>
-<span><span class="co">##  [27] randomForest_4.7-1.1        zlibbioc_1.46.0            </span></span>
-<span><span class="co">##  [29] cachem_1.0.8                GenomeInfoDb_1.36.4        </span></span>
-<span><span class="co">##  [31] jsonlite_1.8.7              progress_1.2.2             </span></span>
-<span><span class="co">##  [33] blob_1.2.4                  DelayedArray_0.26.7        </span></span>
-<span><span class="co">##  [35] prettyunits_1.2.0           parallel_4.3.1             </span></span>
-<span><span class="co">##  [37] rols_2.28.0                 R6_2.5.1                   </span></span>
-<span><span class="co">##  [39] bslib_0.5.1                 stringi_1.7.12             </span></span>
-<span><span class="co">##  [41] limma_3.56.2                GenomicRanges_1.52.1       </span></span>
-<span><span class="co">##  [43] jquerylib_0.1.4             Rcpp_1.0.11                </span></span>
-<span><span class="co">##  [45] bookdown_0.35               SummarizedExperiment_1.30.2</span></span>
-<span><span class="co">##  [47] iterators_1.0.14            knitr_1.44                 </span></span>
-<span><span class="co">##  [49] IRanges_2.34.1              Matrix_1.6-1.1             </span></span>
-<span><span class="co">##  [51] tidyselect_1.2.0            abind_1.4-5                </span></span>
-<span><span class="co">##  [53] yaml_2.3.7                  codetools_0.2-19           </span></span>
-<span><span class="co">##  [55] curl_5.1.0                  doRNG_1.8.6                </span></span>
-<span><span class="co">##  [57] lattice_0.21-8              tibble_3.2.1               </span></span>
-<span><span class="co">##  [59] plyr_1.8.9                  withr_2.5.1                </span></span>
-<span><span class="co">##  [61] Biobase_2.60.0              evaluate_0.22              </span></span>
-<span><span class="co">##  [63] ontologyIndex_2.11          desc_1.4.2                 </span></span>
-<span><span class="co">##  [65] isoband_0.2.7               proxy_0.4-27               </span></span>
-<span><span class="co">##  [67] zip_2.3.0                   filelock_1.0.2             </span></span>
-<span><span class="co">##  [69] pillar_1.9.0                BiocManager_1.30.22        </span></span>
-<span><span class="co">##  [71] MatrixGenerics_1.12.3       rngtools_1.5.2             </span></span>
-<span><span class="co">##  [73] foreach_1.5.2               stats4_4.3.1               </span></span>
-<span><span class="co">##  [75] generics_0.1.3              rprojroot_2.0.3            </span></span>
-<span><span class="co">##  [77] sp_2.1-0                    RCurl_1.98-1.12            </span></span>
-<span><span class="co">##  [79] hms_1.1.3                   S4Vectors_0.38.2           </span></span>
-<span><span class="co">##  [81] munsell_0.5.0               scales_1.2.1               </span></span>
+<span><span class="co">##  [25] xfun_0.41                   MultiAssayExperiment_1.28.0</span></span>
+<span><span class="co">##  [27] randomForest_4.7-1.1        zlibbioc_1.48.0            </span></span>
+<span><span class="co">##  [29] cachem_1.0.8                GenomeInfoDb_1.38.5        </span></span>
+<span><span class="co">##  [31] jsonlite_1.8.8              progress_1.2.3             </span></span>
+<span><span class="co">##  [33] blob_1.2.4                  highr_0.10                 </span></span>
+<span><span class="co">##  [35] DelayedArray_0.28.0         prettyunits_1.2.0          </span></span>
+<span><span class="co">##  [37] parallel_4.3.2              rols_2.30.0                </span></span>
+<span><span class="co">##  [39] R6_2.5.1                    bslib_0.6.1                </span></span>
+<span><span class="co">##  [41] stringi_1.8.3               limma_3.58.1               </span></span>
+<span><span class="co">##  [43] GenomicRanges_1.54.1        jquerylib_0.1.4            </span></span>
+<span><span class="co">##  [45] Rcpp_1.0.12                 bookdown_0.37              </span></span>
+<span><span class="co">##  [47] SummarizedExperiment_1.32.0 iterators_1.0.14           </span></span>
+<span><span class="co">##  [49] knitr_1.45                  IRanges_2.36.0             </span></span>
+<span><span class="co">##  [51] Matrix_1.6-1.1              tidyselect_1.2.0           </span></span>
+<span><span class="co">##  [53] abind_1.4-5                 yaml_2.3.8                 </span></span>
+<span><span class="co">##  [55] codetools_0.2-19            curl_5.2.0                 </span></span>
+<span><span class="co">##  [57] doRNG_1.8.6                 lattice_0.21-9             </span></span>
+<span><span class="co">##  [59] tibble_3.2.1                plyr_1.8.9                 </span></span>
+<span><span class="co">##  [61] withr_3.0.0                 Biobase_2.62.0             </span></span>
+<span><span class="co">##  [63] evaluate_0.23               ontologyIndex_2.11         </span></span>
+<span><span class="co">##  [65] desc_1.4.3                  isoband_0.2.7              </span></span>
+<span><span class="co">##  [67] proxy_0.4-27                zip_2.3.1                  </span></span>
+<span><span class="co">##  [69] filelock_1.0.3              pillar_1.9.0               </span></span>
+<span><span class="co">##  [71] BiocManager_1.30.22         MatrixGenerics_1.14.0      </span></span>
+<span><span class="co">##  [73] rngtools_1.5.2              foreach_1.5.2              </span></span>
+<span><span class="co">##  [75] stats4_4.3.2                generics_0.1.3             </span></span>
+<span><span class="co">##  [77] sp_2.1-2                    RCurl_1.98-1.14            </span></span>
+<span><span class="co">##  [79] hms_1.1.3                   S4Vectors_0.40.2           </span></span>
+<span><span class="co">##  [81] munsell_0.5.0               scales_1.3.0               </span></span>
 <span><span class="co">##  [83] calibrate_1.7.7             class_7.3-22               </span></span>
-<span><span class="co">##  [85] glue_1.6.2                  tools_4.3.1                </span></span>
-<span><span class="co">##  [87] fs_1.6.3                    grid_4.3.1                 </span></span>
-<span><span class="co">##  [89] impute_1.74.1               missForest_1.5             </span></span>
-<span><span class="co">##  [91] colorspace_2.1-0            GenomeInfoDbData_1.2.10    </span></span>
-<span><span class="co">##  [93] cli_3.6.1                   textshaping_0.3.7          </span></span>
-<span><span class="co">##  [95] fansi_1.0.5                 viridisLite_0.4.2          </span></span>
-<span><span class="co">##  [97] ggthemes_4.2.4              S4Arrays_1.0.6             </span></span>
-<span><span class="co">##  [99] dplyr_1.1.3                 pls_2.8-2                  </span></span>
-<span><span class="co">## [101] pcaMethods_1.92.0           gtable_0.3.4               </span></span>
-<span><span class="co">## [103] sass_0.4.7                  digest_0.6.33              </span></span>
-<span><span class="co">## [105] BiocGenerics_0.46.0         farver_2.1.1               </span></span>
-<span><span class="co">## [107] memoise_2.0.1               htmltools_0.5.6.1          </span></span>
-<span><span class="co">## [109] pkgdown_2.0.7               lifecycle_1.0.3            </span></span>
-<span><span class="co">## [111] httr_1.4.7                  qqman_0.1.9                </span></span>
-<span><span class="co">## [113] bit64_4.0.5                 MASS_7.3-60</span></span></code></pre>
+<span><span class="co">##  [85] glue_1.7.0                  tools_4.3.2                </span></span>
+<span><span class="co">##  [87] fs_1.6.3                    grid_4.3.2                 </span></span>
+<span><span class="co">##  [89] impute_1.76.0               missForest_1.5             </span></span>
+<span><span class="co">##  [91] colorspace_2.1-0            GenomeInfoDbData_1.2.11    </span></span>
+<span><span class="co">##  [93] cli_3.6.2                   textshaping_0.3.7          </span></span>
+<span><span class="co">##  [95] fansi_1.0.6                 viridisLite_0.4.2          </span></span>
+<span><span class="co">##  [97] ggthemes_5.0.0              S4Arrays_1.2.0             </span></span>
+<span><span class="co">##  [99] dplyr_1.1.4                 pls_2.8-3                  </span></span>
+<span><span class="co">## [101] pcaMethods_1.94.0           gtable_0.3.4               </span></span>
+<span><span class="co">## [103] sass_0.4.8                  digest_0.6.34              </span></span>
+<span><span class="co">## [105] BiocGenerics_0.48.1         SparseArray_1.2.3          </span></span>
+<span><span class="co">## [107] farver_2.1.1                memoise_2.0.1              </span></span>
+<span><span class="co">## [109] htmltools_0.5.7             pkgdown_2.0.7              </span></span>
+<span><span class="co">## [111] lifecycle_1.0.4             httr_1.4.7                 </span></span>
+<span><span class="co">## [113] statmod_1.5.0               bit64_4.0.5                </span></span>
+<span><span class="co">## [115] qqman_0.1.9                 MASS_7.3-60</span></span></code></pre>
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@@ -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"><title>Articles • structToolbox</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="Articles"><!-- 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"><title>Articles • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Articles"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index 3f752a4..e35bc1e 100644
--- a/authors.html
+++ b/authors.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"><title>Authors and Citation • structToolbox</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="Authors and Citation"><!-- 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"><title>Authors and Citation • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/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="Authors and Citation"><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/deps/bootstrap-5.3.1/bootstrap.bundle.min.js b/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
new file mode 100644
index 0000000..e8f21f7
--- /dev/null
+++ b/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
@@ -0,0 +1,7 @@
+/*!
+  * Bootstrap v5.3.1 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
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t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),"fixed"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof 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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t 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t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div 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t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends 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e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let 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"${t}"`);e[t]()}}))}}N.on(document,Ls,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||Vs.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(qs))Vs.getOrCreateInstance(t)})),m(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return 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diff --git a/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map b/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map
new file mode 100644
index 0000000..3863da8
--- /dev/null
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Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n      return\n    }\n\n    instanceMap.set(key, instance)\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null\n    }\n\n    return null\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return\n    }\n\n    const instanceMap = elementMap.get(element)\n\n    instanceMap.delete(key)\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element)\n    }\n  }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1_000_000\nconst MILLISECONDS_MULTIPLIER = 1000\nconst TRANSITION_END = 'transitionend'\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`)\n  }\n\n  return selector\n}\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`\n  }\n\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase()\n}\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID)\n  } while (document.getElementById(prefix))\n\n  return prefix\n}\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0\n  }\n\n  // Get transition-duration of the element\n  let { transitionDuration, transitionDelay } = window.getComputedStyle(element)\n\n  const floatTransitionDuration = Number.parseFloat(transitionDuration)\n  const floatTransitionDelay = Number.parseFloat(transitionDelay)\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0]\n  transitionDelay = transitionDelay.split(',')[0]\n\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER\n}\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END))\n}\n\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false\n  }\n\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0]\n  }\n\n  return typeof object.nodeType !== 'undefined'\n}\n\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object))\n  }\n\n  return null\n}\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])')\n\n  if (!closedDetails) {\n    return elementIsVisible\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary')\n    if (summary && summary.parentNode !== closedDetails) {\n      return false\n    }\n\n    if (summary === null) {\n      return false\n    }\n  }\n\n  return elementIsVisible\n}\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode()\n    return root instanceof ShadowRoot ? root : null\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null\n  }\n\n  return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery\n  }\n\n  return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback()\n        }\n      })\n    }\n\n    DOMContentLoadedCallbacks.push(callback)\n  } else {\n    callback()\n  }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery()\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME\n      const JQUERY_NO_CONFLICT = $.fn[name]\n      $.fn[name] = plugin.jQueryInterface\n      $.fn[name].Constructor = plugin\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT\n        return plugin.jQueryInterface\n      }\n    }\n  })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback)\n    return\n  }\n\n  const durationPadding = 5\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n  let called = false\n\n  const handler = ({ target }) => {\n    if (target !== transitionElement) {\n      return\n    }\n\n    called = true\n    transitionElement.removeEventListener(TRANSITION_END, handler)\n    execute(callback)\n  }\n\n  transitionElement.addEventListener(TRANSITION_END, handler)\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement)\n    }\n  }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length\n  let index = list.indexOf(activeElement)\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n  }\n\n  index += shouldGetNext ? 1 : -1\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n  defineJQueryPlugin,\n  execute,\n  executeAfterTransition,\n  findShadowRoot,\n  getElement,\n  getjQuery,\n  getNextActiveElement,\n  getTransitionDurationFromElement,\n  getUID,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop,\n  onDOMContentLoaded,\n  parseSelector,\n  reflow,\n  triggerTransitionEnd,\n  toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n  'click',\n  'dblclick',\n  'mouseup',\n  'mousedown',\n  'contextmenu',\n  'mousewheel',\n  'DOMMouseScroll',\n  'mouseover',\n  'mouseout',\n  'mousemove',\n  'selectstart',\n  'selectend',\n  'keydown',\n  'keypress',\n  'keyup',\n  'orientationchange',\n  'touchstart',\n  'touchmove',\n  'touchend',\n  'touchcancel',\n  'pointerdown',\n  'pointermove',\n  'pointerup',\n  'pointerleave',\n  'pointercancel',\n  'gesturestart',\n  'gesturechange',\n  'gestureend',\n  'focus',\n  'blur',\n  'change',\n  'reset',\n  'select',\n  'submit',\n  'focusin',\n  'focusout',\n  'load',\n  'unload',\n  'beforeunload',\n  'resize',\n  'move',\n  'DOMContentLoaded',\n  'readystatechange',\n  'error',\n  'abort',\n  'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element)\n\n  element.uidEvent = uid\n  eventRegistry[uid] = eventRegistry[uid] || {}\n\n  return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, { delegateTarget: element })\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn)\n    }\n\n    return fn.apply(element, [event])\n  }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector)\n\n    for (let { target } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue\n        }\n\n        hydrateObj(event, { delegateTarget: target })\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn)\n        }\n\n        return fn.apply(target, [event])\n      }\n    }\n  }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events)\n    .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n  let typeEvent = getTypeEvent(originalTypeEvent)\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent\n  }\n\n  return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n          return fn.call(this, event)\n        }\n      }\n    }\n\n    callable = wrapFunction(callable)\n  }\n\n  const events = getElementEvents(element)\n  const handlers = events[typeEvent] || (events[typeEvent] = {})\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n    return\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n  const fn = isDelegated ?\n    bootstrapDelegationHandler(element, handler, callable) :\n    bootstrapHandler(element, callable)\n\n  fn.delegationSelector = isDelegated ? handler : null\n  fn.callable = callable\n  fn.oneOff = oneOff\n  fn.uidEvent = uid\n  handlers[uid] = fn\n\n  element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n  if (!fn) {\n    return\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n  delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {}\n\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '')\n  return customEvents[event] || event\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false)\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true)\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n    const inNamespace = typeEvent !== originalTypeEvent\n    const events = getElementEvents(element)\n    const storeElementEvent = events[typeEvent] || {}\n    const isNamespace = originalTypeEvent.startsWith('.')\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n      return\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n      }\n    }\n\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null\n    }\n\n    const $ = getjQuery()\n    const typeEvent = getTypeEvent(event)\n    const inNamespace = event !== typeEvent\n\n    let jQueryEvent = null\n    let bubbles = true\n    let nativeDispatch = true\n    let defaultPrevented = false\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args)\n\n      $(element).trigger(jQueryEvent)\n      bubbles = !jQueryEvent.isPropagationStopped()\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n      defaultPrevented = jQueryEvent.isDefaultPrevented()\n    }\n\n    const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n    if (defaultPrevented) {\n      evt.preventDefault()\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt)\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault()\n    }\n\n    return evt\n  }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value\n    } catch {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value\n        }\n      })\n    }\n  }\n\n  return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true\n  }\n\n  if (value === 'false') {\n    return false\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value)\n  }\n\n  if (value === '' || value === 'null') {\n    return null\n  }\n\n  if (typeof value !== 'string') {\n    return value\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value))\n  } catch {\n    return value\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {}\n    }\n\n    const attributes = {}\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '')\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n      attributes[pureKey] = normalizeData(element.dataset[key])\n    }\n\n    return attributes\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n  }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {}\n  }\n\n  static get DefaultType() {\n    return {}\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!')\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    return config\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    }\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property]\n      const valueType = isElement(value) ? 'element' : toType(value)\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(\n          `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n        )\n      }\n    }\n  }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super()\n\n    element = getElement(element)\n    if (!element) {\n      return\n    }\n\n    this._element = element\n    this._config = this._getConfig(config)\n\n    Data.set(this._element, this.constructor.DATA_KEY, this)\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY)\n    EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated)\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY)\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n  }\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`\n  }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target')\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href')\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n      return null\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n  }\n\n  return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector)\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector))\n  },\n\n  parents(element, selector) {\n    const parents = []\n    let ancestor = element.parentNode.closest(selector)\n\n    while (ancestor) {\n      parents.push(ancestor)\n      ancestor = ancestor.parentNode.closest(selector)\n    }\n\n    return parents\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous]\n      }\n\n      previous = previous.previousElementSibling\n    }\n\n    return []\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next]\n      }\n\n      next = next.nextElementSibling\n    }\n\n    return []\n  },\n\n  focusableChildren(element) {\n    const focusables = [\n      'a',\n      'button',\n      'input',\n      'textarea',\n      'select',\n      'details',\n      '[tabindex]',\n      '[contenteditable=\"true\"]'\n    ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n  },\n\n  getSelectorFromElement(element) {\n    const selector = getSelector(element)\n\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null\n    }\n\n    return null\n  },\n\n  getElementFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.findOne(selector) : null\n  },\n\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.find(selector) : []\n  }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`\n  const name = component.NAME\n\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault()\n    }\n\n    if (isDisabled(this)) {\n      return\n    }\n\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n    const instance = component.getOrCreateInstance(target)\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]()\n  })\n}\n\nexport {\n  enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n    if (closeEvent.defaultPrevented) {\n      return\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove()\n    EventHandler.trigger(this._element, EVENT_CLOSED)\n    this.dispose()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this)\n\n      if (config === 'toggle') {\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n  event.preventDefault()\n\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n  const data = Button.getOrCreateInstance(button)\n\n  data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n}\n\nconst DefaultType = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super()\n    this._element = element\n\n    if (!element || !Swipe.isSupported()) {\n      return\n    }\n\n    this._config = this._getConfig(config)\n    this._deltaX = 0\n    this._supportPointerEvents = Boolean(window.PointerEvent)\n    this._initEvents()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY)\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX\n\n      return\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX\n    }\n\n    this._handleSwipe()\n    execute(this._config.endCallback)\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ?\n      0 :\n      event.touches[0].clientX - this._deltaX\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX)\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return\n    }\n\n    const direction = absDeltaX / this._deltaX\n\n    this._deltaX = 0\n\n    if (!direction) {\n      return\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n  }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getNextActiveElement,\n  isRTL,\n  isVisible,\n  reflow,\n  triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n}\n\nconst DefaultType = {\n  interval: '(number|boolean)', // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._interval = null\n    this._activeElement = null\n    this._isSliding = false\n    this.touchTimeout = null\n    this._swipeHelper = null\n\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n    this._addEventListeners()\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT)\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next()\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV)\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element)\n    }\n\n    this._clearInterval()\n  }\n\n  cycle() {\n    this._clearInterval()\n    this._updateInterval()\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n      return\n    }\n\n    this.cycle()\n  }\n\n  to(index) {\n    const items = this._getItems()\n    if (index > items.length - 1 || index < 0) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n      return\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive())\n    if (activeIndex === index) {\n      return\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n    this._slide(order, items[index])\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose()\n    }\n\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval\n    return config\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n      EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners()\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause()\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout)\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n    }\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    }\n\n    this._swipeHelper = new Swipe(this._element, swipeConfig)\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key]\n    if (direction) {\n      event.preventDefault()\n      this._slide(this._directionToOrder(direction))\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element)\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n    activeIndicator.removeAttribute('aria-current')\n\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n      newActiveIndicator.setAttribute('aria-current', 'true')\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive()\n\n    if (!element) {\n      return\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n    this._config.interval = elementInterval || this._config.defaultInterval\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return\n    }\n\n    const activeElement = this._getActive()\n    const isNext = order === ORDER_NEXT\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n    if (nextElement === activeElement) {\n      return\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement)\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      })\n    }\n\n    const slideEvent = triggerEvent(EVENT_SLIDE)\n\n    if (slideEvent.defaultPrevented) {\n      return\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return\n    }\n\n    const isCycling = Boolean(this._interval)\n    this.pause()\n\n    this._isSliding = true\n\n    this._setActiveIndicatorElement(nextElementIndex)\n    this._activeElement = nextElement\n\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n    nextElement.classList.add(orderClassName)\n\n    reflow(nextElement)\n\n    activeElement.classList.add(directionalClassName)\n    nextElement.classList.add(directionalClassName)\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName)\n      nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n      activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n      this._isSliding = false\n\n      triggerEvent(EVENT_SLID)\n    }\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n    if (isCycling) {\n      this.cycle()\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE)\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element)\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval)\n      this._interval = null\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config)\n\n      if (typeof config === 'number') {\n        data.to(config)\n        return\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return\n  }\n\n  event.preventDefault()\n\n  const carousel = Carousel.getOrCreateInstance(target)\n  const slideIndex = this.getAttribute('data-bs-slide-to')\n\n  if (slideIndex) {\n    carousel.to(slideIndex)\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next()\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  carousel.prev()\n  carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getElement,\n  reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n  parent: null,\n  toggle: true\n}\n\nconst DefaultType = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isTransitioning = false\n    this._triggerArray = []\n\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem)\n      const filterElement = SelectorEngine.find(selector)\n        .filter(foundElement => foundElement === this._element)\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem)\n      }\n    }\n\n    this._initializeChildren()\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n    }\n\n    if (this._config.toggle) {\n      this.toggle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide()\n    } else {\n      this.show()\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return\n    }\n\n    let activeChildren = []\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n        .filter(element => element !== this._element)\n        .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide()\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE)\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n    this._element.style[dimension] = 0\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true)\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n      this._element.style[dimension] = ''\n\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n    }\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n    const scrollSize = `scroll${capitalizedDimension}`\n\n    this._queueCallback(complete, this._element, true)\n    this._element.style[dimension] = `${this._element[scrollSize]}px`\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger)\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false)\n      }\n    }\n\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.style[dimension] = ''\n\n    this._queueCallback(complete, this._element, true)\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle) // Coerce string values\n    config.parent = getElement(config.parent)\n    return config\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element)\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected))\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n      element.setAttribute('aria-expanded', isOpen)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {}\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n    event.preventDefault()\n  }\n\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  execute,\n  getElement,\n  getNextActiveElement,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n}\n\nconst DefaultType = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._popper = null\n    this._parent = this._element.parentNode // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n    this._inNavbar = this._detectNavbar()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show()\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._createPopper()\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    this._element.focus()\n    this._element.setAttribute('aria-expanded', true)\n\n    this._menu.classList.add(CLASS_NAME_SHOW)\n    this._element.classList.add(CLASS_NAME_SHOW)\n    EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    this._completeHide(relatedTarget)\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    super.dispose()\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar()\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW)\n    this._element.classList.remove(CLASS_NAME_SHOW)\n    this._element.setAttribute('aria-expanded', 'false')\n    Manipulator.removeDataAttribute(this._menu, 'popper')\n    EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config)\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) &&\n      typeof config.reference.getBoundingClientRect !== 'function'\n    ) {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n    }\n\n    return config\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n    }\n\n    let referenceElement = this._element\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference)\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference\n    }\n\n    const popperConfig = this._getPopperConfig()\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      },\n      {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _selectMenuItem({ key, target }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n    if (!items.length) {\n      return\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n      return\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle)\n      if (!context || context._config.autoClose === false) {\n        continue\n      }\n\n      const composedPath = event.composedPath()\n      const isMenuTarget = composedPath.includes(context._menu)\n      if (\n        composedPath.includes(context._element) ||\n        (context._config.autoClose === 'inside' && !isMenuTarget) ||\n        (context._config.autoClose === 'outside' && isMenuTarget)\n      ) {\n        continue\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue\n      }\n\n      const relatedTarget = { relatedTarget: context._element }\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event\n      }\n\n      context._completeHide(relatedTarget)\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName)\n    const isEscapeEvent = event.key === ESCAPE_KEY\n    const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return\n    }\n\n    event.preventDefault()\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n      this :\n      (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n    const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation()\n      instance.show()\n      instance._selectMenuItem(event)\n      return\n    }\n\n    if (instance._isShown()) { // else is escape and we check if it is shown\n      event.stopPropagation()\n      instance.hide()\n      getToggleButton.focus()\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  event.preventDefault()\n  Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isAppended = false\n    this._element = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._append()\n\n    const element = this._getElement()\n    if (this._config.isAnimated) {\n      reflow(element)\n    }\n\n    element.classList.add(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      execute(callback)\n    })\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      this.dispose()\n      execute(callback)\n    })\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n    this._element.remove()\n    this._isAppended = false\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div')\n      backdrop.className = this._config.className\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE)\n      }\n\n      this._element = backdrop\n    }\n\n    return this._element\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement)\n    return config\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return\n    }\n\n    const element = this._getElement()\n    this._config.rootElement.append(element)\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback)\n    })\n\n    this._isAppended = true\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n  }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isActive = false\n    this._lastTabNavDirection = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus()\n    }\n\n    EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n    this._isActive = true\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return\n    }\n\n    this._isActive = false\n    EventHandler.off(document, EVENT_KEY)\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const { trapElement } = this._config\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement)\n\n    if (elements.length === 0) {\n      trapElement.focus()\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus()\n    } else {\n      elements[0].focus()\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n  }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth\n    return Math.abs(window.innerWidth - documentWidth)\n  }\n\n  hide() {\n    const width = this.getWidth()\n    this._disableOverFlow()\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow')\n    this._resetElementAttributes(this._element, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow')\n    this._element.style.overflow = 'hidden'\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth()\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return\n      }\n\n      this._saveInitialAttribute(element, styleProperty)\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty)\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue)\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty)\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty)\n        return\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty)\n      element.style.setProperty(styleProperty, value)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector)\n      return\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel)\n    }\n  }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._isShown = false\n    this._isTransitioning = false\n    this._scrollBar = new ScrollBarHelper()\n\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n      relatedTarget\n    })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._isTransitioning = true\n\n    this._scrollBar.hide()\n\n    document.body.classList.add(CLASS_NAME_OPEN)\n\n    this._adjustDialog()\n\n    this._backdrop.show(() => this._showElement(relatedTarget))\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = false\n    this._isTransitioning = true\n    this._focustrap.deactivate()\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n  }\n\n  dispose() {\n    EventHandler.off(window, EVENT_KEY)\n    EventHandler.off(this._dialog, EVENT_KEY)\n\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n\n    super.dispose()\n  }\n\n  handleUpdate() {\n    this._adjustDialog()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element)\n    }\n\n    this._element.style.display = 'block'\n    this._element.removeAttribute('aria-hidden')\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.scrollTop = 0\n\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n    if (modalBody) {\n      modalBody.scrollTop = 0\n    }\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_SHOW)\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate()\n      }\n\n      this._isTransitioning = false\n      EventHandler.trigger(this._element, EVENT_SHOWN, {\n        relatedTarget\n      })\n    }\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      this._triggerBackdropTransition()\n    })\n\n    EventHandler.on(window, EVENT_RESIZE, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog()\n      }\n    })\n\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition()\n          return\n        }\n\n        if (this._config.backdrop) {\n          this.hide()\n        }\n      })\n    })\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none'\n    this._element.setAttribute('aria-hidden', true)\n    this._element.removeAttribute('aria-modal')\n    this._element.removeAttribute('role')\n    this._isTransitioning = false\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN)\n      this._resetAdjustments()\n      this._scrollBar.reset()\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    })\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE)\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const initialOverflowY = this._element.style.overflowY\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden'\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC)\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC)\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY\n      }, this._dialog)\n    }, this._dialog)\n\n    this._element.focus()\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const scrollbarWidth = this._scrollBar.getWidth()\n    const isBodyOverflowing = scrollbarWidth > 0\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = ''\n    this._element.style.paddingRight = ''\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](relatedTarget)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  EventHandler.one(target, EVENT_SHOW, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN, () => {\n      if (isVisible(this)) {\n        this.focus()\n      }\n    })\n  })\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Modal.getOrCreateInstance(target)\n\n  data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n  defineJQueryPlugin,\n  isDisabled,\n  isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isShown = false\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._backdrop.show()\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide()\n    }\n\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.classList.add(CLASS_NAME_SHOWING)\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate()\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW)\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n    }\n\n    this._queueCallback(completeCallBack, this._element, true)\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._focustrap.deactivate()\n    this._element.blur()\n    this._isShown = false\n    this._element.classList.add(CLASS_NAME_HIDING)\n    this._backdrop.hide()\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n      this._element.removeAttribute('aria-modal')\n      this._element.removeAttribute('role')\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset()\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._queueCallback(completeCallback, this._element, true)\n  }\n\n  dispose() {\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n    super.dispose()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n        return\n      }\n\n      this.hide()\n    }\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop)\n\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    })\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus()\n    }\n  })\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target)\n  data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show()\n  }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide()\n    }\n  }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n  'background',\n  'cite',\n  'href',\n  'itemtype',\n  'longdesc',\n  'poster',\n  'src',\n  'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase()\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n    }\n\n    return true\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n    .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml)\n  }\n\n  const domParser = new window.DOMParser()\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase()\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove()\n      continue\n    }\n\n    const attributeList = [].concat(...element.attributes)\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName)\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  content: {}, // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n}\n\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content)\n      .map(config => this._resolvePossibleFunction(config))\n      .filter(Boolean)\n  }\n\n  hasContent() {\n    return this.getContent().length > 0\n  }\n\n  changeContent(content) {\n    this._checkContent(content)\n    this._config.content = { ...this._config.content, ...content }\n    return this\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div')\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector)\n    }\n\n    const template = templateWrapper.children[0]\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '))\n    }\n\n    return template\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config)\n    this._checkContent(config.content)\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template)\n\n    if (!templateElement) {\n      return\n    }\n\n    content = this._resolvePossibleFunction(content)\n\n    if (!content) {\n      templateElement.remove()\n      return\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement)\n      return\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content)\n      return\n    }\n\n    templateElement.textContent = content\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this])\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = ''\n      templateElement.append(element)\n      return\n    }\n\n    templateElement.textContent = element.textContent\n  }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' +\n            '<div class=\"tooltip-arrow\"></div>' +\n            '<div class=\"tooltip-inner\"></div>' +\n            '</div>',\n  title: '',\n  trigger: 'hover focus'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n    }\n\n    super(element, config)\n\n    // Private\n    this._isEnabled = true\n    this._timeout = 0\n    this._isHovered = null\n    this._activeTrigger = {}\n    this._popper = null\n    this._templateFactory = null\n    this._newContent = null\n\n    // Protected\n    this.tip = null\n\n    this._setListeners()\n\n    if (!this._config.selector) {\n      this._fixTitle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true\n  }\n\n  disable() {\n    this._isEnabled = false\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click\n    if (this._isShown()) {\n      this._leave()\n      return\n    }\n\n    this._enter()\n  }\n\n  dispose() {\n    clearTimeout(this._timeout)\n\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n    }\n\n    this._disposePopper()\n    super.dispose()\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements')\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n    const shadowRoot = findShadowRoot(this._element)\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper()\n\n    const tip = this._getTipElement()\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n    const { container } = this._config\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip)\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n    }\n\n    this._popper = this._createPopper(tip)\n\n    tip.classList.add(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n      if (this._isHovered === false) {\n        this._leave()\n      }\n\n      this._isHovered = false\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const tip = this._getTipElement()\n    tip.classList.remove(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false\n    this._activeTrigger[TRIGGER_FOCUS] = false\n    this._activeTrigger[TRIGGER_HOVER] = false\n    this._isHovered = null // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper()\n      }\n\n      this._element.removeAttribute('aria-describedby')\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle())\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n    }\n\n    return this.tip\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml()\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n    const tipId = getUID(this.constructor.NAME).toString()\n\n    tip.setAttribute('id', tipId)\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE)\n    }\n\n    return tip\n  }\n\n  setContent(content) {\n    this._newContent = content\n    if (this._isShown()) {\n      this._disposePopper()\n      this.show()\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content)\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      })\n    }\n\n    return this._templateFactory\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    }\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n  }\n\n  _isAnimated() {\n    return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element])\n    const attachment = AttachmentMap[placement.toUpperCase()]\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element])\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [\n        {\n          name: 'flip',\n          options: {\n            fallbackPlacements: this._config.fallbackPlacements\n          }\n        },\n        {\n          name: 'offset',\n          options: {\n            offset: this._getOffset()\n          }\n        },\n        {\n          name: 'preventOverflow',\n          options: {\n            boundary: this._config.boundary\n          }\n        },\n        {\n          name: 'arrow',\n          options: {\n            element: `.${this.constructor.NAME}-arrow`\n          }\n        },\n        {\n          name: 'preSetPlacement',\n          enabled: true,\n          phase: 'beforeMain',\n          fn: data => {\n            // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n            // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n            this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n          }\n        }\n      ]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ')\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context.toggle()\n        })\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSEENTER) :\n          this.constructor.eventName(EVENT_FOCUSIN)\n        const eventOut = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSELEAVE) :\n          this.constructor.eventName(EVENT_FOCUSOUT)\n\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n          context._enter()\n        })\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n            context._element.contains(event.relatedTarget)\n\n          context._leave()\n        })\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide()\n      }\n    }\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title')\n\n    if (!title) {\n      return\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title)\n    }\n\n    this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title')\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true\n      return\n    }\n\n    this._isHovered = true\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show()\n      }\n    }, this._config.delay.show)\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return\n    }\n\n    this._isHovered = false\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide()\n      }\n    }, this._config.delay.hide)\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout)\n    this._timeout = setTimeout(handler, timeout)\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true)\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute]\n      }\n    }\n\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    }\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container)\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      }\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString()\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString()\n    }\n\n    return config\n  }\n\n  _getDelegateConfig() {\n    const config = {}\n\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value\n      }\n    }\n\n    config.selector = false\n    config.trigger = 'manual'\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy()\n      this._popper = null\n    }\n\n    if (this.tip) {\n      this.tip.remove()\n      this.tip = null\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' +\n    '<div class=\"popover-arrow\"></div>' +\n    '<h3 class=\"popover-header\"></h3>' +\n    '<div class=\"popover-body\"></div>' +\n    '</div>',\n  trigger: 'click'\n}\n\nconst DefaultType = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent()\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    }\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content)\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n  offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n  offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n    this._activeTarget = null\n    this._observer = null\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    }\n    this.refresh() // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables()\n    this._maybeEnableSmoothScroll()\n\n    if (this._observer) {\n      this._observer.disconnect()\n    } else {\n      this._observer = this._getNewObserver()\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section)\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect()\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n    }\n\n    return config\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK)\n\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash)\n      if (observableSection) {\n        event.preventDefault()\n        const root = this._rootElement || window\n        const height = observableSection.offsetTop - this._element.offsetTop\n        if (root.scrollTo) {\n          root.scrollTo({ top: height, behavior: 'smooth' })\n          return\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height\n      }\n    })\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    }\n\n    return new IntersectionObserver(entries => this._observerCallback(entries), options)\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n      this._process(targetElement(entry))\n    }\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n    this._previousScrollData.parentScrollTop = parentScrollTop\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null\n        this._clearActiveClass(targetElement(entry))\n\n        continue\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry)\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return\n        }\n\n        continue\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry)\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue\n      }\n\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor)\n        this._observableSections.set(anchor.hash, observableSection)\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return\n    }\n\n    this._clearActiveClass(this._config.target)\n    this._activeTarget = target\n    target.classList.add(CLASS_NAME_ACTIVE)\n    this._activateParents(target)\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n        .classList.add(CLASS_NAME_ACTIVE)\n      return\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE)\n      }\n    }\n  }\n\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE)\n\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE}`, parent)\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy)\n\nexport default ScrollSpy\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getNextActiveElement, isDisabled } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tab'\nconst DATA_KEY = 'bs.tab'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}`\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst HOME_KEY = 'Home'\nconst END_KEY = 'End'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_DROPDOWN = 'dropdown'\n\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu'\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = ':not(.dropdown-toggle)'\n\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]'\nconst SELECTOR_OUTER = '.nav-item, .list-group-item'\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]' // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`\n\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element)\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL)\n\n    if (!this._parent) {\n      return\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren())\n\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() { // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element\n    if (this._elemIsActive(innerElem)) {\n      return\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem()\n\n    const hideEvent = active ?\n      EventHandler.trigger(active, EVENT_HIDE, { relatedTarget: innerElem }) :\n      null\n\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW, { relatedTarget: active })\n\n    if (showEvent.defaultPrevented || (hideEvent && hideEvent.defaultPrevented)) {\n      return\n    }\n\n    this._deactivate(active, innerElem)\n    this._activate(innerElem, active)\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE)\n\n    this._activate(SelectorEngine.getElementFromSelector(element)) // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.removeAttribute('tabindex')\n      element.setAttribute('aria-selected', true)\n      this._toggleDropDown(element, true)\n      EventHandler.trigger(element, EVENT_SHOWN, {\n        relatedTarget: relatedElem\n      })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.remove(CLASS_NAME_ACTIVE)\n    element.blur()\n\n    this._deactivate(SelectorEngine.getElementFromSelector(element)) // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.setAttribute('aria-selected', false)\n      element.setAttribute('tabindex', '-1')\n      this._toggleDropDown(element, false)\n      EventHandler.trigger(element, EVENT_HIDDEN, { relatedTarget: relatedElem })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _keydown(event) {\n    if (!([ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key))) {\n      return\n    }\n\n    event.stopPropagation()// stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault()\n\n    const children = this._getChildren().filter(element => !isDisabled(element))\n    let nextActiveElement\n\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1]\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key)\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true)\n    }\n\n    if (nextActiveElement) {\n      nextActiveElement.focus({ preventScroll: true })\n      Tab.getOrCreateInstance(nextActiveElement).show()\n    }\n  }\n\n  _getChildren() { // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent)\n  }\n\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null\n  }\n\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist')\n\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child)\n    }\n  }\n\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child)\n    const isActive = this._elemIsActive(child)\n    const outerElem = this._getOuterElement(child)\n    child.setAttribute('aria-selected', isActive)\n\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation')\n    }\n\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1')\n    }\n\n    this._setAttributeIfNotExists(child, 'role', 'tab')\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child)\n  }\n\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child)\n\n    if (!target) {\n      return\n    }\n\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel')\n\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`)\n    }\n  }\n\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element)\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return\n    }\n\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem)\n      if (element) {\n        element.classList.toggle(className, open)\n      }\n    }\n\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE)\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW)\n    outerElem.setAttribute('aria-expanded', open)\n  }\n\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value)\n    }\n  }\n\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE)\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem)\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  Tab.getOrCreateInstance(this).show()\n})\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element)\n  }\n})\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab)\n\nexport default Tab\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin, reflow } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'toast'\nconst DATA_KEY = 'bs.toast'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_HIDE = 'hide' // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\n\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n}\n\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n}\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._timeout = null\n    this._hasMouseInteraction = false\n    this._hasKeyboardInteraction = false\n    this._setListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._clearTimeout()\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE)\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n\n      this._maybeScheduleHide()\n    }\n\n    this._element.classList.remove(CLASS_NAME_HIDE) // @deprecated\n    reflow(this._element)\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING)\n\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  hide() {\n    if (!this.isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE) // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.classList.add(CLASS_NAME_SHOWING)\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  dispose() {\n    this._clearTimeout()\n\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW)\n    }\n\n    super.dispose()\n  }\n\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide()\n    }, this._config.delay)\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout': {\n        this._hasMouseInteraction = isInteracting\n        break\n      }\n\n      case 'focusin':\n      case 'focusout': {\n        this._hasKeyboardInteraction = isInteracting\n        break\n      }\n\n      default: {\n        break\n      }\n    }\n\n    if (isInteracting) {\n      this._clearTimeout()\n      return\n    }\n\n    const nextElement = event.relatedTarget\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return\n    }\n\n    this._maybeScheduleHide()\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false))\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false))\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout)\n    this._timeout = null\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config](this)\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast)\n\nexport default Toast\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap index.umd.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Alert from './src/alert.js'\nimport Button from './src/button.js'\nimport Carousel from './src/carousel.js'\nimport Collapse from './src/collapse.js'\nimport Dropdown from './src/dropdown.js'\nimport Modal from './src/modal.js'\nimport Offcanvas from './src/offcanvas.js'\nimport Popover from './src/popover.js'\nimport ScrollSpy from './src/scrollspy.js'\nimport Tab from './src/tab.js'\nimport Toast from './src/toast.js'\nimport Tooltip from 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\ No newline at end of file
diff --git a/deps/bootstrap-5.3.1/bootstrap.min.css b/deps/bootstrap-5.3.1/bootstrap.min.css
new file mode 100644
index 0000000..740f5fd
--- /dev/null
+++ b/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -0,0 +1,5 @@
+@import url("font.css");:root{--bslib-bootstrap-version: 5;--bslib-preset-name: flatly;--bslib-preset-type: bootswatch}/*!
+   * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+   * Copyright 2011-2023 The Bootstrap Authors
+   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+   */:root,[data-bs-theme="light"]{--bs-blue: #2c3e50;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #e74c3c;--bs-orange: #fd7e14;--bs-yellow: #f39c12;--bs-green: #18bc9c;--bs-teal: #20c997;--bs-cyan: #3498db;--bs-black: #000;--bs-white: #fff;--bs-gray: #95a5a6;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #ecf0f1;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #b4bcc2;--bs-gray-600: #95a5a6;--bs-gray-700: #7b8a8b;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #95a5a6;--bs-primary: #2c3e50;--bs-secondary: #95a5a6;--bs-success: #18bc9c;--bs-info: #3498db;--bs-warning: #f39c12;--bs-danger: #e74c3c;--bs-light: #ecf0f1;--bs-dark: #7b8a8b;--bs-default-rgb: 149,165,166;--bs-primary-rgb: 44,62,80;--bs-secondary-rgb: 149,165,166;--bs-success-rgb: 24,188,156;--bs-info-rgb: 52,152,219;--bs-warning-rgb: 243,156,18;--bs-danger-rgb: 231,76,60;--bs-light-rgb: 236,240,241;--bs-dark-rgb: 123,138,139;--bs-primary-text-emphasis: #121920;--bs-secondary-text-emphasis: #3c4242;--bs-success-text-emphasis: #0a4b3e;--bs-info-text-emphasis: #153d58;--bs-warning-text-emphasis: #613e07;--bs-danger-text-emphasis: #5c1e18;--bs-light-text-emphasis: #7b8a8b;--bs-dark-text-emphasis: #7b8a8b;--bs-primary-bg-subtle: #d5d8dc;--bs-secondary-bg-subtle: #eaeded;--bs-success-bg-subtle: #d1f2eb;--bs-info-bg-subtle: #d6eaf8;--bs-warning-bg-subtle: #fdebd0;--bs-danger-bg-subtle: #fadbd8;--bs-light-bg-subtle: #fcfcfd;--bs-dark-bg-subtle: #ced4da;--bs-primary-border-subtle: #abb2b9;--bs-secondary-border-subtle: #d5dbdb;--bs-success-border-subtle: #a3e4d7;--bs-info-border-subtle: #aed6f1;--bs-warning-border-subtle: #fad7a0;--bs-danger-border-subtle: #f5b7b1;--bs-light-border-subtle: #ecf0f1;--bs-dark-border-subtle: #b4bcc2;--bs-white-rgb: 255,255,255;--bs-black-rgb: 0,0,0;--bs-font-sans-serif: Lato, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #2c3e50;--bs-dropdown-link-disabled-color: #b4bcc2;--bs-dropdown-header-color: #b4bcc2}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: #95a5a6;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(44,62,80,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: #ecf0f1;--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) #ecf0f1;--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius: var(--bs-border-radius);--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #2c3e50}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: .125rem;--bs-nav-underline-link-active-color: var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 1rem;--bs-navbar-color: #fff;--bs-navbar-hover-color: #18bc9c;--bs-navbar-disabled-color: rgba(255,255,255,0.3);--bs-navbar-active-color: #18bc9c;--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(255,255,255,0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:transparent;border:var(--bs-border-width) solid 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid 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ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23121920'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: .75rem;--bs-breadcrumb-padding-y: .375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: .25rem;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: rgba(0,0,0,0);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: rgba(0,0,0,0);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: rgba(0,0,0,0);--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: rgba(0,0,0,0);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0 * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto 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var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: .5rem;--bs-tooltip-padding-y: .25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:.875rem;--bs-tooltip-color: var(--bs-body-bg);--bs-tooltip-bg: var(--bs-emphasis-color);--bs-tooltip-border-radius: var(--bs-border-radius);--bs-tooltip-opacity: .9;--bs-tooltip-arrow-width: .8rem;--bs-tooltip-arrow-height: .4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow{bottom:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) 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var(--bs-border-width);--bs-popover-border-color: var(--bs-border-color-translucent);--bs-popover-border-radius: var(--bs-border-radius-lg);--bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: .5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: var(--bs-secondary-bg);--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: var(--bs-body-color);--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: .5rem;--bs-popover-arrow-border: 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#6f42c1;color:var(--bslib-color-fg)}.bg-pink{--bslib-color-bg: #e83e8c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-pink{--bslib-color-fg: #e83e8c;color:var(--bslib-color-fg)}.bg-red{--bslib-color-bg: #e74c3c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-red{--bslib-color-fg: #e74c3c;color:var(--bslib-color-fg)}.bg-orange{--bslib-color-bg: #fd7e14;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-orange{--bslib-color-fg: #fd7e14;color:var(--bslib-color-fg)}.bg-yellow{--bslib-color-bg: #f39c12;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-yellow{--bslib-color-fg: #f39c12;color:var(--bslib-color-fg)}.bg-green{--bslib-color-bg: #18bc9c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-green{--bslib-color-fg: 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diff --git a/deps/bootstrap-5.3.1/font.css b/deps/bootstrap-5.3.1/font.css
new file mode 100644
index 0000000..409df22
--- /dev/null
+++ b/deps/bootstrap-5.3.1/font.css
@@ -0,0 +1,21 @@
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6u8w4BMUTPHjxswWA.woff) format('woff');
+}
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6uyw4BMUTPHvxo.woff) format('woff');
+}
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 700;
+  font-display: swap;
+  src: url(fonts/S6u9w4BMUTPHh6UVeww.woff) format('woff');
+}
diff --git a/deps/bootstrap-5.3.1/fonts/1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff b/deps/bootstrap-5.3.1/fonts/1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff
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diff --git a/deps/data-deps.txt b/deps/data-deps.txt
index 21592b5..693b82d 100644
--- a/deps/data-deps.txt
+++ b/deps/data-deps.txt
@@ -1,4 +1,4 @@
 <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>
+<link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
diff --git a/index.html b/index.html
index a871351..a6fca14 100644
--- a/index.html
+++ b/index.html
@@ -20,8 +20,8 @@
     standardised definitions for the different methods, inputs and outputs.">
 <title>Data processing &amp; analysis tools for Metabolomics and other omics • structToolbox</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">
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-<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 href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.3.1/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">
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 <!-- 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="Data processing &amp; analysis tools for Metabolomics and other omics">
 <meta property="og:description" content="An extensive set of data (pre-)processing and analysis methods and 
@@ -50,7 +50,7 @@
     
     <a class="navbar-brand me-2" href="index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/pkgdown.yml b/pkgdown.yml
index a7e8337..bd6f7fc 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   data_analysis_omics_using_the_structtoolbox: data_analysis_omics_using_the_structtoolbox.html
-last_built: 2023-10-13T20:09Z
+last_built: 2024-01-31T09:59Z
 urls:
   reference: https://computational-metabolomics.github.io/structToolbox/reference
   article: https://computational-metabolomics.github.io/structToolbox/articles
diff --git a/reference/ANOVA.html b/reference/ANOVA.html
index b02aa7e..732512a 100644
--- a/reference/ANOVA.html
+++ b/reference/ANOVA.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="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."><title>Analysis of Variance — ANOVA • structToolbox</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="Analysis of Variance — ANOVA"><meta property="og:description" content="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."><!-- 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="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."><title>Analysis of Variance — ANOVA • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Analysis of Variance — ANOVA"><meta property="og:description" content="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -74,7 +75,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>ss_type</dt>
-<dd><p>(character) ANOVA sum of squares. Allowed values are limited to the following:</p><ul><li><p><code>"II"</code>: Type II sum of squares.</p></li>
+<dd><p>(character) ANOVA sum of squares. Allowed values are limited to the following:</p><ul><li><p><code>"I"</code>: Type I sum of squares.</p></li>
+<li><p><code>"II"</code>: Type II sum of squares.</p></li>
 <li><p><code>"III"</code>: Type III sum of squares.</p></li>
 </ul><p>The default is <code>"III"</code>.</p></dd>
 
@@ -92,6 +94,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>car</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>ANOVA</code> object inherits the following <code>struct</code> classes: <br><br><code>ANOVA()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Fox J, Weisberg S (2019). <em>An R Companion to Applied Regression</em>, Third
@@ -101,7 +109,13 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">ANOVA</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span></span>
+<span class="r-in"><span>      ss_type <span class="op">=</span> <span class="st">"III"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">ANOVA</span><span class="op">(</span>formula<span class="op">=</span><span class="va">y</span><span class="op">~</span><span class="va">Species</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/AUC.html b/reference/AUC.html
index 03d5a02..a0fe80f 100644
--- a/reference/AUC.html
+++ b/reference/AUC.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="The area under the ROC curve of a classifier is estimated using the trapezoid method."><title>Area under ROC curve — AUC • structToolbox</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="Area under ROC curve — AUC"><meta property="og:description" content="The area under the ROC curve of a classifier is estimated using the trapezoid method."><!-- 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="The area under the ROC curve of a classifier is estimated using the trapezoid method."><title>Area under ROC curve — AUC • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Area under ROC curve — AUC"><meta property="og:description" content="The area under the ROC curve of a classifier is estimated using the trapezoid method."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,10 +71,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 AUC
 </code> object. This object has no <code>output</code> slots.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>AUC</code> object inherits the following <code>struct</code> classes: <br><br><code>AUC()</code> ⭢ <code>metric()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">AUC</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">XCV</span> <span class="op">=</span> <span class="fu"><a href="kfold_xval.html">kfold_xval</a></span><span class="op">(</span>folds<span class="op">=</span><span class="fl">5</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span> <span class="op">*</span></span></span>
 <span class="r-in"><span>      <span class="op">(</span><span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>number_components<span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">MET</span> <span class="op">=</span> <span class="fu">AUC</span><span class="op">(</span><span class="op">)</span></span></span>
diff --git a/reference/DFA.html b/reference/DFA.html
index bb83679..e0dbbc8 100644
--- a/reference/DFA.html
+++ b/reference/DFA.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="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."><title>Discriminant Factor Analysis — DFA • structToolbox</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="Discriminant Factor Analysis — DFA"><meta property="og:description" content="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."><!-- 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="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."><title>Discriminant Factor Analysis — DFA • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Discriminant Factor Analysis — DFA"><meta property="og:description" content="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,7 +64,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>number_components</dt>
-<dd><p>(numeric, integer) The number of DFA components calculated. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric, integer) The number of DFA components calculated. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -76,6 +76,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>DFA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>eigenvalues</code></td><td>(data.frame)</td></tr><tr><td><code>that</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>DFA</code> object inherits the following <code>struct</code> classes: <br><br><code>DFA()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Manly B (1986). <em>Multivariate Statistical Methods: A Primer</em>. Chapman
@@ -84,7 +90,11 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">DFA</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      number_components <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">DFA</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/DatasetExperiment_boxplot.html b/reference/DatasetExperiment_boxplot.html
index 6b10a2f..27139d3 100644
--- a/reference/DatasetExperiment_boxplot.html
+++ b/reference/DatasetExperiment_boxplot.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="A boxplot to visualise the distribution of values within a subset of features."><title>Feature distribution histogram — DatasetExperiment_boxplot • structToolbox</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="Feature distribution histogram — DatasetExperiment_boxplot"><meta property="og:description" content="A boxplot to visualise the distribution of values within a subset of features."><!-- 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="A boxplot to visualise the distribution of values within a subset of features."><title>Feature distribution histogram — DatasetExperiment_boxplot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Feature distribution histogram — DatasetExperiment_boxplot"><meta property="og:description" content="A boxplot to visualise the distribution of values within a subset of features."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -70,17 +70,19 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>by_sample</dt>
-<dd><p>(logical) Plot by sample. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: The data is plotted across samples for a subset of features.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Plot by sample. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is plotted across features for a subset of samples.</p></li>
+<li><p><code>"FALSE"</code>: The data is plotted across samples for a subset of features.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>per_class</dt>
-<dd><p>(logical) Plot per class. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: The data is plotted for all samples.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Plot per class. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is plotted for each class.</p></li>
+<li><p><code>"FALSE"</code>: The data is plotted for all samples.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>number</dt>
-<dd><p>(numeric, integer) The number of features/samples plotted. The default is <code>50</code>.</p></dd>
+<dd><p>(numeric, integer) The number of features/samples plotted. The default is <code>50</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -99,10 +101,22 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>DatasetExperiment_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>DatasetExperiment_boxplot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">DatasetExperiment_boxplot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      by_sample <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      per_class <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      number <span class="op">=</span> <span class="fl">50</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">DatasetExperiment_boxplot</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span>,number<span class="op">=</span><span class="fl">10</span>,per_class<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 585 rows containing non-finite values (`stat_boxplot()`).</span>
diff --git a/reference/DatasetExperiment_dist.html b/reference/DatasetExperiment_dist.html
index 5cbb4c1..44dd070 100644
--- a/reference/DatasetExperiment_dist.html
+++ b/reference/DatasetExperiment_dist.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="A histogram to visualise the distribution of values within features."><title>Feature distribution histogram — DatasetExperiment_dist • structToolbox</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="Feature distribution histogram — DatasetExperiment_dist"><meta property="og:description" content="A histogram to visualise the distribution of values within features."><!-- 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="A histogram to visualise the distribution of values within features."><title>Feature distribution histogram — DatasetExperiment_dist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Feature distribution histogram — DatasetExperiment_dist"><meta property="og:description" content="A histogram to visualise the distribution of values within features."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,8 +64,9 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>per_class</dt>
-<dd><p>(logical) Plot per class. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: The distribution is plotted for all samples.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Plot per class. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The distributions are plotted for each class.</p></li>
+<li><p><code>"FALSE"</code>: The distribution is plotted for all samples.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -81,10 +82,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>DatasetExperiment_dist</code> object inherits the following <code>struct</code> classes: <br><br><code>DatasetExperiment_dist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">DatasetExperiment_dist</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      per_class <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">DatasetExperiment_dist</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 9222 rows containing non-finite values (`stat_bin()`).</span>
diff --git a/reference/DatasetExperiment_factor_boxplot.html b/reference/DatasetExperiment_factor_boxplot.html
index 6321b9f..214cfe7 100644
--- a/reference/DatasetExperiment_factor_boxplot.html
+++ b/reference/DatasetExperiment_factor_boxplot.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="Boxplot for a feature to visualise the distribution of values within each group"><title>Factor boxplot — DatasetExperiment_factor_boxplot • structToolbox</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="Factor boxplot — DatasetExperiment_factor_boxplot"><meta property="og:description" content="Boxplot for a feature to visualise the distribution of values within each group"><!-- 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="Boxplot for a feature to visualise the distribution of values within each group"><title>Factor boxplot — DatasetExperiment_factor_boxplot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Factor boxplot — DatasetExperiment_factor_boxplot"><meta property="og:description" content="Boxplot for a feature to visualise the distribution of values within each group"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,10 +80,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>DatasetExperiment_factor_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>DatasetExperiment_factor_boxplot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">DatasetExperiment_factor_boxplot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_names <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      feature_to_plot <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">DatasetExperiment_factor_boxplot</span><span class="op">(</span>factor_names<span class="op">=</span><span class="st">'Species'</span>,feature_to_plot<span class="op">=</span><span class="st">'Petal.Width'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="DatasetExperiment_factor_boxplot-1.png" alt="" width="700" height="433"></span>
diff --git a/reference/DatasetExperiment_heatmap.html b/reference/DatasetExperiment_heatmap.html
index 0a2b710..34d2d53 100644
--- a/reference/DatasetExperiment_heatmap.html
+++ b/reference/DatasetExperiment_heatmap.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="A heatmap to visualise the measured values in a data matrix."><title>DatasetExperiment heatmap — DatasetExperiment_heatmap • structToolbox</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="DatasetExperiment heatmap — DatasetExperiment_heatmap"><meta property="og:description" content="A heatmap to visualise the measured values in a data matrix."><!-- 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="A heatmap to visualise the measured values in a data matrix."><title>DatasetExperiment heatmap — DatasetExperiment_heatmap • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="DatasetExperiment heatmap — DatasetExperiment_heatmap"><meta property="og:description" content="A heatmap to visualise the measured values in a data matrix."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,6 +80,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>reshape2</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>DatasetExperiment_heatmap</code> object inherits the following <code>struct</code> classes: <br><br><code>DatasetExperiment_heatmap()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Wickham H (2007). "Reshaping Data with the reshape Package." <em>Journal
@@ -89,7 +95,10 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">DatasetExperiment_heatmap</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      na_colour <span class="op">=</span> <span class="st">"#FF00E4"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">DatasetExperiment_heatmap</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="DatasetExperiment_heatmap-1.png" alt="" width="700" height="433"></span>
diff --git a/reference/HCA.html b/reference/HCA.html
index d52c3db..6d957d1 100644
--- a/reference/HCA.html
+++ b/reference/HCA.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="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."><title>Hierarchical Cluster Analysis — HCA • structToolbox</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="Hierarchical Cluster Analysis — HCA"><meta property="og:description" content="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."><!-- 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="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."><title>Hierarchical Cluster Analysis — HCA • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Hierarchical Cluster Analysis — HCA"><meta property="og:description" content="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -66,7 +66,8 @@ <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>dist_method</dt>
-<dd><p>(character) Distance measure. Allowed values are limited to the following:</p><ul><li><p><code>"maximum"</code>: The maximum distance.</p></li>
+<dd><p>(character) Distance measure. Allowed values are limited to the following:</p><ul><li><p><code>"euclidean"</code>: The euclidean distance (2 norm).</p></li>
+<li><p><code>"maximum"</code>: The maximum distance.</p></li>
 <li><p><code>"manhattan"</code>: The absolute distance (1 norm).</p></li>
 <li><p><code>"canberra"</code>: A weighted version of the mahattan distance.</p></li>
 <li><p><code>"minkowski"</code>: A generalisation of manhattan and euclidean distance to nth norm.</p></li>
@@ -74,7 +75,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>cluster_method</dt>
-<dd><p>(character) Agglomeration method. Allowed values are limited to the following:</p><ul><li><p><code>"ward.D2"</code>: Ward clustering using sqaured distances.</p></li>
+<dd><p>(character) Agglomeration method. Allowed values are limited to the following:</p><ul><li><p><code>"ward.D"</code>: Ward clustering.</p></li>
+<li><p><code>"ward.D2"</code>: Ward clustering using sqaured distances.</p></li>
 <li><p><code>"single"</code>: Single linkage.</p></li>
 <li><p><code>"complete"</code>: Complete linkage.</p></li>
 <li><p><code>"average"</code>: Average linkage (UPGMA).</p></li>
@@ -85,7 +87,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>minkowski_power</dt>
-<dd><p>(numeric)  The default is <code>2</code>.</p></dd>
+<dd><p>(numeric)  The default is <code>2</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -105,6 +107,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>stats</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>HCA</code> object inherits the following <code>struct</code> classes: <br><br><code>HCA()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>R Core Team (2023). <em>R: A Language and Environment for Statistical
@@ -114,7 +122,13 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">HCA</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      dist_method <span class="op">=</span> <span class="st">"euclidean"</span>,</span></span>
+<span class="r-in"><span>      cluster_method <span class="op">=</span> <span class="st">"complete"</span>,</span></span>
+<span class="r-in"><span>      minkowski_power <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">HCA</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/HSD.html b/reference/HSD.html
index 26ecd83..70dc1c6 100644
--- a/reference/HSD.html
+++ b/reference/HSD.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="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature."><title>Tukey's Honest Significant Difference — HSD • structToolbox</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="Tukey's Honest Significant Difference — HSD"><meta property="og:description" content="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature."><!-- 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="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature."><title>Tukey's Honest Significant Difference — HSD • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Tukey's Honest Significant Difference — HSD"><meta property="og:description" content="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -74,8 +75,9 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>unbalanced</dt>
-<dd><p>(logical) Unbalanced model. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: No correction is applied for unbalanced designs.</p></li>
-</ul><p>The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Unbalanced model. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: A correction is applied for unbalanced designs.</p></li>
+<li><p><code>"FALSE"</code>: No correction is applied for unbalanced designs.</p></li>
+</ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -91,16 +93,28 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>agricolae</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>HSD</code> object inherits the following <code>struct</code> classes: <br><br><code>HSD()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>de Mendiburu F (2023). <em>agricolae: Statistical Procedures for
-Agricultural Research</em>. R package version 1.3-6,
+Agricultural Research</em>. R package version 1.3-7,
 <a href="https://CRAN.R-project.org/package=agricolae" class="external-link">https://CRAN.R-project.org/package=agricolae</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">HSD</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span></span>
+<span class="r-in"><span>      unbalanced <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">HSD</span><span class="op">(</span>formula<span class="op">=</span><span class="va">y</span><span class="op">~</span><span class="va">Species</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/HSDEM.html b/reference/HSDEM.html
index f4d8f21..5977be3 100644
--- a/reference/HSDEM.html
+++ b/reference/HSDEM.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="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature. For mixed effects models estimated marginal means are used."><title>Tukey's Honest Significant Difference using estimated marginal means — HSDEM • structToolbox</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="Tukey's Honest Significant Difference using estimated marginal means — HSDEM"><meta property="og:description" content="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature. For mixed effects models estimated marginal means are used."><!-- 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="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature. For mixed effects models estimated marginal means are used."><title>Tukey's Honest Significant Difference using estimated marginal means — HSDEM • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Tukey's Honest Significant Difference using estimated marginal means — HSDEM"><meta property="og:description" content="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature. For mixed effects models estimated marginal means are used."><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -84,15 +85,22 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <p>A  <code>HSDEM</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>nlme</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>emmeans</code></p></li>
+<li><p><code>nlme</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>HSDEM</code> object inherits the following <code>struct</code> classes: <br><br><code>HSDEM()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Lenth R (2023). <em>emmeans: Estimated Marginal Means, aka Least-Squares
-Means</em>. R package version 1.8.7,
+Means</em>. R package version 1.9.0,
 <a href="https://CRAN.R-project.org/package=emmeans" class="external-link">https://CRAN.R-project.org/package=emmeans</a>.</p>
 <p>Pinheiro J, Bates D, R Core Team (2023). <em>nlme: Linear and Nonlinear
-Mixed Effects Models</em>. R package version 3.1-162,
+Mixed Effects Models</em>. R package version 3.1-164,
 <a href="https://CRAN.R-project.org/package=nlme" class="external-link">https://CRAN.R-project.org/package=nlme</a>.</p>
 <p>Pinheiro JC, Bates DM (2000). <em>Mixed-Effects Models in S and S-PLUS</em>.
 Springer, New York. doi:10.1007/b98882
@@ -101,7 +109,12 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">HSDEM</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">D</span><span class="op">$</span><span class="va">sample_meta</span><span class="op">$</span><span class="va">id</span><span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">D</span><span class="op">)</span> <span class="co"># dummy id column</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">HSDEM</span><span class="op">(</span>formula <span class="op">=</span> <span class="va">y</span><span class="op">~</span><span class="va">Species</span><span class="op">+</span> <span class="fu">Error</span><span class="op">(</span><span class="va">id</span><span class="op">/</span><span class="va">Species</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
diff --git a/reference/MTBLS79_DatasetExperiment.html b/reference/MTBLS79_DatasetExperiment.html
index 88e77c3..d7d5ba0 100644
--- a/reference/MTBLS79_DatasetExperiment.html
+++ b/reference/MTBLS79_DatasetExperiment.html
@@ -8,7 +8,7 @@
 extracts. It comprises of twenty biological samples (cow vs. sheep) that were
 analysed repeatedly, in 8 batches across 7 days, together with a concurrent set
 of quality control (QC) samples. Data are presented from each step of the workflow
-and are available in MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)"><title>MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment • structToolbox</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="MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment"><meta property="og:description" content="Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach
+and are available in MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)"><title>MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment"><meta property="og:description" content="Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach
 for characterising molecular responses of organisms to disease, drugs and the
 environment. Increasingly large-scale metabolomics studies are being conducted,
 necessitating improvements in both bioanalytical and computational workflows
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/OPLSDA.html b/reference/OPLSDA.html
index 4af4384..554ca0b 100644
--- a/reference/OPLSDA.html
+++ b/reference/OPLSDA.html
@@ -1,6 +1,6 @@
 <!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="OPLS splits a data matrix into two parts. One part
-contains information orthogonal to the input vector, and the other is non-orthogonal."><title>Orthogonal Partial Least Squares regression — OPLSDA • structToolbox</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="Orthogonal Partial Least Squares regression — OPLSDA"><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
+contains information orthogonal to the input vector, and the other is non-orthogonal."><title>Orthogonal Partial Least Squares regression — OPLSDA • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Orthogonal Partial Least Squares regression — OPLSDA"><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
 contains information orthogonal to the input vector, and the other is non-orthogonal."><!-- 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>
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -63,7 +63,7 @@ <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>number_components</dt>
-<dd><p>(numeric, integer) The number of orthgonal components. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric, integer) The number of orthgonal components. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -79,10 +79,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>OPLSDA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>opls_model</code></td><td>(list)</td></tr><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>orthogonal</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>OPLSDA</code> object inherits the following <code>struct</code> classes: <br><br><code>OPLSDA()</code> ⭢ <code><a href="OPLSR.html">OPLSR()</a></code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="OPLSR.html">OPLSR</a></span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">OPLSDA</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_components <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="OPLSR.html">OPLSR</a></span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/OPLSR.html b/reference/OPLSR.html
index 036dc70..bf8ae9a 100644
--- a/reference/OPLSR.html
+++ b/reference/OPLSR.html
@@ -1,6 +1,6 @@
 <!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="OPLS splits a data matrix into two parts. One part
-contains information orthogonal to the input vector, and the other is non-orthogonal."><title>Orthogonal Partial Least Squares regression — OPLSR • structToolbox</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="Orthogonal Partial Least Squares regression — OPLSR"><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
+contains information orthogonal to the input vector, and the other is non-orthogonal."><title>Orthogonal Partial Least Squares regression — OPLSR • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Orthogonal Partial Least Squares regression — OPLSR"><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
 contains information orthogonal to the input vector, and the other is non-orthogonal."><!-- 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>
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -63,7 +63,7 @@ <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>number_components</dt>
-<dd><p>(numeric, integer) The number of orthgonal components. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric, integer) The number of orthgonal components. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -79,10 +79,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>OPLSR</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>opls_model</code></td><td>(list)</td></tr><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>orthogonal</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>OPLSR</code> object inherits the following <code>struct</code> classes: <br><br><code>OPLSR()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">OPLSR</span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">OPLSR</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_components <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">OPLSR</span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/PCA.html b/reference/PCA.html
index 0075814..34b4fa8 100644
--- a/reference/PCA.html
+++ b/reference/PCA.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="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."><title>Principal Component Analysis (PCA) — PCA • structToolbox</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="Principal Component Analysis (PCA) — PCA"><meta property="og:description" content="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."><!-- 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="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."><title>Principal Component Analysis (PCA) — PCA • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Principal Component Analysis (PCA) — PCA"><meta property="og:description" content="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."><!-- 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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@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>number_components</dt>
-<dd><p>(numeric, integer) The number of Principal Components calculated. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric, integer) The number of Principal Components calculated. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>PCA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment) A matrix of PCA scores where each column corresponds to a Principal Component.</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>eigenvalues</code></td><td>(data.frame)</td></tr><tr><td><code>ssx</code></td><td>(numeric)</td></tr><tr><td><code>correlation</code></td><td>(data.frame)</td></tr><tr><td><code>that</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>PCA</code> object inherits the following <code>struct</code> classes: <br><br><code>PCA()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PCA</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PCA</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_components <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/PLSDA.html b/reference/PLSDA.html
index 2d50844..e46e8c3 100644
--- a/reference/PLSDA.html
+++ b/reference/PLSDA.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="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable. For &amp;gt;2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value."><title>Partial least squares discriminant analysis — PLSDA • structToolbox</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="Partial least squares discriminant analysis — PLSDA"><meta property="og:description" content="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable. For &amp;gt;2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value."><!-- 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="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable. For &amp;gt;2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value."><title>Partial least squares discriminant analysis — PLSDA • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Partial least squares discriminant analysis — PLSDA"><meta property="og:description" content="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable. For &amp;gt;2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>number_components</dt>
-<dd><p>(numeric, integer) The number of PLS components. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric, integer) The number of PLS components. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -68,7 +68,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>pred_method</dt>
-<dd><p>(character) Prediction method. Allowed values are limited to the following:</p><ul><li><p><code>"max_prob"</code>: The predicted group is selected based on the largest probability of group membership.</p></li>
+<dd><p>(character) Prediction method. Allowed values are limited to the following:</p><ul><li><p><code>"max_yhat"</code>: The predicted group is selected based on the largest value of y_hat.</p></li>
+<li><p><code>"max_prob"</code>: The predicted group is selected based on the largest probability of group membership.</p></li>
 </ul><p>The default is <code>"max_prob"</code>.</p></dd>
 
 
@@ -85,10 +86,16 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>PLSDA</code> object inherits the following <code>struct</code> classes: <br><br><code>PLSDA()</code> ⭢ <code><a href="PLSR.html">PLSR()</a></code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Liland K, Mevik B, Wehrens R (2023). <em>pls: Partial Least Squares and
-Principal Component Regression</em>. R package version 2.8-2,
+Principal Component Regression</em>. R package version 2.8-3,
 <a href="https://CRAN.R-project.org/package=pls" class="external-link">https://CRAN.R-project.org/package=pls</a>.</p>
 <p>Perez NF, Ferre J, Boque R (2009). "Calculation of the reliability of
 classification in discriminant partial least-squares binary
@@ -100,7 +107,12 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSDA</span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSDA</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_components <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      pred_method <span class="op">=</span> <span class="st">"max_prob"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSDA</span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/PLSR.html b/reference/PLSR.html
index 8bbe1eb..0de3bdf 100644
--- a/reference/PLSR.html
+++ b/reference/PLSR.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="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. For regression the response is a continuous variable."><title>Partial least squares regression — PLSR • structToolbox</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="Partial least squares regression — PLSR"><meta property="og:description" content="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. For regression the response is a continuous variable."><!-- 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="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. For regression the response is a continuous variable."><title>Partial least squares regression — PLSR • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Partial least squares regression — PLSR"><meta property="og:description" content="PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. For regression the response is a continuous variable."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>number_components</dt>
-<dd><p>(numeric, integer) The number of PLS components. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric, integer) The number of PLS components. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -80,16 +80,26 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>PLSR</code> object inherits the following <code>struct</code> classes: <br><br><code>PLSR()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Liland K, Mevik B, Wehrens R (2023). <em>pls: Partial Least Squares and
-Principal Component Regression</em>. R package version 2.8-2,
+Principal Component Regression</em>. R package version 2.8-3,
 <a href="https://CRAN.R-project.org/package=pls" class="external-link">https://CRAN.R-project.org/package=pls</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSR</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'run_order'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSR</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_components <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSR</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'run_order'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/SVM.html b/reference/SVM.html
index e1939be..a1c340f 100644
--- a/reference/SVM.html
+++ b/reference/SVM.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="Support Vector Machines (SVM) are a machine learning algorithm for classification. They can make use of kernel functions to generate highly non-linear boundaries between groups."><title>Support Vector Machine Classifier — SVM • structToolbox</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="Support Vector Machine Classifier — SVM"><meta property="og:description" content="Support Vector Machines (SVM) are a machine learning algorithm for classification. They can make use of kernel functions to generate highly non-linear boundaries between groups."><!-- 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="Support Vector Machines (SVM) are a machine learning algorithm for classification. They can make use of kernel functions to generate highly non-linear boundaries between groups."><title>Support Vector Machine Classifier — SVM • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Support Vector Machine Classifier — SVM"><meta property="og:description" content="Support Vector Machines (SVM) are a machine learning algorithm for classification. They can make use of kernel functions to generate highly non-linear boundaries between groups."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -73,26 +73,27 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>kernel</dt>
-<dd><p>(character) Kernel type. Allowed values are limited to the following:</p><ul><li><p><code>"polynomial"</code>: .</p></li>
+<dd><p>(character) Kernel type. Allowed values are limited to the following:</p><ul><li><p><code>"linear"</code>: .</p></li>
+<li><p><code>"polynomial"</code>: .</p></li>
 <li><p><code>"radial"</code>: .</p></li>
 <li><p><code>"sigmoid"</code>: .</p></li>
 </ul><p>The default is <code>"linear"</code>.</p></dd>
 
 
 <dt>degree</dt>
-<dd><p>(numeric) The polynomial degree. The default is <code>3</code>.</p></dd>
+<dd><p>(numeric) The polynomial degree. The default is <code>3</code>.<br></p></dd>
 
 
 <dt>gamma</dt>
-<dd><p>(numeric) The gamma parameter. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric) The gamma parameter. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>coef0</dt>
-<dd><p>(numeric) The offset coefficient. The default is <code>0</code>.</p></dd>
+<dd><p>(numeric) The offset coefficient. The default is <code>0</code>.<br></p></dd>
 
 
 <dt>cost</dt>
-<dd><p>(numeric) The cost of violating the constraints. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric) The cost of violating the constraints. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>class_weights</dt>
@@ -114,11 +115,17 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>e1071</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>SVM</code> object inherits the following <code>struct</code> classes: <br><br><code>SVM()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2023).
 <em>e1071: Misc Functions of the Department of Statistics, Probability
-Theory Group (Formerly: E1071), TU Wien</em>. R package version 1.7-13,
+Theory Group (Formerly: E1071), TU Wien</em>. R package version 1.7-14,
 <a href="https://CRAN.R-project.org/package=e1071" class="external-link">https://CRAN.R-project.org/package=e1071</a>.</p>
 <p>Brereton RG, Lloyd GR (2010). "Support Vector Machines for
 classification and regression." <em>The Analyst</em>, <em>135</em>(2), 230-267.</p>
@@ -126,7 +133,16 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">SVM</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,gamma<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">SVM</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      kernel <span class="op">=</span> <span class="st">"linear"</span>,</span></span>
+<span class="r-in"><span>      degree <span class="op">=</span> <span class="fl">3</span>,</span></span>
+<span class="r-in"><span>      gamma <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      coef0 <span class="op">=</span> <span class="fl">0</span>,</span></span>
+<span class="r-in"><span>      cost <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      class_weights <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">SVM</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,gamma<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/as_data_frame.html b/reference/as_data_frame.html
index 086d590..f899892 100644
--- a/reference/as_data_frame.html
+++ b/reference/as_data_frame.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="Convert the outputs of the input model into a data.frame."><title>Convert to data.frame — as_data_frame • structToolbox</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="Convert to data.frame — as_data_frame"><meta property="og:description" content="Convert the outputs of the input model into a data.frame."><!-- 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="Convert the outputs of the input model into a data.frame."><title>Convert to data.frame — as_data_frame • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Convert to data.frame — as_data_frame"><meta property="og:description" content="Convert the outputs of the input model into a data.frame."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/autoscale.html b/reference/autoscale.html
index b9b4549..c5d3615 100644
--- a/reference/autoscale.html
+++ b/reference/autoscale.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="Each variable/feature is mean centred and scaled by the standard deviation. The transformed variables have zero-mean and unit-variance."><title>Autoscaling — autoscale • structToolbox</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="Autoscaling — autoscale"><meta property="og:description" content="Each variable/feature is mean centred and scaled by the standard deviation. The transformed variables have zero-mean and unit-variance."><!-- 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="Each variable/feature is mean centred and scaled by the standard deviation. The transformed variables have zero-mean and unit-variance."><title>Autoscaling — autoscale • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Autoscaling — autoscale"><meta property="og:description" content="Each variable/feature is mean centred and scaled by the standard deviation. The transformed variables have zero-mean and unit-variance."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,8 @@ <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>mode</dt>
-<dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"sample_meta"</code>: Autoscaling is applied to the sample_meta data only.</p></li>
+<dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"data"</code>: Autoscaling is applied to the data matrix only.</p></li>
+<li><p><code>"sample_meta"</code>: Autoscaling is applied to the sample_meta data only.</p></li>
 <li><p><code>"both"</code>: Autoscaling is applied to both the data matrix and the meta data.</p></li>
 </ul><p>The default is <code>"data"</code>.</p></dd>
 
@@ -74,10 +75,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>autoscale</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>autoscaled</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean_data</code></td><td>(numeric)</td></tr><tr><td><code>sd_data</code></td><td>(numeric)</td></tr><tr><td><code>mean_sample_meta</code></td><td>(numeric)</td></tr><tr><td><code>sd_sample_meta</code></td><td>(numeric)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>autoscale</code> object inherits the following <code>struct</code> classes: <br><br><code>autoscale()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">autoscale</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      mode <span class="op">=</span> <span class="st">"data"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">autoscale</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_train.html">model_train</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_predict.html">model_predict</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
diff --git a/reference/balanced_accuracy.html b/reference/balanced_accuracy.html
index 36c13b9..ef801da 100644
--- a/reference/balanced_accuracy.html
+++ b/reference/balanced_accuracy.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="Balanced Accuracy is the average proportion of correctly classified samples across all groups."><title>Balanced Accuracy — balanced_accuracy • structToolbox</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="Balanced Accuracy — balanced_accuracy"><meta property="og:description" content="Balanced Accuracy is the average proportion of correctly classified samples across all groups."><!-- 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="Balanced Accuracy is the average proportion of correctly classified samples across all groups."><title>Balanced Accuracy — balanced_accuracy • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Balanced Accuracy — balanced_accuracy"><meta property="og:description" content="Balanced Accuracy is the average proportion of correctly classified samples across all groups."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,10 +71,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 balanced_accuracy
 </code> object. This object has no <code>output</code> slots.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>balanced_accuracy</code> object inherits the following <code>struct</code> classes: <br><br><code>balanced_accuracy()</code> ⭢ <code>metric()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">balanced_accuracy</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">XCV</span> <span class="op">=</span> <span class="fu"><a href="kfold_xval.html">kfold_xval</a></span><span class="op">(</span>folds<span class="op">=</span><span class="fl">5</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span> <span class="op">*</span></span></span>
 <span class="r-in"><span>      <span class="op">(</span><span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>number_components<span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">MET</span> <span class="op">=</span> <span class="fu">balanced_accuracy</span><span class="op">(</span><span class="op">)</span></span></span>
diff --git a/reference/blank_filter.html b/reference/blank_filter.html
index 2e81916..35f121d 100644
--- a/reference/blank_filter.html
+++ b/reference/blank_filter.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="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."><title>Blank filter — blank_filter • structToolbox</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="Blank filter — blank_filter"><meta property="og:description" content="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."><!-- 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="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."><title>Blank filter — blank_filter • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Blank filter — blank_filter"><meta property="og:description" content="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -67,7 +67,7 @@ <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>fold_change</dt>
-<dd><p>(numeric) Features with fold change less than this value are removed. The default is <code>20</code>.</p></dd>
+<dd><p>(numeric) Features with fold change less than this value are removed. The default is <code>20</code>.<br></p></dd>
 
 
 <dt>blank_label</dt>
@@ -83,7 +83,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>fraction_in_blank</dt>
-<dd><p>(numeric) Features present in less than this proportion of the blanks are not considered for removal. The default is <code>0</code>.</p></dd>
+<dd><p>(numeric) Features present in less than this proportion of the blanks are not considered for removal. The default is <code>0</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -99,17 +99,30 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>blank_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>blank_filter()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">blank_filter</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      fold_change <span class="op">=</span> <span class="fl">20</span>,</span></span>
+<span class="r-in"><span>      blank_label <span class="op">=</span> <span class="st">"Blank"</span>,</span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      fraction_in_blank <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">blank_filter</span><span class="op">(</span>fold_change<span class="op">=</span><span class="fl">2</span>,</span></span>
 <span class="r-in"><span>                 factor_name<span class="op">=</span><span class="st">'Species'</span>,</span></span>
 <span class="r-in"><span>                 blank_label<span class="op">=</span><span class="st">'setosa'</span>,</span></span>
diff --git a/reference/blank_filter_hist.html b/reference/blank_filter_hist.html
index 3f4fbbc..a3b41ef 100644
--- a/reference/blank_filter_hist.html
+++ b/reference/blank_filter_hist.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="A histogram of the calculated fold changes for the blank filter (median samples divided by median blanks)"><title>Histogram of blank filter fold changes — blank_filter_hist • structToolbox</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="Histogram of blank filter fold changes — blank_filter_hist"><meta property="og:description" content="A histogram of the calculated fold changes for the blank filter (median samples divided by median blanks)"><!-- 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="A histogram of the calculated fold changes for the blank filter (median samples divided by median blanks)"><title>Histogram of blank filter fold changes — blank_filter_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Histogram of blank filter fold changes — blank_filter_hist"><meta property="og:description" content="A histogram of the calculated fold changes for the blank filter (median samples divided by median blanks)"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>blank_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>blank_filter_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">blank_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">blank_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">blank_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/bootstrap.html b/reference/bootstrap.html
index fb3fbdc..78b084f 100644
--- a/reference/bootstrap.html
+++ b/reference/bootstrap.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="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."><title>Bootstrap resampling — bootstrap • structToolbox</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="Bootstrap resampling — bootstrap"><meta property="og:description" content="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."><!-- 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="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."><title>Bootstrap resampling — bootstrap • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Bootstrap resampling — bootstrap"><meta property="og:description" content="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>number_of_repetitions</dt>
-<dd><p>(numeric, integer) The number of bootstrap repetitions. The default is <code>100</code>.</p></dd>
+<dd><p>(numeric, integer) The number of bootstrap repetitions. The default is <code>100</code>.<br></p></dd>
 
 
 <dt>collect</dt>
@@ -76,10 +76,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>bootstrap</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>collected</code></td><td>(logical, list)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>bootstrap</code> object inherits the following <code>struct</code> classes: <br><br><code>bootstrap()</code> ⭢ <code>resampler()</code> ⭢ <code>iterator()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu">bootstrap</span><span class="op">(</span>number_of_repetitions <span class="op">=</span> <span class="fl">10</span>, collect <span class="op">=</span> <span class="st">'vip'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">bootstrap</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_of_repetitions <span class="op">=</span> <span class="fl">10</span>,</span></span>
+<span class="r-in"><span>      collect <span class="op">=</span> <span class="st">"vip"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu">bootstrap</span><span class="op">(</span>number_of_repetitions <span class="op">=</span> <span class="fl">10</span>, collect <span class="op">=</span> <span class="st">'vip'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/calculate.html b/reference/calculate.html
index 2d7b7d1..06ef8d2 100644
--- a/reference/calculate.html
+++ b/reference/calculate.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="Calculate metric"><title>Calculate metric — calculate,AUC-method • structToolbox</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="Calculate metric — calculate,AUC-method"><meta property="og:description" content="Calculate metric"><!-- 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="Calculate metric"><title>Calculate metric — calculate,AUC-method • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Calculate metric — calculate,AUC-method"><meta property="og:description" content="Calculate metric"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/chart_plot.html b/reference/chart_plot.html
index 9c52e0f..76d5551 100644
--- a/reference/chart_plot.html
+++ b/reference/chart_plot.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="Plots a chart object"><title>chart_plot method — chart_plot,dfa_scores_plot,DFA-method • structToolbox</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="chart_plot method — chart_plot,dfa_scores_plot,DFA-method"><meta property="og:description" content="Plots a chart object"><!-- 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="Plots a chart object"><title>chart_plot method — chart_plot,dfa_scores_plot,DFA-method • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="chart_plot method — chart_plot,dfa_scores_plot,DFA-method"><meta property="og:description" content="Plots a chart object"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/classical_lsq.html b/reference/classical_lsq.html
index cb461dd..8363e91 100644
--- a/reference/classical_lsq.html
+++ b/reference/classical_lsq.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="In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance."><title>Univariate Classical Least Squares Regression — classical_lsq • structToolbox</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="Univariate Classical Least Squares Regression — classical_lsq"><meta property="og:description" content="In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance."><!-- 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="In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance."><title>Univariate Classical Least Squares Regression — classical_lsq • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Univariate Classical Least Squares Regression — classical_lsq"><meta property="og:description" content="In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -74,8 +75,9 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>intercept</dt>
-<dd><p>(logical) Model intercept. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: An intercept term is not included in the model.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Model intercept. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: An intercept term is included in the model.</p></li>
+<li><p><code>"FALSE"</code>: An intercept term is not included in the model.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -87,10 +89,22 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>classical_lsq</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coefficients</code></td><td>(data.frame) The regression coefficients for each term in the model.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr><tr><td><code>r_squared</code></td><td>(data.frame) The value of R Squared for the fitted model.</td></tr><tr><td><code>adj_r_squared</code></td><td>(data.frame) The value ofAdjusted  R Squared for the fitted model.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>classical_lsq</code> object inherits the following <code>struct</code> classes: <br><br><code>classical_lsq()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">classical_lsq</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_names <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      intercept <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">classical_lsq</span><span class="op">(</span>factor_names <span class="op">=</span> <span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/compare_dist.html b/reference/compare_dist.html
index 81e5f59..ca3ddc9 100644
--- a/reference/compare_dist.html
+++ b/reference/compare_dist.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="Histograms and boxplots computed across samples and features are used to visually compare two datasets e.g. before and after filtering and/or normalisation."><title>Compare distributions — compare_dist • structToolbox</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="Compare distributions — compare_dist"><meta property="og:description" content="Histograms and boxplots computed across samples and features are used to visually compare two datasets e.g. before and after filtering and/or normalisation."><!-- 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="Histograms and boxplots computed across samples and features are used to visually compare two datasets e.g. before and after filtering and/or normalisation."><title>Compare distributions — compare_dist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Compare distributions — compare_dist"><meta property="og:description" content="Histograms and boxplots computed across samples and features are used to visually compare two datasets e.g. before and after filtering and/or normalisation."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -76,10 +76,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>compare_dist</code> object inherits the following <code>struct</code> classes: <br><br><code>compare_dist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D1</span><span class="op">=</span><span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">compare_dist</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D1</span><span class="op">=</span><span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">D2</span><span class="op">=</span><span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">compare_dist</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D1</span>,<span class="va">D2</span><span class="op">)</span></span></span>
diff --git a/reference/confounders_clsq.html b/reference/confounders_clsq.html
index f7ce9d3..1de6c05 100644
--- a/reference/confounders_clsq.html
+++ b/reference/confounders_clsq.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="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."><title>Check for confounding factors — confounders_clsq • structToolbox</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="Check for confounding factors — confounders_clsq"><meta property="og:description" content="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."><!-- 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="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."><title>Check for confounding factors — confounders_clsq • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Check for confounding factors — confounders_clsq"><meta property="og:description" content="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -67,11 +67,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -85,7 +86,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>threshold</dt>
-<dd><p>(numeric) Factors with a delta greater than the the threshold are considered to be confounding. The default is <code>0.15</code>.</p></dd>
+<dd><p>(numeric) Factors with a delta greater than the the threshold are considered to be confounding. The default is <code>0.15</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -97,10 +98,23 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>confounders_clsq</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coefficients</code></td><td>(data.frame)</td></tr><tr><td><code>p_value</code></td><td>(data.frame)</td></tr><tr><td><code>significant</code></td><td>(data.frame)</td></tr><tr><td><code>percent_change</code></td><td>(data.frame)</td></tr><tr><td><code>potential_confounders</code></td><td>(list)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>confounders_clsq</code> object inherits the following <code>struct</code> classes: <br><br><code>confounders_clsq()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">confounders_clsq</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      confounding_factors <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">0.15</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="filter_by_name.html">filter_by_name</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'include'</span>,dimension<span class="op">=</span><span class="st">'variable'</span>,</span></span>
 <span class="r-in"><span>        names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">D</span><span class="op">$</span><span class="va">data</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span> <span class="op">+</span> <span class="co"># first 10 features</span></span></span>
 <span class="r-in"><span>    <span class="fu"><a href="filter_smeta.html">filter_smeta</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'exclude'</span>,levels<span class="op">=</span><span class="st">'QC'</span>,</span></span>
diff --git a/reference/confounders_lsq_barchart.html b/reference/confounders_lsq_barchart.html
index a1c5714..45e280d 100644
--- a/reference/confounders_lsq_barchart.html
+++ b/reference/confounders_lsq_barchart.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="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><title>Confounding factor relative change barchart — confounders_lsq_barchart • structToolbox</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="Confounding factor relative change barchart — confounders_lsq_barchart"><meta property="og:description" content="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><!-- 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="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><title>Confounding factor relative change barchart — confounders_lsq_barchart • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Confounding factor relative change barchart — confounders_lsq_barchart"><meta property="og:description" content="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,7 +64,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>threshold</dt>
-<dd><p>(numeric) A horizontal line is plotted to indicate the threshold. The default is <code>10</code>.</p></dd>
+<dd><p>(numeric) A horizontal line is plotted to indicate the threshold. The default is <code>10</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -80,10 +80,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>confounders_lsq_barchart</code> object inherits the following <code>struct</code> classes: <br><br><code>confounders_lsq_barchart()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">confounders_lsq_barchart</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      feature_to_plot <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="filter_by_name.html">filter_by_name</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'include'</span>,dimension<span class="op">=</span><span class="st">'variable'</span>,</span></span>
 <span class="r-in"><span>        names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">D</span><span class="op">$</span><span class="va">data</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span> <span class="op">+</span> <span class="co"># first 10 features</span></span></span>
 <span class="r-in"><span>    <span class="fu"><a href="filter_smeta.html">filter_smeta</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'exclude'</span>,levels<span class="op">=</span><span class="st">'QC'</span>,</span></span>
diff --git a/reference/confounders_lsq_boxplot.html b/reference/confounders_lsq_boxplot.html
index 673d0ba..02ee570 100644
--- a/reference/confounders_lsq_boxplot.html
+++ b/reference/confounders_lsq_boxplot.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="A boxplot of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><title>Confounding factor relative change boxplot — confounders_lsq_boxplot • structToolbox</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="Confounding factor relative change boxplot — confounders_lsq_boxplot"><meta property="og:description" content="A boxplot of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><!-- 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="A boxplot of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><title>Confounding factor relative change boxplot — confounders_lsq_boxplot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Confounding factor relative change boxplot — confounders_lsq_boxplot"><meta property="og:description" content="A boxplot of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>threshold</dt>
-<dd><p>(numeric) A horizontal line is plotted to indicate the threshold. The default is <code>10</code>.</p></dd>
+<dd><p>(numeric) A horizontal line is plotted to indicate the threshold. The default is <code>10</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -76,10 +76,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>confounders_lsq_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>confounders_lsq_boxplot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">confounders_lsq_boxplot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="filter_by_name.html">filter_by_name</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'include'</span>,dimension<span class="op">=</span><span class="st">'variable'</span>,</span></span>
 <span class="r-in"><span>        names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">D</span><span class="op">$</span><span class="va">data</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span> <span class="op">+</span> <span class="co"># first 10 features</span></span></span>
 <span class="r-in"><span>    <span class="fu"><a href="filter_smeta.html">filter_smeta</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'exclude'</span>,levels<span class="op">=</span><span class="st">'QC'</span>,</span></span>
diff --git a/reference/constant_sum_norm.html b/reference/constant_sum_norm.html
index 24eabdb..1a62bcd 100644
--- a/reference/constant_sum_norm.html
+++ b/reference/constant_sum_norm.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="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."><title>Normalisation to constant sum — constant_sum_norm • structToolbox</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="Normalisation to constant sum — constant_sum_norm"><meta property="og:description" content="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."><!-- 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="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."><title>Normalisation to constant sum — constant_sum_norm • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Normalisation to constant sum — constant_sum_norm"><meta property="og:description" content="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>scaling_factor</dt>
-<dd><p>(numeric) The scaling factor applied after normalisation. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric) The scaling factor applied after normalisation. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -72,10 +72,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>constant_sum_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The sum of each row, used to normalise the samples.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>constant_sum_norm</code> object inherits the following <code>struct</code> classes: <br><br><code>constant_sum_norm()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">constant_sum_norm</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">constant_sum_norm</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      scaling_factor <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">constant_sum_norm</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/corr_coef.html b/reference/corr_coef.html
index ad48bf7..9098a2a 100644
--- a/reference/corr_coef.html
+++ b/reference/corr_coef.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="The correlation between features and a set of continuous factor are calculated. Multiple-test corrected p-values are used to indicate whether the computed coefficients may have occurred by chance."><title>Correlation coefficient — corr_coef • structToolbox</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="Correlation coefficient — corr_coef"><meta property="og:description" content="The correlation between features and a set of continuous factor are calculated. Multiple-test corrected p-values are used to indicate whether the computed coefficients may have occurred by chance."><!-- 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="The correlation between features and a set of continuous factor are calculated. Multiple-test corrected p-values are used to indicate whether the computed coefficients may have occurred by chance."><title>Correlation coefficient — corr_coef • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Correlation coefficient — corr_coef"><meta property="og:description" content="The correlation between features and a set of continuous factor are calculated. Multiple-test corrected p-values are used to indicate whether the computed coefficients may have occurred by chance."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -74,7 +75,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>method</dt>
-<dd><p>(character) Type of correlation. Allowed values are limited to the following:</p><ul><li><p><code>"pearson"</code>: Pearson product moment correlation is computed.</p></li>
+<dd><p>(character) Type of correlation. Allowed values are limited to the following:</p><ul><li><p><code>"kendall"</code>: Kendall's tau is computed.</p></li>
+<li><p><code>"pearson"</code>: Pearson product moment correlation is computed.</p></li>
 <li><p><code>"spearman"</code>: Spearman's rho statistic is computed.</p></li>
 </ul><p>The default is <code>"spearman"</code>.</p></dd>
 
@@ -92,6 +94,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>stats</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>corr_coef</code> object inherits the following <code>struct</code> classes: <br><br><code>corr_coef()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>R Core Team (2023). <em>R: A Language and Environment for Statistical
@@ -101,7 +109,13 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">corr_coef</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_names <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"spearman"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># subset for this example</span></span></span>
 <span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="va">D</span><span class="op">[</span>,<span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span></span></span>
diff --git a/reference/dfa_scores_plot.html b/reference/dfa_scores_plot.html
index 1d5d014..c6fec04 100644
--- a/reference/dfa_scores_plot.html
+++ b/reference/dfa_scores_plot.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="A scatter plot of the selected DFA components."><title>DFA scores plot — dfa_scores_plot • structToolbox</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="DFA scores plot — dfa_scores_plot"><meta property="og:description" content="A scatter plot of the selected DFA components."><!-- 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="A scatter plot of the selected DFA components."><title>DFA scores plot — dfa_scores_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="DFA scores plot — dfa_scores_plot"><meta property="og:description" content="A scatter plot of the selected DFA components."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -73,7 +73,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>points_to_label</dt>
-<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
+<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
+<li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
@@ -83,7 +84,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>ellipse</dt>
-<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"group"</code>: Hotelling T2 ellipses (p=0.95) are plotted for all groups.</p></li>
+<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Hotelling T2 ellipses (p=0.95) are plotted for all groups and all samples.</p></li>
+<li><p><code>"group"</code>: Hotelling T2 ellipses (p=0.95) are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
 <li><p><code>"sample"</code>: A Hotelling T2 ellipse (p=0.95) is plotted for all samples (ignoring group).</p></li>
 </ul><p>The default is <code>"all"</code>.</p></dd>
@@ -98,7 +100,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>label_size</dt>
-<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.</p></dd>
+<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -116,12 +118,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>ggplot2</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>scales</code></p></li>
+<li><p><code>ggplot2</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>dfa_scores_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>dfa_scores_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Wickham H, Seidel D (2022). <em>scales: Scale Functions for
-Visualization</em>. R package version 1.2.1,
+    <p>Wickham H, Pedersen T, Seidel D (2023). <em>scales: Scale Functions for
+Visualization</em>. R package version 1.3.0,
 <a href="https://CRAN.R-project.org/package=scales" class="external-link">https://CRAN.R-project.org/package=scales</a>.</p>
 <p>Wickham H (2016). <em>ggplot2: Elegant Graphics for Data Analysis</em>.
 Springer-Verlag New York. ISBN 978-3-319-24277-4,
@@ -130,7 +139,16 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">dfa_scores_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      components <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      points_to_label <span class="op">=</span> <span class="st">"none"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      ellipse <span class="op">=</span> <span class="st">"all"</span>,</span></span>
+<span class="r-in"><span>      label_filter <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      label_factor <span class="op">=</span> <span class="st">"rownames"</span>,</span></span>
+<span class="r-in"><span>      label_size <span class="op">=</span> <span class="fl">3.88</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="DFA.html">DFA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">dfa_scores_plot</span><span class="op">(</span>factor_name <span class="op">=</span> <span class="st">'Species'</span><span class="op">)</span></span></span>
diff --git a/reference/dratio_filter.html b/reference/dratio_filter.html
index dd5a397..cc222c6 100644
--- a/reference/dratio_filter.html
+++ b/reference/dratio_filter.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="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."><title>Dispersion ratio filter — dratio_filter • structToolbox</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="Dispersion ratio filter — dratio_filter"><meta property="og:description" content="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."><!-- 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="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."><title>Dispersion ratio filter — dratio_filter • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Dispersion ratio filter — dratio_filter"><meta property="og:description" content="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."><!-- 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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@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -67,7 +67,7 @@ <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>threshold</dt>
-<dd><p>(numeric) The threshold above which features are removed. The default is <code>20</code>.</p></dd>
+<dd><p>(numeric) The threshold above which features are removed. The default is <code>20</code>.<br></p></dd>
 
 
 <dt>qc_label</dt>
@@ -79,12 +79,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>method</dt>
-<dd><p>(character) dratio method. Allowed values are limited to the following:</p><ul><li><p><code>"euclidean"</code>: Dispersion of the QCs divided by the euclidean length of the total dispersion. Total dispersion is estimated from the QC and Sample dispersion by assuming that they are orthogonal. Corresponds to Eq 5 in Broadhurst et al (2018).</p></li>
+<dd><p>(character) dratio method. Allowed values are limited to the following:</p><ul><li><p><code>"ratio"</code>: Dispersion of the QCs divided by the dispersion of the samples. Corresponds to Eq 4 in  Broadhurst et al (2018).</p></li>
+<li><p><code>"euclidean"</code>: Dispersion of the QCs divided by the euclidean length of the total dispersion. Total dispersion is estimated from the QC and Sample dispersion by assuming that they are orthogonal. Corresponds to Eq 5 in Broadhurst et al (2018).</p></li>
 </ul><p>The default is <code>"ratio"</code>.</p></dd>
 
 
 <dt>dispersion</dt>
-<dd><p>(character) Dispersion method. Allowed values are limited to the following:</p><ul><li><p><code>"mad"</code>: Dispersion is estimated using the median absolute deviation.</p></li>
+<dd><p>(character) Dispersion method. Allowed values are limited to the following:</p><ul><li><p><code>"sd"</code>: Dispersion is estimated using the standard deviation.</p></li>
+<li><p><code>"mad"</code>: Dispersion is estimated using the median absolute deviation.</p></li>
 </ul><p>The default is <code>"sd"</code>.</p></dd>
 
 
@@ -97,6 +99,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>dratio_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) Flag indicating whether the feature was rejected by the filter or not.</td></tr><tr><td><code>d_ratio</code></td><td>(data.frame)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>dratio_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>dratio_filter()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR,
@@ -108,7 +116,14 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">dratio_filter</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">20</span>,</span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"ratio"</span>,</span></span>
+<span class="r-in"><span>      dispersion <span class="op">=</span> <span class="st">"sd"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">dratio_filter</span><span class="op">(</span>threshold<span class="op">=</span><span class="fl">20</span>,qc_label<span class="op">=</span><span class="st">'QC'</span>,factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/equal_split.html b/reference/equal_split.html
index 88be893..f3a321c 100644
--- a/reference/equal_split.html
+++ b/reference/equal_split.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="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."><title>Equal group sized sampling — equal_split • structToolbox</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="Equal group sized sampling — equal_split"><meta property="og:description" content="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."><!-- 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="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."><title>Equal group sized sampling — equal_split • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Equal group sized sampling — equal_split"><meta property="og:description" content="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>p_train</dt>
-<dd><p>(numeric) The proportion of samples selected for the training set. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric) The proportion of samples selected for the training set. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -76,10 +76,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>equal_split</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>equal_split</code> object inherits the following <code>struct</code> classes: <br><br><code>equal_split()</code> ⭢ <code><a href="split_data.html">split_data()</a></code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">equal_split</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      p_train <span class="op">=</span> <span class="fl">0.75</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">equal_split</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/feature_boxplot.html b/reference/feature_boxplot.html
index 02e0ce2..0d60242 100644
--- a/reference/feature_boxplot.html
+++ b/reference/feature_boxplot.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="A boxplot to visualise the distribution of values within a feature."><title>Feature boxplot — feature_boxplot • structToolbox</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="Feature boxplot — feature_boxplot"><meta property="og:description" content="A boxplot to visualise the distribution of values within a feature."><!-- 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="A boxplot to visualise the distribution of values within a feature."><title>Feature boxplot — feature_boxplot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Feature boxplot — feature_boxplot"><meta property="og:description" content="A boxplot to visualise the distribution of values within a feature."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -69,8 +69,9 @@ <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>label_outliers</dt>
-<dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: No labels are displayed.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The index for outlier samples is included on the plot.</p></li>
+<li><p><code>"FALSE"</code>: No labels are displayed.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>feature_to_plot</dt>
@@ -82,21 +83,23 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>show_counts</dt>
-<dd><p>(logical) Show counts. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: The number of samples for each box is not displayed.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Show counts. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The number of samples for each box is displayed.</p></li>
+<li><p><code>"FALSE"</code>: The number of samples for each box is not displayed.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>style</dt>
-<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"violin"</code>: Violon plot style.</p></li>
+<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"boxplot"</code>: Boxplot style.</p></li>
+<li><p><code>"violin"</code>: Violon plot style.</p></li>
 </ul><p>The default is <code>"boxplot"</code>.</p></dd>
 
 
 <dt>jitter</dt>
-<dd><p>(logical) Include points plotted with added jitter. The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Include points plotted with added jitter. The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>fill</dt>
-<dd><p>(logical) Block fill the boxes or violins with the group colour. The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Block fill the boxes or violins with the group colour. The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -112,10 +115,25 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>feature_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>feature_boxplot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="va">MTBLS79_DatasetExperiment</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">feature_boxplot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      label_outliers <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      feature_to_plot <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      show_counts <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      style <span class="op">=</span> <span class="st">"boxplot"</span>,</span></span>
+<span class="r-in"><span>      jitter <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      fill <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="va">MTBLS79_DatasetExperiment</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">feature_boxplot</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,feature_to_plot<span class="op">=</span><span class="st">'Petal.Width'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>no chart defined for "function"</span>
diff --git a/reference/feature_profile.html b/reference/feature_profile.html
index be740b1..bcba429 100644
--- a/reference/feature_profile.html
+++ b/reference/feature_profile.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="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><title>Feature profile — feature_profile • structToolbox</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="Feature profile — feature_profile"><meta property="og:description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><!-- 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="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><title>Feature profile — feature_profile • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Feature profile — feature_profile"><meta property="og:description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -88,8 +88,9 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>plot_sd</dt>
-<dd><p>(logical) Plot standard deviation. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Standard deviation is not plotted.</p></li>
-</ul><p>The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Plot standard deviation. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Standard deviation of samples and QCs are included on the plot.</p></li>
+<li><p><code>"FALSE"</code>: Standard deviation is not plotted.</p></li>
+</ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -105,10 +106,24 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>feature_profile</code> object inherits the following <code>struct</code> classes: <br><br><code>feature_profile()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">feature_profile</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      run_order <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      qc_column <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      colour_by <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      feature_to_plot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      plot_sd <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">feature_profile</span><span class="op">(</span>run_order<span class="op">=</span><span class="st">'run_order'</span>,</span></span>
 <span class="r-in"><span>    qc_label<span class="op">=</span><span class="st">'QC'</span>,</span></span>
 <span class="r-in"><span>    qc_column<span class="op">=</span><span class="st">'Class'</span>,</span></span>
diff --git a/reference/feature_profile_array.html b/reference/feature_profile_array.html
index 499c8dc..0f396ff 100644
--- a/reference/feature_profile_array.html
+++ b/reference/feature_profile_array.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="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><title>Feature profile — feature_profile_array • structToolbox</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="Feature profile — feature_profile_array"><meta property="og:description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><!-- 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="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><title>Feature profile — feature_profile_array • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Feature profile — feature_profile_array"><meta property="og:description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -89,12 +89,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>nrow</dt>
-<dd><p>(numeric, integer) The number of rows in the plot. The default is <code>5</code>.</p></dd>
+<dd><p>(numeric, integer) The number of rows in the plot. The default is <code>5</code>.<br></p></dd>
 
 
 <dt>log</dt>
-<dd><p>(logical) Log transform. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: The data is not transformed before plotting.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Log transform. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is log tranformed before plotting.</p></li>
+<li><p><code>"FALSE"</code>: The data is not transformed before plotting.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -110,10 +111,25 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>feature_profile_array</code> object inherits the following <code>struct</code> classes: <br><br><code>feature_profile_array()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">feature_profile_array</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      run_order <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      qc_column <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      colour_by <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      feature_to_plot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      nrow <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      log <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">feature_profile_array</span><span class="op">(</span></span></span>
 <span class="r-in"><span>    run_order<span class="op">=</span><span class="st">'run_order'</span>,</span></span>
 <span class="r-in"><span>    qc_label<span class="op">=</span><span class="st">'QC'</span>,</span></span>
diff --git a/reference/filter_by_name.html b/reference/filter_by_name.html
index 487c986..eae0444 100644
--- a/reference/filter_by_name.html
+++ b/reference/filter_by_name.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="Filter samples/variables by row/column name, index or logicals."><title>Filter by name — filter_by_name • structToolbox</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="Filter by name — filter_by_name"><meta property="og:description" content="Filter samples/variables by row/column name, index or logicals."><!-- 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="Filter samples/variables by row/column name, index or logicals."><title>Filter by name — filter_by_name • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Filter by name — filter_by_name"><meta property="og:description" content="Filter samples/variables by row/column name, index or logicals."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,10 +80,21 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>filter_by_name</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>filter_by_name</code> object inherits the following <code>struct</code> classes: <br><br><code>filter_by_name()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">filter_by_name</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      mode <span class="op">=</span> <span class="st">"exclude"</span>,</span></span>
+<span class="r-in"><span>      dimension <span class="op">=</span> <span class="st">"sample"</span>,</span></span>
+<span class="r-in"><span>      names <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">filter_by_name</span><span class="op">(</span>mode<span class="op">=</span><span class="st">'exclude'</span>,dimension<span class="op">=</span><span class="st">'variable'</span>,names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/filter_na_count.html b/reference/filter_na_count.html
index cb3f156..c77e785 100644
--- a/reference/filter_na_count.html
+++ b/reference/filter_na_count.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="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."><title>Minimum number of measured values filter — filter_na_count • structToolbox</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="Minimum number of measured values filter — filter_na_count"><meta property="og:description" content="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."><!-- 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="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."><title>Minimum number of measured values filter — filter_na_count • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Minimum number of measured values filter — filter_na_count"><meta property="og:description" content="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -76,10 +76,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>filter_na_count</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>count</code></td><td>(data.frame) The number of measured values in each group/interaction.</td></tr><tr><td><code>na_count</code></td><td>(data.frame) The number of missing values in each group/interaction.</td></tr><tr><td><code>flags</code></td><td>(data.frame) Flags to indicate which features were removed.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>filter_na_count</code> object inherits the following <code>struct</code> classes: <br><br><code>filter_na_count()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">filter_na_count</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">filter_na_count</span><span class="op">(</span>threshold<span class="op">=</span><span class="fl">3</span>,factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/filter_smeta.html b/reference/filter_smeta.html
index 87ac208..43ecb7b 100644
--- a/reference/filter_smeta.html
+++ b/reference/filter_smeta.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="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."><title>Filter by sample meta data — filter_smeta • structToolbox</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="Filter by sample meta data — filter_smeta"><meta property="og:description" content="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."><!-- 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="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."><title>Filter by sample meta data — filter_smeta • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Filter by sample meta data — filter_smeta"><meta property="og:description" content="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,8 @@ <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>mode</dt>
-<dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"exclude"</code>: Samples in the specified levels are excluded.</p></li>
+<dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"include"</code>: Samples in the specified levels are retained.</p></li>
+<li><p><code>"exclude"</code>: Samples in the specified levels are excluded.</p></li>
 </ul><p>The default is <code>"include"</code>.</p></dd>
 
 
@@ -81,10 +82,21 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>filter_smeta</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>filter_smeta</code> object inherits the following <code>struct</code> classes: <br><br><code>filter_smeta()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">filter_smeta</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      mode <span class="op">=</span> <span class="st">"include"</span>,</span></span>
+<span class="r-in"><span>      levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">filter_smeta</span><span class="op">(</span>mode<span class="op">=</span><span class="st">'exclude'</span>,levels<span class="op">=</span><span class="st">'QC'</span>,factor_name<span class="op">=</span><span class="st">'QC'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/fisher_exact.html b/reference/fisher_exact.html
index c31e275..d605d84 100644
--- a/reference/fisher_exact.html
+++ b/reference/fisher_exact.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="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."><title>Fisher Exact Test — fisher_exact • structToolbox</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="Fisher Exact Test — fisher_exact"><meta property="og:description" content="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."><!-- 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="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."><title>Fisher Exact Test — fisher_exact • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Fisher Exact Test — fisher_exact"><meta property="og:description" content="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -86,10 +87,22 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>fisher_exact</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>fisher_exact</code> object inherits the following <code>struct</code> classes: <br><br><code>fisher_exact()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># load some data</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">fisher_exact</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      factor_pred <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>id<span class="op">=</span><span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># load some data</span></span></span>
 <span class="r-in"><span><span class="va">D</span><span class="op">=</span><span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># prepare predictions based on NA</span></span></span>
diff --git a/reference/fold_change.html b/reference/fold_change.html
index cf263d5..3f52964 100644
--- a/reference/fold_change.html
+++ b/reference/fold_change.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="Fold change is the relative change in mean (or non-parametric equivalent) intensities of a feature between all pairs of levels in a factor."><title>Fold change — fold_change • structToolbox</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="Fold change — fold_change"><meta property="og:description" content="Fold change is the relative change in mean (or non-parametric equivalent) intensities of a feature between all pairs of levels in a factor."><!-- 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="Fold change is the relative change in mean (or non-parametric equivalent) intensities of a feature between all pairs of levels in a factor."><title>Fold change — fold_change • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Fold change — fold_change"><meta property="og:description" content="Fold change is the relative change in mean (or non-parametric equivalent) intensities of a feature between all pairs of levels in a factor."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -73,8 +73,9 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>paired</dt>
-<dd><p>(logical) Paired fold change. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Fold change is calculated assuming there is no paired sampling.</p></li>
-</ul><p>The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Paired fold change. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Fold change is calculated taking into account paired sampling.</p></li>
+<li><p><code>"FALSE"</code>: Fold change is calculated assuming there is no paired sampling.</p></li>
+</ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>sample_name</dt>
@@ -82,7 +83,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>threshold</dt>
-<dd><p>(numeric) The fold change threshold for labelling features as significant. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric) The fold change threshold for labelling features as significant. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>control_group</dt>
@@ -90,13 +91,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>method</dt>
-<dd><p>(character) Fold change method. Allowed values are limited to the following:</p><ul><li><p><code>"median"</code>: The group medians and the method described by Price and Bonett is used to estimate confidence intervals. For paired data standard error of the median is used to estimate confidence intervals from the median fold change of all pairs.</p></li>
+<dd><p>(character) Fold change method. Allowed values are limited to the following:</p><ul><li><p><code>"geometric"</code>: A log transform is applied before using group means to calculate fold change. In the non-tranformedspace this is equivalent to using geometric group means. Confidence intervals for independant and paired sampling are estimated using standard error of the mean in log transformed space before being transformed back to the original space.</p></li>
+<li><p><code>"median"</code>: The group medians and the method described by Price and Bonett is used to estimate confidence intervals. For paired data standard error of the median is used to estimate confidence intervals from the median fold change of all pairs.</p></li>
 <li><p><code>"mean"</code>: The group means and the method described by Price and Bonnet is used to estimate confidence intervals. For paired data standard error of the mean is used to estimate confidence intervals from the mean fold change of all pairs.</p></li>
 </ul><p>The default is <code>"geometric"</code>.</p></dd>
 
 
 <dt>conf_level</dt>
-<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -108,6 +110,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>fold_change</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>fold_change</code></td><td>(data.frame) The fold change between groups.</td></tr><tr><td><code>lower_ci</code></td><td>(data.frame) Lower confidence interval for fold change.</td></tr><tr><td><code>upper_ci</code></td><td>(data.frame) Upper confidence interval for fold change.</td></tr><tr><td><code>significant</code></td><td>(data.frame) A logical indictor of whether the calculated fold change including the estimated confidence limits is greater than the selected threshold.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>fold_change</code> object inherits the following <code>struct</code> classes: <br><br><code>fold_change()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Price Jr RM, Bonett DG (2020). "Confidence Intervals for Ratios of
@@ -117,7 +125,16 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">fold_change</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      sample_name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      paired <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      control_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"geometric"</span>,</span></span>
+<span class="r-in"><span>      conf_level <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">fold_change</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/fold_change_int.html b/reference/fold_change_int.html
index b30146b..0a261a6 100644
--- a/reference/fold_change_int.html
+++ b/reference/fold_change_int.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="For more than one factor the fold change calculation is extended to include all combinations of levels (interactions) of all factors. Paired fold changes are not possible for this computation."><title>Fold change for interactions between factors — fold_change_int • structToolbox</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="Fold change for interactions between factors — fold_change_int"><meta property="og:description" content="For more than one factor the fold change calculation is extended to include all combinations of levels (interactions) of all factors. Paired fold changes are not possible for this computation."><!-- 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="For more than one factor the fold change calculation is extended to include all combinations of levels (interactions) of all factors. Paired fold changes are not possible for this computation."><title>Fold change for interactions between factors — fold_change_int • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Fold change for interactions between factors — fold_change_int"><meta property="og:description" content="For more than one factor the fold change calculation is extended to include all combinations of levels (interactions) of all factors. Paired fold changes are not possible for this computation."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,7 +71,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>threshold</dt>
-<dd><p>(numeric) The fold change threshold for labelling features as significant. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric) The fold change threshold for labelling features as significant. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>control_group</dt>
@@ -79,13 +79,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>method</dt>
-<dd><p>(character) Fold change method. Allowed values are limited to the following:</p><ul><li><p><code>"median"</code>: The group medians and the method described by Price and Bonett is used to estimate confidence intervals. For paired data standard error of the median is used to estimate confidence intervals from the median fold change of all pairs.</p></li>
+<dd><p>(character) Fold change method. Allowed values are limited to the following:</p><ul><li><p><code>"geometric"</code>: A log transform is applied before using group means to calculate fold change. In the non-tranformedspace this is equivalent to using geometric group means. Confidence intervals for independant and paired sampling are estimated using standard error of the mean in log transformed space before being transformed back to the original space.</p></li>
+<li><p><code>"median"</code>: The group medians and the method described by Price and Bonett is used to estimate confidence intervals. For paired data standard error of the median is used to estimate confidence intervals from the median fold change of all pairs.</p></li>
 <li><p><code>"mean"</code>: The group means and the method described by Price and Bonnet is used to estimate confidence intervals. For paired data standard error of the mean is used to estimate confidence intervals from the mean fold change of all pairs.</p></li>
 </ul><p>The default is <code>"geometric"</code>.</p></dd>
 
 
 <dt>conf_level</dt>
-<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -97,6 +98,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>fold_change_int</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>fold_change</code></td><td>(data.frame) The fold change between groups.</td></tr><tr><td><code>lower_ci</code></td><td>(data.frame) Lower confidence interval for fold change.</td></tr><tr><td><code>upper_ci</code></td><td>(data.frame) Upper confidence interval for fold change.</td></tr><tr><td><code>significant</code></td><td>(data.frame) A logical indictor of whether the calculated fold change including the estimated confidence limits is greater than the selected threshold.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>fold_change_int</code> object inherits the following <code>struct</code> classes: <br><br><code>fold_change_int()</code> ⭢ <code><a href="fold_change.html">fold_change()</a></code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Lloyd GR, Jankevics A, Weber RJM (2020). "struct: an
@@ -107,7 +114,16 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">fold_change_int</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      sample_name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      control_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"geometric"</span>,</span></span>
+<span class="r-in"><span>      paired <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      conf_level <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">D</span><span class="op">=</span><span class="va">D</span><span class="op">[</span>,<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>,drop<span class="op">=</span><span class="cn">FALSE</span><span class="op">]</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="filter_smeta.html">filter_smeta</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'exclude'</span>,levels<span class="op">=</span><span class="st">'QC'</span>,factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span> <span class="op">+</span></span></span>
 <span class="r-in"><span>    <span class="fu">fold_change_int</span><span class="op">(</span>factor_name<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'Class'</span>,<span class="st">'Batch'</span><span class="op">)</span><span class="op">)</span></span></span>
diff --git a/reference/fold_change_plot.html b/reference/fold_change_plot.html
index 3b5b260..754ea38 100644
--- a/reference/fold_change_plot.html
+++ b/reference/fold_change_plot.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="A plot of fold changes calculated for a chosen subset of features. A predefined fold change threshold is indicated by shaded regions."><title>Fold change plot — fold_change_plot • structToolbox</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="Fold change plot — fold_change_plot"><meta property="og:description" content="A plot of fold changes calculated for a chosen subset of features. A predefined fold change threshold is indicated by shaded regions."><!-- 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="A plot of fold changes calculated for a chosen subset of features. A predefined fold change threshold is indicated by shaded regions."><title>Fold change plot — fold_change_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Fold change plot — fold_change_plot"><meta property="og:description" content="A plot of fold changes calculated for a chosen subset of features. A predefined fold change threshold is indicated by shaded regions."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,11 +61,12 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>number_features</dt>
 <dd><p>(numeric) The number randomly selected features to plot, or
-a list of column numbers. The default is <code>20</code>.</p></dd>
+a list of column numbers. The default is <code>20</code>.<br></p></dd>
 
 
 <dt>orientation</dt>
-<dd><p>(character) Plot orientation. Allowed values are limited to the following:</p><ul><li><p><code>"portrait"</code>: Features are plotted on the x-axis.</p></li>
+<dd><p>(character) Plot orientation. Allowed values are limited to the following:</p><ul><li><p><code>"landscape"</code>: Features are plotted on the y-axis.</p></li>
+<li><p><code>"portrait"</code>: Features are plotted on the x-axis.</p></li>
 </ul><p>The default is <code>"portrait"</code>.</p></dd>
 
 
@@ -82,10 +83,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>fold_change_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>fold_change_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">fold_change_plot</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">fold_change_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_features <span class="op">=</span> <span class="fl">10</span>,</span></span>
+<span class="r-in"><span>      orientation <span class="op">=</span> <span class="st">"portrait"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">fold_change_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/forward_selection_by_rank.html b/reference/forward_selection_by_rank.html
index 7436219..34d451a 100644
--- a/reference/forward_selection_by_rank.html
+++ b/reference/forward_selection_by_rank.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='A model is trained and performance metric computed by including increasing numbers of features in the model. The features to be included in each step are defined by their rank, which is computed from another variable e.g. VIP score. An "optimal"subset of features is suggested by minimising the input performance metric.'><title>Forward selection by rank — forward_selection_by_rank • structToolbox</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="Forward selection by rank — forward_selection_by_rank"><meta property="og:description" content='A model is trained and performance metric computed by including increasing numbers of features in the model. The features to be included in each step are defined by their rank, which is computed from another variable e.g. VIP score. An "optimal"subset of features is suggested by minimising the input performance metric.'><!-- 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='A model is trained and performance metric computed by including increasing numbers of features in the model. The features to be included in each step are defined by their rank, which is computed from another variable e.g. VIP score. An "optimal"subset of features is suggested by minimising the input performance metric.'><title>Forward selection by rank — forward_selection_by_rank • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Forward selection by rank — forward_selection_by_rank"><meta property="og:description" content='A model is trained and performance metric computed by including increasing numbers of features in the model. The features to be included in each step are defined by their rank, which is computed from another variable e.g. VIP score. An "optimal"subset of features is suggested by minimising the input performance metric.'><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -67,15 +67,15 @@ <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>min_no_vars</dt>
-<dd><p>(numeric) The minimum number of variables to include in the model. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric) The minimum number of variables to include in the model. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>max_no_vars</dt>
-<dd><p>(numeric) The maximum number of variables to include in the model. The default is <code>100</code>.</p></dd>
+<dd><p>(numeric) The maximum number of variables to include in the model. The default is <code>100</code>.<br></p></dd>
 
 
 <dt>step_size</dt>
-<dd><p>(numeric) The incremental change in number of features in the model. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric) The incremental change in number of features in the model. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -95,10 +95,23 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>forward_selection_by_rank</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>metric</code></td><td>(data.frame) The value of the computed metric for each model. For nested models the metric is averaged.</td></tr><tr><td><code>results</code></td><td>(data.frame) The predicted outputs from collated from all models computed during forward selection.</td></tr><tr><td><code>chosen_vars</code></td><td>(numeric, integer) The column number of the variables chosen for the best performing model.</td></tr><tr><td><code>smoothed</code></td><td>(numeric) The value of the performance metric for each evaluated model after smoothing.</td></tr><tr><td><code>searchlist</code></td><td>(numeric) The maxmimum rank of features included in each model.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>forward_selection_by_rank</code> object inherits the following <code>struct</code> classes: <br><br><code>forward_selection_by_rank()</code> ⭢ <code>resampler()</code> ⭢ <code>iterator()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># some data</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">forward_selection_by_rank</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      min_no_vars <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      max_no_vars <span class="op">=</span> <span class="fl">100</span>,</span></span>
+<span class="r-in"><span>      step_size <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      variable_rank <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># some data</span></span></span>
 <span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># normalise, impute and scale then remove QCs</span></span></span>
diff --git a/reference/fs_line.html b/reference/fs_line.html
index b3a525f..ed0c754 100644
--- a/reference/fs_line.html
+++ b/reference/fs_line.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="A line plot for forward selection. The computed model performance metric is plotted against the number of features included in the model."><title>Forward selection line plot — fs_line • structToolbox</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="Forward selection line plot — fs_line"><meta property="og:description" content="A line plot for forward selection. The computed model performance metric is plotted against the number of features included in the model."><!-- 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="A line plot for forward selection. The computed model performance metric is plotted against the number of features included in the model."><title>Forward selection line plot — fs_line • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Forward selection line plot — fs_line"><meta property="og:description" content="A line plot for forward selection. The computed model performance metric is plotted against the number of features included in the model."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>fs_line</code> object inherits the following <code>struct</code> classes: <br><br><code>fs_line()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># some data</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">fs_line</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># some data</span></span></span>
 <span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># normalise, impute and scale then remove QCs</span></span></span>
diff --git a/reference/glog_opt_plot.html b/reference/glog_opt_plot.html
index ef443b8..00d74ac 100644
--- a/reference/glog_opt_plot.html
+++ b/reference/glog_opt_plot.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="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."><title>Glog optimisation — glog_opt_plot • structToolbox</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="Glog optimisation — glog_opt_plot"><meta property="og:description" content="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."><!-- 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="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."><title>Glog optimisation — glog_opt_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Glog optimisation — glog_opt_plot"><meta property="og:description" content="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>plot_grid</dt>
-<dd><p>(numeric)  The default is <code>100</code>.</p></dd>
+<dd><p>(numeric)  The default is <code>100</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -80,17 +80,26 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>glog_opt_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>glog_opt_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">glog_opt_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      plot_grid <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="glog_transform.html">glog_transform</a></span><span class="op">(</span>qc_label<span class="op">=</span><span class="st">'versicolor'</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error!Lambda tending to infinity! Using standard</span>
diff --git a/reference/glog_transform.html b/reference/glog_transform.html
index 8ff88ae..ef61abb 100644
--- a/reference/glog_transform.html
+++ b/reference/glog_transform.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="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."><title>Generalised logarithmic transform — glog_transform • structToolbox</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="Generalised logarithmic transform — glog_transform"><meta property="og:description" content="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."><!-- 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="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."><title>Generalised logarithmic transform — glog_transform • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Generalised logarithmic transform — glog_transform"><meta property="og:description" content="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,12 +84,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>glog_transform</code> object inherits the following <code>struct</code> classes: <br><br><code>glog_transform()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
 <p>Durbin B, Hardin J, Hawkins D, Rocke D (2002). "A variance-stabilizing
 transformation for gene-expression microarray data." <em>Bioinformatics</em>,
 <em>18</em>(Suppl 1), S105-S110.</p>
@@ -101,7 +107,12 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">glog_transform</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      lambda <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">glog_transform</span><span class="op">(</span>qc_label<span class="op">=</span><span class="st">'versicolor'</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error!Lambda tending to infinity! Using standard</span>
diff --git a/reference/grid_search_1d.html b/reference/grid_search_1d.html
index b5e8da9..ac1add8 100644
--- a/reference/grid_search_1d.html
+++ b/reference/grid_search_1d.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='A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)'><title>One dimensional grid search — grid_search_1d • structToolbox</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="One dimensional grid search — grid_search_1d"><meta property="og:description" content='A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)'><!-- 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='A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)'><title>One dimensional grid search — grid_search_1d • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="One dimensional grid search — grid_search_1d"><meta property="og:description" content='A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)'><!-- 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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@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -83,7 +83,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>max_min</dt>
-<dd><p>(character) Maximise or minimise. Allowed values are limited to the following:</p><ul><li><p><code>"min"</code>: The optimium parameter value is suggested based on minimising the performance metric.</p></li>
+<dd><p>(character) Maximise or minimise. Allowed values are limited to the following:</p><ul><li><p><code>"max"</code>: The optimium parameter value is suggested based on maximising the performance metric.</p></li>
+<li><p><code>"min"</code>: The optimium parameter value is suggested based on minimising the performance metric.</p></li>
 </ul><p>The default is <code>"min"</code>.</p></dd>
 
 
@@ -96,10 +97,23 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>grid_search_1d</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>optimum_value</code></td><td>(numeric)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>grid_search_1d</code> object inherits the following <code>struct</code> classes: <br><br><code>grid_search_1d()</code> ⭢ <code>resampler()</code> ⭢ <code>iterator()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">grid_search_1d</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      param_to_optimise <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      search_values <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      model_index <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      max_min <span class="op">=</span> <span class="st">"min"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># some preprocessing</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="pqn_norm.html">pqn_norm</a></span><span class="op">(</span>qc_label<span class="op">=</span><span class="st">'QC'</span>,factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span> <span class="op">+</span></span></span>
 <span class="r-in"><span>    <span class="fu"><a href="knn_impute.html">knn_impute</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span></span>
diff --git a/reference/gs_line.html b/reference/gs_line.html
index 8d3a1a3..83137b3 100644
--- a/reference/gs_line.html
+++ b/reference/gs_line.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="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."><title>Grid search line plot — gs_line • structToolbox</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="Grid search line plot — gs_line"><meta property="og:description" content="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."><!-- 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="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."><title>Grid search line plot — gs_line • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Grid search line plot — gs_line"><meta property="og:description" content="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>gs_line</code> object inherits the following <code>struct</code> classes: <br><br><code>gs_line()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">gs_line</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">gs_line</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">gs_line</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/hca_dendrogram.html b/reference/hca_dendrogram.html
index 1a56668..bce5311 100644
--- a/reference/hca_dendrogram.html
+++ b/reference/hca_dendrogram.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="A dendrogram visualising the clustering by HCA."><title>HCA dendrogram — hca_dendrogram • structToolbox</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="HCA dendrogram — hca_dendrogram"><meta property="og:description" content="A dendrogram visualising the clustering by HCA."><!-- 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="A dendrogram visualising the clustering by HCA."><title>HCA dendrogram — hca_dendrogram • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="HCA dendrogram — hca_dendrogram"><meta property="og:description" content="A dendrogram visualising the clustering by HCA."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -76,6 +76,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>ggdendro</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>hca_dendrogram</code> object inherits the following <code>struct</code> classes: <br><br><code>hca_dendrogram()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>de Vries A, Ripley BD (2022). <em>ggdendro: Create Dendrograms and Tree
@@ -85,7 +91,9 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">hca_dendrogram</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">hca_dendrogram</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">hca_dendrogram</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/index.html b/reference/index.html
index 09ea8a5..27f1f83 100644
--- a/reference/index.html
+++ b/reference/index.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"><title>Function reference • structToolbox</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="Function reference"><!-- 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"><title>Function reference • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Function reference"><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/kfold_xval.html b/reference/kfold_xval.html
index e540880..5affc1d 100644
--- a/reference/kfold_xval.html
+++ b/reference/kfold_xval.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='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'><title>k-fold cross-validation — kfold_xval • structToolbox</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="k-fold cross-validation — kfold_xval"><meta property="og:description" content='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'><!-- 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='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'><title>k-fold cross-validation — kfold_xval • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="k-fold cross-validation — kfold_xval"><meta property="og:description" content='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>folds</dt>
-<dd><p>(numeric, integer) The number of cross-validation folds. The default is <code>10</code>.</p></dd>
+<dd><p>(numeric, integer) The number of cross-validation folds. The default is <code>10</code>.<br></p></dd>
 
 
 <dt>method</dt>
-<dd><p>(character) Fold selection method. Allowed values are limited to the following:</p><ul><li><p><code>"blocks"</code>: Blocks of adjacent samples are assigned to the same fold.</p></li>
+<dd><p>(character) Fold selection method. Allowed values are limited to the following:</p><ul><li><p><code>"venetian"</code>: Every nth sample is assigned to the same fold, where n is the number of folds.</p></li>
+<li><p><code>"blocks"</code>: Blocks of adjacent samples are assigned to the same fold.</p></li>
 <li><p><code>"random"</code>: Samples are randomly assigned to a fold.</p></li>
 </ul><p>The default is <code>"venetian"</code>.</p></dd>
 
@@ -86,10 +87,22 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>kfold_xval</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr><tr><td><code>metric.test</code></td><td>(numeric)</td></tr><tr><td><code>collected</code></td><td>(list)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>kfold_xval</code> object inherits the following <code>struct</code> classes: <br><br><code>kfold_xval()</code> ⭢ <code>resampler()</code> ⭢ <code>iterator()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">kfold_xval</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      folds <span class="op">=</span> <span class="fl">5</span>,</span></span>
+<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"random"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      collect <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu">kfold_xval</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span> <span class="op">*</span></span></span>
 <span class="r-in"><span>    <span class="op">(</span><span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu"><a href="run.html">run</a></span><span class="op">(</span><span class="va">I</span>,<span class="va">D</span>,<span class="fu"><a href="balanced_accuracy.html">balanced_accuracy</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
diff --git a/reference/kfoldxcv_grid.html b/reference/kfoldxcv_grid.html
index e296491..590541d 100644
--- a/reference/kfoldxcv_grid.html
+++ b/reference/kfoldxcv_grid.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="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."><title>k-fold cross validation plot — kfoldxcv_grid • structToolbox</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="k-fold cross validation plot — kfoldxcv_grid"><meta property="og:description" content="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."><!-- 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="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."><title>k-fold cross validation plot — kfoldxcv_grid • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="k-fold cross validation plot — kfoldxcv_grid"><meta property="og:description" content="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,10 +80,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>kfoldxcv_grid</code> object inherits the following <code>struct</code> classes: <br><br><code>kfoldxcv_grid()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">kfoldxcv_grid</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      level <span class="op">=</span> <span class="st">"level_1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu"><a href="kfold_xval.html">kfold_xval</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span> <span class="op">*</span></span></span>
 <span class="r-in"><span>    <span class="op">(</span><span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu"><a href="run.html">run</a></span><span class="op">(</span><span class="va">I</span>,<span class="va">D</span>,<span class="fu"><a href="balanced_accuracy.html">balanced_accuracy</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
diff --git a/reference/kfoldxcv_metric.html b/reference/kfoldxcv_metric.html
index eaf85ec..e7e7fe3 100644
--- a/reference/kfoldxcv_metric.html
+++ b/reference/kfoldxcv_metric.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="A boxplot of the performance metric computed for each fold of a k-fold cross-validation."><title>kfoldxcv metric plot — kfoldxcv_metric • structToolbox</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="kfoldxcv metric plot — kfoldxcv_metric"><meta property="og:description" content="A boxplot of the performance metric computed for each fold of a k-fold cross-validation."><!-- 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="A boxplot of the performance metric computed for each fold of a k-fold cross-validation."><title>kfoldxcv metric plot — kfoldxcv_metric • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="kfoldxcv metric plot — kfoldxcv_metric"><meta property="og:description" content="A boxplot of the performance metric computed for each fold of a k-fold cross-validation."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>kfoldxcv_metric</code> object inherits the following <code>struct</code> classes: <br><br><code>kfoldxcv_metric()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">kfoldxcv_metric</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">kfoldxcv_metric</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">kfoldxcv_metric</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/knn_impute.html b/reference/knn_impute.html
index 79dab0c..3a52c62 100644
--- a/reference/knn_impute.html
+++ b/reference/knn_impute.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="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"><title>kNN missing value imputation — knn_impute • structToolbox</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="kNN missing value imputation — knn_impute"><meta property="og:description" content="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"><!-- 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="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"><title>kNN missing value imputation — knn_impute • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="kNN missing value imputation — knn_impute"><meta property="og:description" content="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -66,15 +66,15 @@ <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>neighbours</dt>
-<dd><p>(numeric) The number of neighbours (k) to use for imputation. The default is <code>5</code>.</p></dd>
+<dd><p>(numeric) The number of neighbours (k) to use for imputation. The default is <code>5</code>.<br></p></dd>
 
 
 <dt>sample_max</dt>
-<dd><p>(numeric) The maximum percent missing values per sample. The default is <code>50</code>.</p></dd>
+<dd><p>(numeric) The maximum percent missing values per sample. The default is <code>50</code>.<br></p></dd>
 
 
 <dt>feature_max</dt>
-<dd><p>(numeric) The maximum percent missing values per feature. The default is <code>50</code>.</p></dd>
+<dd><p>(numeric) The maximum percent missing values per feature. The default is <code>50</code>.<br></p></dd>
 
 
 <dt>by</dt>
@@ -94,17 +94,29 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>knn_impute</code> object inherits the following <code>struct</code> classes: <br><br><code>knn_impute()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">knn_impute</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">knn_impute</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      neighbours <span class="op">=</span> <span class="fl">5</span>,</span></span>
+<span class="r-in"><span>      feature_max <span class="op">=</span> <span class="fl">50</span>,</span></span>
+<span class="r-in"><span>      sample_max <span class="op">=</span> <span class="fl">50</span>,</span></span>
+<span class="r-in"><span>      by <span class="op">=</span> <span class="st">"features"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">knn_impute</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/kw_p_hist.html b/reference/kw_p_hist.html
index 0c8c46f..5db4d5d 100644
--- a/reference/kw_p_hist.html
+++ b/reference/kw_p_hist.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="A histogram of the p-values computed by the kruskal-wallis method"><title>Histogram of p values — kw_p_hist • structToolbox</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="Histogram of p values — kw_p_hist"><meta property="og:description" content="A histogram of the p-values computed by the kruskal-wallis method"><!-- 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="A histogram of the p-values computed by the kruskal-wallis method"><title>Histogram of p values — kw_p_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Histogram of p values — kw_p_hist"><meta property="og:description" content="A histogram of the p-values computed by the kruskal-wallis method"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>kw_p_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>kw_p_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">kw_p_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">kw_p_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">kw_p_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/kw_rank_sum.html b/reference/kw_rank_sum.html
index ceda38d..2e2f0c0 100644
--- a/reference/kw_rank_sum.html
+++ b/reference/kw_rank_sum.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="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."><title>Kruskal-Wallis rank sum test — kw_rank_sum • structToolbox</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="Kruskal-Wallis rank sum test — kw_rank_sum"><meta property="og:description" content="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."><!-- 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="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."><title>Kruskal-Wallis rank sum test — kw_rank_sum • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Kruskal-Wallis rank sum test — kw_rank_sum"><meta property="og:description" content="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -82,10 +83,21 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>kw_rank_sum</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>test_statistic</code></td><td>(data.frame) The value of the calculated statistic which is converted to a p-value when compared to a chi2-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>estimates</code></td><td>(data.frame)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>kw_rank_sum</code> object inherits the following <code>struct</code> classes: <br><br><code>kw_rank_sum()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">kw_rank_sum</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_names <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">kw_rank_sum</span><span class="op">(</span>factor_names<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/linear_model.html b/reference/linear_model.html
index c47d21c..8d8a754 100644
--- a/reference/linear_model.html
+++ b/reference/linear_model.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="Linear models can be used to carry out regression, single stratum analysis of variance and analysis of covariance."><title>Linear model — linear_model • structToolbox</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="Linear model — linear_model"><meta property="og:description" content="Linear models can be used to carry out regression, single stratum analysis of variance and analysis of covariance."><!-- 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="Linear models can be used to carry out regression, single stratum analysis of variance and analysis of covariance."><title>Linear model — linear_model • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Linear model — linear_model"><meta property="og:description" content="Linear models can be used to carry out regression, single stratum analysis of variance and analysis of covariance."><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,14 +64,15 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>na_action</dt>
-<dd><p>(character) NA action. Allowed values are limited to the following:</p><ul><li><p><code>"na.fail"</code>: An error is thrown if NA are present.</p></li>
+<dd><p>(character) NA action. Allowed values are limited to the following:</p><ul><li><p><code>"na.omit"</code>: Incomplete cases are removed.</p></li>
+<li><p><code>"na.fail"</code>: An error is thrown if NA are present.</p></li>
 <li><p><code>"na.exclude"</code>: Incomplete cases are removed, and the output result is padded to the correct size using NA.</p></li>
 <li><p><code>"na.pass"</code>: Does not apply a linear model if NA are present.</p></li>
 </ul><p>The default is <code>"na.omit"</code>.</p></dd>
 
 
 <dt>contrasts</dt>
-<dd><p>(list) The contrasts associated with a factor. The default is <code>list</code>.</p></dd>
+<dd><p>(list) The contrasts associated with a factor. The default is <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>.</p></dd>
 
 
 <dt>...</dt>
@@ -87,6 +88,12 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>stats</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>linear_model</code> object inherits the following <code>struct</code> classes: <br><br><code>linear_model()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>R Core Team (2023). <em>R: A Language and Environment for Statistical
@@ -96,7 +103,12 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">linear_model</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span></span>
+<span class="r-in"><span>      na_action <span class="op">=</span> <span class="st">"na.omit"</span>,</span></span>
+<span class="r-in"><span>      contrasts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">linear_model</span><span class="op">(</span>formula <span class="op">=</span> <span class="va">y</span><span class="op">~</span><span class="va">Species</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
diff --git a/reference/log_transform.html b/reference/log_transform.html
index 1a7df0b..2d7b22f 100644
--- a/reference/log_transform.html
+++ b/reference/log_transform.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="A logarithmic transform is applied to all values in the data matrix."><title>logarithm transform — log_transform • structToolbox</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="logarithm transform — log_transform"><meta property="og:description" content="A logarithmic transform is applied to all values in the data matrix."><!-- 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="A logarithmic transform is applied to all values in the data matrix."><title>logarithm transform — log_transform • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="logarithm transform — log_transform"><meta property="og:description" content="A logarithmic transform is applied to all values in the data matrix."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>base</dt>
-<dd><p>(numeric) The base of the logarithm used for the transform. The default is <code>10</code>.</p></dd>
+<dd><p>(numeric) The base of the logarithm used for the transform. The default is <code>10</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -73,10 +73,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>A  <code>log_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the log transformed data.</td></tr></table><p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>log_transform</code> object inherits the following <code>struct</code> classes: <br><br><code>log_transform()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">log_transform</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">log_transform</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      base <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">log_transform</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/mean_centre.html b/reference/mean_centre.html
index 92baa57..7e63a07 100644
--- a/reference/mean_centre.html
+++ b/reference/mean_centre.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="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."><title>Mean centre — mean_centre • structToolbox</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="Mean centre — mean_centre"><meta property="og:description" content="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."><!-- 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="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."><title>Mean centre — mean_centre • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Mean centre — mean_centre"><meta property="og:description" content="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,8 @@ <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>mode</dt>
-<dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"sample_meta"</code>: Centring is applied to the sample_meta block.</p></li>
+<dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"data"</code>: Centring is applied to the data block.</p></li>
+<li><p><code>"sample_meta"</code>: Centring is applied to the sample_meta block.</p></li>
 <li><p><code>"both"</code>: Centring is applied to both the data and the sample_meta blocks.</p></li>
 </ul><p>The default is <code>"data"</code>.</p></dd>
 
@@ -74,10 +75,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>mean_centre</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>centred</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean_data</code></td><td>(numeric)</td></tr><tr><td><code>mean_sample_meta</code></td><td>(numeric)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mean_centre</code> object inherits the following <code>struct</code> classes: <br><br><code>mean_centre()</code> ⭢ <code>preprocess()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mean_centre</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mean_centre</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      mode <span class="op">=</span> <span class="st">"data"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mean_centre</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/mean_of_medians.html b/reference/mean_of_medians.html
index c2a36ff..0b59a62 100644
--- a/reference/mean_of_medians.html
+++ b/reference/mean_of_medians.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="The data matrix is normalised by the mean of the median of each factor level."><title>Mean of medians — mean_of_medians • structToolbox</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="Mean of medians — mean_of_medians"><meta property="og:description" content="The data matrix is normalised by the mean of the median of each factor level."><!-- 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="The data matrix is normalised by the mean of the median of each factor level."><title>Mean of medians — mean_of_medians • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Mean of medians — mean_of_medians"><meta property="og:description" content="The data matrix is normalised by the mean of the median of each factor level."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>mean_of_medians</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) Data after the tranformation has been applied.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mean_of_medians</code> object inherits the following <code>struct</code> classes: <br><br><code>mean_of_medians()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mean_of_medians</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mean_of_medians</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/mixed_effect.html b/reference/mixed_effect.html
index 0d8bb59..1252949 100644
--- a/reference/mixed_effect.html
+++ b/reference/mixed_effect.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="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."><title>Mixed effects model — mixed_effect • structToolbox</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="Mixed effects model — mixed_effect"><meta property="og:description" content="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."><!-- 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="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."><title>Mixed effects model — mixed_effect • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Mixed effects model — mixed_effect"><meta property="og:description" content="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -74,7 +75,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>ss_type</dt>
-<dd><p>(character) Sum of squares type. Allowed values are limited to the following:</p><ul><li><p><code>"sequential"</code>: Type II sum of squares.</p></li>
+<dd><p>(character) Sum of squares type. Allowed values are limited to the following:</p><ul><li><p><code>"marginal"</code>: Type III sum of squares.</p></li>
+<li><p><code>"sequential"</code>: Type II sum of squares.</p></li>
 </ul><p>The default is <code>"marginal"</code>.</p></dd>
 
 
@@ -89,18 +91,25 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <p>A  <code>mixed_effect</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>f_statistic</code></td><td>(data.frame) The value of the calculated statistic.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>emmeans</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>nlme</code></p></li>
+<li><p><code>emmeans</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mixed_effect</code> object inherits the following <code>struct</code> classes: <br><br><code>mixed_effect()</code> ⭢ <code><a href="ANOVA.html">ANOVA()</a></code> ⭢ <code>model()</code> ⭢ <code>stato()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Pinheiro J, Bates D, R Core Team (2023). <em>nlme: Linear and Nonlinear
-Mixed Effects Models</em>. R package version 3.1-162,
+Mixed Effects Models</em>. R package version 3.1-164,
 <a href="https://CRAN.R-project.org/package=nlme" class="external-link">https://CRAN.R-project.org/package=nlme</a>.</p>
 <p>Pinheiro JC, Bates DM (2000). <em>Mixed-Effects Models in S and S-PLUS</em>.
 Springer, New York. doi:10.1007/b98882
 <a href="https://doi.org/10.1007/b98882" class="external-link">https://doi.org/10.1007/b98882</a>.</p>
 <p>Lenth R (2023). <em>emmeans: Estimated Marginal Means, aka Least-Squares
-Means</em>. R package version 1.8.7,
+Means</em>. R package version 1.9.0,
 <a href="https://CRAN.R-project.org/package=emmeans" class="external-link">https://CRAN.R-project.org/package=emmeans</a>.</p>
 <p>Fox J, Weisberg S (2019). <em>An R Companion to Applied Regression</em>, Third
 edition. Sage, Thousand Oaks CA.
@@ -109,7 +118,13 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mixed_effect</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span></span>
+<span class="r-in"><span>      ss_type <span class="op">=</span> <span class="st">"marginal"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">D</span><span class="op">$</span><span class="va">sample_meta</span><span class="op">$</span><span class="va">id</span><span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">D</span><span class="op">)</span> <span class="co"># dummy id column</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mixed_effect</span><span class="op">(</span>formula <span class="op">=</span> <span class="va">y</span><span class="op">~</span><span class="va">Species</span><span class="op">+</span> <span class="fu">Error</span><span class="op">(</span><span class="va">id</span><span class="op">/</span><span class="va">Species</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
diff --git a/reference/model_apply.html b/reference/model_apply.html
index 79d0358..a665460 100644
--- a/reference/model_apply.html
+++ b/reference/model_apply.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="Applies method to the input DatasetExperiment"><title>Apply method — model_apply,ANOVA,DatasetExperiment-method • structToolbox</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="Apply method — model_apply,ANOVA,DatasetExperiment-method"><meta property="og:description" content="Applies method to the input DatasetExperiment"><!-- 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="Applies method to the input DatasetExperiment"><title>Apply method — model_apply,ANOVA,DatasetExperiment-method • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Apply method — model_apply,ANOVA,DatasetExperiment-method"><meta property="og:description" content="Applies method to the input DatasetExperiment"><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/model_predict.html b/reference/model_predict.html
index 5ce0f0e..58308ce 100644
--- a/reference/model_predict.html
+++ b/reference/model_predict.html
@@ -1,6 +1,6 @@
 <!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="Apply a model using the input DatasetExperiment. Assumes the model is trained
-first."><title>Model prediction — model_predict,DFA,DatasetExperiment-method • structToolbox</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="Model prediction — model_predict,DFA,DatasetExperiment-method"><meta property="og:description" content="Apply a model using the input DatasetExperiment. Assumes the model is trained
+first."><title>Model prediction — model_predict,DFA,DatasetExperiment-method • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Model prediction — model_predict,DFA,DatasetExperiment-method"><meta property="og:description" content="Apply a model using the input DatasetExperiment. Assumes the model is trained
 first."><!-- 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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@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/model_reverse.html b/reference/model_reverse.html
index e60ca44..52fdcb5 100644
--- a/reference/model_reverse.html
+++ b/reference/model_reverse.html
@@ -1,5 +1,5 @@
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+<!-- 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="Reverse the effect of a preprocessing step on a DatasetExperiment."><title>Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method"><meta property="og:description" content="Reverse the effect of a preprocessing step on a DatasetExperiment."><!-- 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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@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/model_train.html b/reference/model_train.html
index 3629c7a..bf6de04 100644
--- a/reference/model_train.html
+++ b/reference/model_train.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- 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="Trains a model using the input DatasetExperiment"><title>Train a model — model_train,DFA,DatasetExperiment-method • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Train a model — model_train,DFA,DatasetExperiment-method"><meta property="og:description" content="Trains a model using the input DatasetExperiment"><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mv_boxplot.html b/reference/mv_boxplot.html
index 64591c7..a86f350 100644
--- a/reference/mv_boxplot.html
+++ b/reference/mv_boxplot.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="Boxplots of the number of missing values per sample/feature."><title>Missing value boxplots — mv_boxplot • structToolbox</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="Missing value boxplots — mv_boxplot"><meta property="og:description" content="Boxplots of the number of missing values per sample/feature."><!-- 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="Boxplots of the number of missing values per sample/feature."><title>Missing value boxplots — mv_boxplot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Missing value boxplots — mv_boxplot"><meta property="og:description" content="Boxplots of the number of missing values per sample/feature."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -66,13 +66,15 @@ <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>label_outliers</dt>
-<dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Sample labels are not included on the plot.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Sample labels for potential outliers are displayed on the plot.</p></li>
+<li><p><code>"FALSE"</code>: Sample labels are not included on the plot.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>by_sample</dt>
-<dd><p>(logical) Plot by sample or by feature. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Missing values are plotted per feature.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Plot by sample or by feature. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Missing values are plotted per sample.</p></li>
+<li><p><code>"FALSE"</code>: Missing values are plotted per feature.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -80,8 +82,9 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>show_counts</dt>
-<dd><p>(logical) Show counts. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: The number of samples for each box is not displayed.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Show counts. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The number of samples for each box is displayed.</p></li>
+<li><p><code>"FALSE"</code>: The number of samples for each box is not displayed.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -97,10 +100,22 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mv_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>mv_boxplot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mv_boxplot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      label_outliers <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      by_sample <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      show_counts <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_boxplot</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="mv_boxplot-1.png" alt="" width="700" height="433"></span>
diff --git a/reference/mv_feature_filter.html b/reference/mv_feature_filter.html
index 670874a..c5f4f55 100644
--- a/reference/mv_feature_filter.html
+++ b/reference/mv_feature_filter.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="Removes features where the percentage of non-missing values falls below a threshold."><title>Filter features by missing values — mv_feature_filter • structToolbox</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="Filter features by missing values — mv_feature_filter"><meta property="og:description" content="Removes features where the percentage of non-missing values falls below a threshold."><!-- 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="Removes features where the percentage of non-missing values falls below a threshold."><title>Filter features by missing values — mv_feature_filter • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Filter features by missing values — mv_feature_filter"><meta property="og:description" content="Removes features where the percentage of non-missing values falls below a threshold."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -66,7 +66,7 @@ <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>threshold</dt>
-<dd><p>(numeric) The minimum percentage of non-missing values. The default is <code>20</code>.</p></dd>
+<dd><p>(numeric) The minimum percentage of non-missing values. The default is <code>20</code>.<br></p></dd>
 
 
 <dt>qc_label</dt>
@@ -74,7 +74,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>method</dt>
-<dd><p>(character) Filtering method. Allowed values are limited to the following:</p><ul><li><p><code>"within_one"</code>: Features are removed if the threshold for non-missing values is not met for at least one group.</p></li>
+<dd><p>(character) Filtering method. Allowed values are limited to the following:</p><ul><li><p><code>"within_all"</code>: Features are removed if the threshold for non-missing values is not met for all groups.</p></li>
+<li><p><code>"within_one"</code>: Features are removed if the threshold for non-missing values is not met for any group.</p></li>
 <li><p><code>"QC"</code>: Features are removed if the threshold for non-missing values is not met for the named group.</p></li>
 <li><p><code>"across"</code>: The filter is applied ignoring sample group.</p></li>
 </ul><p>The default is <code>"QC"</code>.</p></dd>
@@ -97,17 +98,29 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mv_feature_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>mv_feature_filter()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mv_feature_filter</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">20</span>,</span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mv_feature_filter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,qc_label<span class="op">=</span><span class="st">'versicolor'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
diff --git a/reference/mv_feature_filter_hist.html b/reference/mv_feature_filter_hist.html
index 3864233..0049e5e 100644
--- a/reference/mv_feature_filter_hist.html
+++ b/reference/mv_feature_filter_hist.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="A histogram of the proportion of missing values per feature."><title>Histogram of missing values per feature — mv_feature_filter_hist • structToolbox</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="Histogram of missing values per feature — mv_feature_filter_hist"><meta property="og:description" content="A histogram of the proportion of missing values per feature."><!-- 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="A histogram of the proportion of missing values per feature."><title>Histogram of missing values per feature — mv_feature_filter_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Histogram of missing values per feature — mv_feature_filter_hist"><meta property="og:description" content="A histogram of the proportion of missing values per feature."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mv_feature_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>mv_feature_filter_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_feature_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mv_feature_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_feature_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/mv_histogram.html b/reference/mv_histogram.html
index 09ccc3e..2fdc086 100644
--- a/reference/mv_histogram.html
+++ b/reference/mv_histogram.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="A histogram of the numbers of missing values per sample/feature"><title>Missing value histogram — mv_histogram • structToolbox</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="Missing value histogram — mv_histogram"><meta property="og:description" content="A histogram of the numbers of missing values per sample/feature"><!-- 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="A histogram of the numbers of missing values per sample/feature"><title>Missing value histogram — mv_histogram • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Missing value histogram — mv_histogram"><meta property="og:description" content="A histogram of the numbers of missing values per sample/feature"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,13 +60,15 @@ <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>label_outliers</dt>
-<dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Sample labels are not included on the plot.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Sample labels for potential outliers are displayed on the plot.</p></li>
+<li><p><code>"FALSE"</code>: Sample labels are not included on the plot.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>by_sample</dt>
-<dd><p>(logical) Plot by sample or by feature. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Missing values are plotted per feature.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Plot by sample or by feature. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Missing values are plotted per sample.</p></li>
+<li><p><code>"FALSE"</code>: Missing values are plotted per feature.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -85,10 +87,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mv_histogram</code> object inherits the following <code>struct</code> classes: <br><br><code>mv_histogram()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mv_histogram</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      label_outliers <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      by_sample <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_histogram</span><span class="op">(</span>label_outliers<span class="op">=</span><span class="cn">FALSE</span>,by_sample<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</span>
diff --git a/reference/mv_sample_filter.html b/reference/mv_sample_filter.html
index e8c2b1b..3a6dee5 100644
--- a/reference/mv_sample_filter.html
+++ b/reference/mv_sample_filter.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="Removes samples where the percent number of missing values exceeds a threshold."><title>Missing value sample filter — mv_sample_filter • structToolbox</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="Missing value sample filter — mv_sample_filter"><meta property="og:description" content="Removes samples where the percent number of missing values exceeds a threshold."><!-- 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="Removes samples where the percent number of missing values exceeds a threshold."><title>Missing value sample filter — mv_sample_filter • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Missing value sample filter — mv_sample_filter"><meta property="og:description" content="Removes samples where the percent number of missing values exceeds a threshold."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>mv_threshold</dt>
-<dd><p>(numeric) The maximum percentage of features with missing values in a sample. The default is <code>20</code>.</p></dd>
+<dd><p>(numeric) The maximum percentage of features with missing values in a sample. The default is <code>20</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -76,17 +76,26 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mv_sample_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>mv_sample_filter()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_sample_filter</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mv_sample_filter</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      mv_threshold <span class="op">=</span> <span class="fl">20</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_sample_filter</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/mv_sample_filter_hist.html b/reference/mv_sample_filter_hist.html
index 4931220..c2aac73 100644
--- a/reference/mv_sample_filter_hist.html
+++ b/reference/mv_sample_filter_hist.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="A histogram of the the proportion of missing values per sample"><title>Histogram of missing values per sample — mv_sample_filter_hist • structToolbox</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="Histogram of missing values per sample — mv_sample_filter_hist"><meta property="og:description" content="A histogram of the the proportion of missing values per sample"><!-- 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="A histogram of the the proportion of missing values per sample"><title>Histogram of missing values per sample — mv_sample_filter_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Histogram of missing values per sample — mv_sample_filter_hist"><meta property="og:description" content="A histogram of the the proportion of missing values per sample"><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>mv_sample_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>mv_sample_filter_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_sample_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mv_sample_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_sample_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/nroot_transform.html b/reference/nroot_transform.html
index f67fcb2..a0a0840 100644
--- a/reference/nroot_transform.html
+++ b/reference/nroot_transform.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="All values in the data matrix are transformed by raising them to the power of 1/n."><title>nth root transform — nroot_transform • structToolbox</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="nth root transform — nroot_transform"><meta property="og:description" content="All values in the data matrix are transformed by raising them to the power of 1/n."><!-- 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="All values in the data matrix are transformed by raising them to the power of 1/n."><title>nth root transform — nroot_transform • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="nth root transform — nroot_transform"><meta property="og:description" content="All values in the data matrix are transformed by raising them to the power of 1/n."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>root</dt>
-<dd><p>(numeric) The nth root used for the transform. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric) The nth root used for the transform. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -72,10 +72,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>nroot_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the nth root transformed data.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>nroot_transform</code> object inherits the following <code>struct</code> classes: <br><br><code>nroot_transform()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">nroot_transform</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">nroot_transform</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      root <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">nroot_transform</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/ontology_cache.html b/reference/ontology_cache.html
index 86225eb..1f38a25 100644
--- a/reference/ontology_cache.html
+++ b/reference/ontology_cache.html
@@ -1,6 +1,6 @@
 <!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="A cached list of ontology terms obtained from the ontology lookup service
-(OLS) for ontology terms specified for objects in structToolbox."><title>ontology cache — ontology_cache • structToolbox</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="ontology cache — ontology_cache"><meta property="og:description" content="A cached list of ontology terms obtained from the ontology lookup service
+(OLS) for ontology terms specified for objects in structToolbox."><title>ontology cache — ontology_cache • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="ontology cache — ontology_cache"><meta property="og:description" content="A cached list of ontology terms obtained from the ontology lookup service
 (OLS) for ontology terms specified for objects in structToolbox."><!-- 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>
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/pairs_filter.html b/reference/pairs_filter.html
index f489aa8..61a12e5 100644
--- a/reference/pairs_filter.html
+++ b/reference/pairs_filter.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="This filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."><title>Pairs filter — pairs_filter • structToolbox</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="Pairs filter — pairs_filter"><meta property="og:description" content="This filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."><!-- 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 filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."><title>Pairs filter — pairs_filter • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Pairs filter — pairs_filter"><meta property="og:description" content="This filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -77,10 +77,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>A  <code>pairs_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object after the filter has been applied.</td></tr><tr><td><code>flags</code></td><td>(data.frame) A data.frame indicating whether features were filtered from the DatasetExperiment.</td></tr></table><p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pairs_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>pairs_filter()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span><span class="op">=</span><span class="fu">pairs_filter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span>,sample_id<span class="op">=</span><span class="st">'ids'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pairs_filter</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      sample_id <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span><span class="op">=</span><span class="fu">pairs_filter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span>,sample_id<span class="op">=</span><span class="st">'ids'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/pareto_scale.html b/reference/pareto_scale.html
index 3ff0e57..21ad654 100644
--- a/reference/pareto_scale.html
+++ b/reference/pareto_scale.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="The mean sample is subtracted from all samples and then scaled by the square root of the standard deviation. The transformed data has zero mean."><title>Pareto scaling — pareto_scale • structToolbox</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="Pareto scaling — pareto_scale"><meta property="og:description" content="The mean sample is subtracted from all samples and then scaled by the square root of the standard deviation. The transformed data has zero mean."><!-- 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="The mean sample is subtracted from all samples and then scaled by the square root of the standard deviation. The transformed data has zero mean."><title>Pareto scaling — pareto_scale • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Pareto scaling — pareto_scale"><meta property="og:description" content="The mean sample is subtracted from all samples and then scaled by the square root of the standard deviation. The transformed data has zero mean."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -68,10 +68,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>pareto_scale</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scaled</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean</code></td><td>(numeric)</td></tr><tr><td><code>sd</code></td><td>(numeric)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pareto_scale</code> object inherits the following <code>struct</code> classes: <br><br><code>pareto_scale()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pareto_scale</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pareto_scale</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_train.html">model_train</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_predict.html">model_predict</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
diff --git a/reference/pca_biplot.html b/reference/pca_biplot.html
index 22c3ea8..44b8a41 100644
--- a/reference/pca_biplot.html
+++ b/reference/pca_biplot.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="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."><title>PCA biplot — pca_biplot • structToolbox</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="PCA biplot — pca_biplot"><meta property="og:description" content="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."><!-- 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="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."><title>PCA biplot — pca_biplot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PCA biplot — pca_biplot"><meta property="og:description" content="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,7 +72,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>points_to_label</dt>
-<dd><p>(character) points_to_label. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: All samples are labelled on the plot.</p></li>
+<dd><p>(character) points_to_label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples are labelled on the plot.</p></li>
+<li><p><code>"all"</code>: All samples are labelled on the plot.</p></li>
 <li><p><code>"outliers"</code>: Potential outliers are labelled on the plot.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
@@ -82,17 +83,19 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>scale_factor</dt>
-<dd><p>(numeric) The scaling factor applied to the loadings. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The scaling factor applied to the loadings. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>style</dt>
-<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"arrows"</code>: The loadings are plotted as arrow vectors.</p></li>
+<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"points"</code>: Loadings and scores are plotted as a scatter plot.</p></li>
+<li><p><code>"arrows"</code>: The loadings are plotted as arrow vectors.</p></li>
 </ul><p>The default is <code>"points"</code>.</p></dd>
 
 
 <dt>label_features</dt>
-<dd><p>(logical) Add feature labels. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Features are not labelled.</p></li>
-</ul><p>The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Add feature labels. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Features are labelled.</p></li>
+<li><p><code>"FALSE"</code>: Features are not labelled.</p></li>
+</ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -108,10 +111,24 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pca_biplot</code> object inherits the following <code>struct</code> classes: <br><br><code>pca_biplot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_biplot</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pca_biplot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      components <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      points_to_label <span class="op">=</span> <span class="st">"none"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      scale_factor <span class="op">=</span> <span class="fl">0.95</span>,</span></span>
+<span class="r-in"><span>      style <span class="op">=</span> <span class="st">"points"</span>,</span></span>
+<span class="r-in"><span>      label_features <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_biplot</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/pca_correlation_plot.html b/reference/pca_correlation_plot.html
index 59f3091..87dcfbb 100644
--- a/reference/pca_correlation_plot.html
+++ b/reference/pca_correlation_plot.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="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"><title>PCA correlation plot — pca_correlation_plot • structToolbox</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="PCA correlation plot — pca_correlation_plot"><meta property="og:description" content="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"><!-- 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="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"><title>PCA correlation plot — pca_correlation_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PCA correlation plot — pca_correlation_plot"><meta property="og:description" content="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -76,10 +76,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pca_correlation_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pca_correlation_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_correlation_plot</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pca_correlation_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      components <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_correlation_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/pca_dstat_plot.html b/reference/pca_dstat_plot.html
index c53d622..1040eba 100644
--- a/reference/pca_dstat_plot.html
+++ b/reference/pca_dstat_plot.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="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."><title>d-statistic plot — pca_dstat_plot • structToolbox</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="d-statistic plot — pca_dstat_plot"><meta property="og:description" content="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."><!-- 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="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."><title>d-statistic plot — pca_dstat_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="d-statistic plot — pca_dstat_plot"><meta property="og:description" content="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,11 @@ <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>number_components</dt>
-<dd><p>(numeric) The number of principal components to use. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric) The number of principal components to use. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>alpha</dt>
-<dd><p>(numeric) A confidence threshold for rejecting samples based on the d-statistic. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) A confidence threshold for rejecting samples based on the d-statistic. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -80,10 +80,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pca_dstat_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pca_dstat_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_dstat_plot</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pca_dstat_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_components <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_dstat_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/pca_loadings_plot.html b/reference/pca_loadings_plot.html
index dec5dba..2661daa 100644
--- a/reference/pca_loadings_plot.html
+++ b/reference/pca_loadings_plot.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="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."><title>PCA loadings plot — pca_loadings_plot • structToolbox</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="PCA loadings plot — pca_loadings_plot"><meta property="og:description" content="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."><!-- 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="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."><title>PCA loadings plot — pca_loadings_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PCA loadings plot — pca_loadings_plot"><meta property="og:description" content="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -69,12 +69,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>style</dt>
-<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"arrows"</code>: The loadings are plotted as arrow vectors.</p></li>
+<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"points"</code>: Loadings and scores are plotted as a scatter plot.</p></li>
+<li><p><code>"arrows"</code>: The loadings are plotted as arrow vectors.</p></li>
 </ul><p>The default is <code>"points"</code>.</p></dd>
 
 
 <dt>label_features</dt>
-<dd><p>(character, NULL) Feature labels. Allowed values are limited to the following:</p><ul><li><p><code>"NULL"</code>: No labels.</p></li>
+<dd><p>(character, NULL) Feature labels. Allowed values are limited to the following:</p><ul><li><p><code>"character()"</code>: A vector of labels for the features.</p></li>
+<li><p><code>"NULL"</code>: No labels.</p></li>
 <li><p><code>"row.names"</code>: Labels will be extracted from the column names of the data matrix.</p></li>
 </ul><p>The default is <code>NULL</code>.</p></dd>
 
@@ -92,10 +94,21 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pca_loadings_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pca_loadings_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_loadings_plot</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pca_loadings_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      components <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      style <span class="op">=</span> <span class="st">"points"</span>,</span></span>
+<span class="r-in"><span>      label_features <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_loadings_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/pca_scores_plot.html b/reference/pca_scores_plot.html
index 96980a7..8a4d5a7 100644
--- a/reference/pca_scores_plot.html
+++ b/reference/pca_scores_plot.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="Plots a 2d scatter plot of the selected components"><title>PCA scores plot — pca_scores_plot • structToolbox</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="PCA scores plot — pca_scores_plot"><meta property="og:description" content="Plots a 2d scatter plot of the selected components"><!-- 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="Plots a 2d scatter plot of the selected components"><title>PCA scores plot — pca_scores_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PCA scores plot — pca_scores_plot"><meta property="og:description" content="Plots a 2d scatter plot of the selected components"><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -81,7 +81,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>points_to_label</dt>
-<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
+<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
+<li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
@@ -91,19 +92,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>ellipse</dt>
-<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
+<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Ellipses are plotted for all groups and all samples.</p></li>
+<li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
 <li><p><code>"sample"</code>: An ellipse is plotted for all samples (ignoring group).</p></li>
 </ul><p>The default is <code>"all"</code>.</p></dd>
 
 
 <dt>ellipse_type</dt>
-<dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
+<dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"norm"</code>: Multivariate normal (p = 0.95).</p></li>
+<li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
 </ul><p>The default is <code>"norm"</code>.</p></dd>
 
 
 <dt>ellipse_confidence</dt>
-<dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>label_filter</dt>
@@ -115,7 +118,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>label_size</dt>
-<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.</p></dd>
+<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
 <dt>components</dt>
@@ -135,10 +138,29 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pca_scores_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pca_scores_plot()</code> ⭢ <code><a href="scatter_chart.html">scatter_chart()</a></code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pca_scores_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      components <span class="op">=</span> <span class="cn">NULL</span>,</span></span>
+<span class="r-in"><span>      xcol <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      points_to_label <span class="op">=</span> <span class="st">"none"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      ellipse <span class="op">=</span> <span class="st">"all"</span>,</span></span>
+<span class="r-in"><span>      label_filter <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      label_factor <span class="op">=</span> <span class="st">"rownames"</span>,</span></span>
+<span class="r-in"><span>      label_size <span class="op">=</span> <span class="fl">3.88</span>,</span></span>
+<span class="r-in"><span>      ellipse_type <span class="op">=</span> <span class="st">"norm"</span>,</span></span>
+<span class="r-in"><span>      ellipse_confidence <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="PCA.html">PCA</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_scores_plot</span><span class="op">(</span>factor_name <span class="op">=</span> <span class="st">'Species'</span><span class="op">)</span></span></span>
diff --git a/reference/pca_scree_plot.html b/reference/pca_scree_plot.html
index 8f44428..889820f 100644
--- a/reference/pca_scree_plot.html
+++ b/reference/pca_scree_plot.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="A plot of the percent variance and cumulative percent variance for the components of a PCA model."><title>Scree plot — pca_scree_plot • structToolbox</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="Scree plot — pca_scree_plot"><meta property="og:description" content="A plot of the percent variance and cumulative percent variance for the components of a PCA model."><!-- 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="A plot of the percent variance and cumulative percent variance for the components of a PCA model."><title>Scree plot — pca_scree_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Scree plot — pca_scree_plot"><meta property="og:description" content="A plot of the percent variance and cumulative percent variance for the components of a PCA model."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>max_pc</dt>
-<dd><p>(numeric, integer) The maximum number of components to include in the plot. The default is <code>15</code>.</p></dd>
+<dd><p>(numeric, integer) The maximum number of components to include in the plot. The default is <code>15</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -79,10 +79,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pca_scree_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pca_scree_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_scree_plot</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pca_scree_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      max_pc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_scree_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/permutation_test.html b/reference/permutation_test.html
index 0bba8e1..9c545a1 100644
--- a/reference/permutation_test.html
+++ b/reference/permutation_test.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='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'><title>Permutation test — permutation_test • structToolbox</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="Permutation test — permutation_test"><meta property="og:description" content='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'><!-- 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='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'><title>Permutation test — permutation_test • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Permutation test — permutation_test"><meta property="og:description" content='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>number_of_permutations</dt>
-<dd><p>(numeric, integer) The number of permutations. The default is <code>50</code>.</p></dd>
+<dd><p>(numeric, integer) The number of permutations. The default is <code>50</code>.<br></p></dd>
 
 
 <dt>factor_name</dt>
@@ -76,10 +76,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>permutation_test</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results.permuted</code></td><td>(data.frame)</td></tr><tr><td><code>results.unpermuted</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>permutation_test</code> object inherits the following <code>struct</code> classes: <br><br><code>permutation_test()</code> ⭢ <code>resampler()</code> ⭢ <code>iterator()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">I</span><span class="op">=</span><span class="fu">permutation_test</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">permutation_test</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_of_permutations <span class="op">=</span> <span class="fl">100</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">I</span><span class="op">=</span><span class="fu">permutation_test</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/permutation_test_plot.html b/reference/permutation_test_plot.html
index 7085c7b..11fc3fa 100644
--- a/reference/permutation_test_plot.html
+++ b/reference/permutation_test_plot.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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diff --git a/reference/permute_sample_order.html b/reference/permute_sample_order.html
index b101b20..3b615e9 100644
--- a/reference/permute_sample_order.html
+++ b/reference/permute_sample_order.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="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."><title>Permute Sample Order — permute_sample_order • structToolbox</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="Permute Sample Order — permute_sample_order"><meta property="og:description" content="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."><!-- 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="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."><title>Permute Sample Order — permute_sample_order • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Permute Sample Order — permute_sample_order"><meta property="og:description" content="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>number_of_permutations</dt>
-<dd><p>(numeric, integer) The number of times the sample order is permuted. The default is <code>10</code>.</p></dd>
+<dd><p>(numeric, integer) The number of times the sample order is permuted. The default is <code>10</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -72,10 +72,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>permute_sample_order</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>permute_sample_order</code> object inherits the following <code>struct</code> classes: <br><br><code>permute_sample_order()</code> ⭢ <code>resampler()</code> ⭢ <code>iterator()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">permute_sample_order</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">permute_sample_order</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_of_permutations <span class="op">=</span> <span class="fl">100</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">permute_sample_order</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/pls_regcoeff_plot.html b/reference/pls_regcoeff_plot.html
index fbc60d6..a8697af 100644
--- a/reference/pls_regcoeff_plot.html
+++ b/reference/pls_regcoeff_plot.html
@@ -1,6 +1,6 @@
 <!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="Plots the regression coefficients of a PLSDA model.
-Plots the regression coefficient scores of a PLSDA model"><title>pls_regcoeff_plot class — pls_regcoeff_plot • structToolbox</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="pls_regcoeff_plot class — pls_regcoeff_plot"><meta property="og:description" content="Plots the regression coefficients of a PLSDA model.
+Plots the regression coefficient scores of a PLSDA model"><title>pls_regcoeff_plot class — pls_regcoeff_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="pls_regcoeff_plot class — pls_regcoeff_plot"><meta property="og:description" content="Plots the regression coefficients of a PLSDA model.
 Plots the regression coefficient scores of a PLSDA model"><!-- 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>
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -63,7 +63,7 @@ <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>ycol</dt>
-<dd><p>(character, numeric, integer) The Y column to plot. The default is <code>1</code>.</p></dd>
+<dd><p>(character, numeric, integer) The Y column to plot. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -84,12 +84,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>ggplot2</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
+<li><p><code>ggplot2</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pls_regcoeff_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pls_regcoeff_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Liland K, Mevik B, Wehrens R (2023). <em>pls: Partial Least Squares and
-Principal Component Regression</em>. R package version 2.8-2,
+Principal Component Regression</em>. R package version 2.8-3,
 <a href="https://CRAN.R-project.org/package=pls" class="external-link">https://CRAN.R-project.org/package=pls</a>.</p>
 <p>Wickham H (2016). <em>ggplot2: Elegant Graphics for Data Analysis</em>.
 Springer-Verlag New York. ISBN 978-3-319-24277-4,
@@ -98,7 +105,10 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pls_regcoeff_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/pls_scores_plot.html b/reference/pls_scores_plot.html
index b70fad8..b57ee1c 100644
--- a/reference/pls_scores_plot.html
+++ b/reference/pls_scores_plot.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="A scatter plot of the selected PLSDA scores."><title>PLSDA scores plot — pls_scores_plot • structToolbox</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="PLSDA scores plot — pls_scores_plot"><meta property="og:description" content="A scatter plot of the selected PLSDA scores."><!-- 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="A scatter plot of the selected PLSDA scores."><title>PLSDA scores plot — pls_scores_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSDA scores plot — pls_scores_plot"><meta property="og:description" content="A scatter plot of the selected PLSDA scores."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -96,7 +96,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>points_to_label</dt>
-<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
+<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
+<li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
@@ -106,19 +107,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>ellipse</dt>
-<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
+<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Ellipses are plotted for all groups and all samples.</p></li>
+<li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
 <li><p><code>"sample"</code>: An ellipse is plotted for all samples (ignoring group).</p></li>
 </ul><p>The default is <code>"all"</code>.</p></dd>
 
 
 <dt>ellipse_type</dt>
-<dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
+<dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"norm"</code>: Multivariate normal (p = 0.95).</p></li>
+<li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
 </ul><p>The default is <code>"norm"</code>.</p></dd>
 
 
 <dt>ellipse_confidence</dt>
-<dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>label_filter</dt>
@@ -130,7 +133,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>label_size</dt>
-<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.</p></dd>
+<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
 <dt>components</dt>
@@ -150,10 +153,29 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pls_scores_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pls_scores_plot()</code> ⭢ <code><a href="scatter_chart.html">scatter_chart()</a></code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pls_scores_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      components <span class="op">=</span> <span class="cn">NULL</span>,</span></span>
+<span class="r-in"><span>      xcol <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      points_to_label <span class="op">=</span> <span class="st">"none"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      ellipse <span class="op">=</span> <span class="st">"all"</span>,</span></span>
+<span class="r-in"><span>      label_filter <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      label_factor <span class="op">=</span> <span class="st">"rownames"</span>,</span></span>
+<span class="r-in"><span>      label_size <span class="op">=</span> <span class="fl">3.88</span>,</span></span>
+<span class="r-in"><span>      ellipse_type <span class="op">=</span> <span class="st">"norm"</span>,</span></span>
+<span class="r-in"><span>      ellipse_confidence <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/pls_vip_plot.html b/reference/pls_vip_plot.html
index 26a3330..18af1e5 100644
--- a/reference/pls_vip_plot.html
+++ b/reference/pls_vip_plot.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="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."><title>PLSDA VIP plot — pls_vip_plot • structToolbox</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="PLSDA VIP plot — pls_vip_plot"><meta property="og:description" content="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."><!-- 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="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."><title>PLSDA VIP plot — pls_vip_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSDA VIP plot — pls_vip_plot"><meta property="og:description" content="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,11 @@ <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>threshold</dt>
-<dd><p>(numeric, integer) The threshold for indicating significant features. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric, integer) The threshold for indicating significant features. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>ycol</dt>
-<dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.</p></dd>
+<dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -82,12 +82,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>ggplot2</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
+<li><p><code>ggplot2</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pls_vip_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>pls_vip_plot()</code> ⭢ <code>chart()</code> ⭢ <code>stato()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Liland K, Mevik B, Wehrens R (2023). <em>pls: Partial Least Squares and
-Principal Component Regression</em>. R package version 2.8-2,
+Principal Component Regression</em>. R package version 2.8-3,
 <a href="https://CRAN.R-project.org/package=pls" class="external-link">https://CRAN.R-project.org/package=pls</a>.</p>
 <p>Wickham H (2016). <em>ggplot2: Elegant Graphics for Data Analysis</em>.
 Springer-Verlag New York. ISBN 978-3-319-24277-4,
@@ -96,7 +103,11 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pls_vip_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      threshold <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/plsda_feature_importance_plot.html b/reference/plsda_feature_importance_plot.html
index 59a37b1..0778340 100644
--- a/reference/plsda_feature_importance_plot.html
+++ b/reference/plsda_feature_importance_plot.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="A plot of the selected feature significance metric for a PLSDA model for the top selected features."><title>PLSDA feature importance summary plot — plsda_feature_importance_plot • structToolbox</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="PLSDA feature importance summary plot — plsda_feature_importance_plot"><meta property="og:description" content="A plot of the selected feature significance metric for a PLSDA model for the top selected features."><!-- 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="A plot of the selected feature significance metric for a PLSDA model for the top selected features."><title>PLSDA feature importance summary plot — plsda_feature_importance_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSDA feature importance summary plot — plsda_feature_importance_plot"><meta property="og:description" content="A plot of the selected feature significance metric for a PLSDA model for the top selected features."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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_features</dt>
-<dd><p>(numeric, integer) The number of features to include in the summary. The default is <code>30</code>.</p></dd>
+<dd><p>(numeric, integer) The number of features to include in the summary. The default is <code>30</code>.<br></p></dd>
 
 
 <dt>metric</dt>
-<dd><p>(character) Metric to plot. Allowed values are limited to the following:</p><ul><li><p><code>"sr_pvalue"</code>: Plot SR p-values.</p></li>
+<dd><p>(character) Metric to plot. Allowed values are limited to the following:</p><ul><li><p><code>"sr"</code>: Plot Selectivity Ratio.</p></li>
+<li><p><code>"sr_pvalue"</code>: Plot SR p-values.</p></li>
 <li><p><code>"vip"</code>: Plot Variable Importance in Projection scores.</p></li>
 </ul><p>The default is <code>"vip"</code>.</p></dd>
 
@@ -84,14 +85,21 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>ggplot2</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
+<li><p><code>ggplot2</code></p></li>
 <li><p><code>reshape2</code></p></li>
 <li><p><code>cowplot</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>plsda_feature_importance_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>plsda_feature_importance_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Liland K, Mevik B, Wehrens R (2023). <em>pls: Partial Least Squares and
-Principal Component Regression</em>. R package version 2.8-2,
+Principal Component Regression</em>. R package version 2.8-3,
 <a href="https://CRAN.R-project.org/package=pls" class="external-link">https://CRAN.R-project.org/package=pls</a>.</p>
 <p>Wickham H (2016). <em>ggplot2: Elegant Graphics for Data Analysis</em>.
 Springer-Verlag New York. ISBN 978-3-319-24277-4,
@@ -99,14 +107,18 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 <p>Wickham H (2007). "Reshaping Data with the reshape Package." <em>Journal
 of Statistical Software</em>, <em>21</em>(12), 1-20.
 <a href="http://www.jstatsoft.org/v21/i12/" class="external-link">http://www.jstatsoft.org/v21/i12/</a>.</p>
-<p>Wilke C (2020). <em>cowplot: Streamlined Plot Theme and Plot Annotations
-for 'ggplot2'</em>. R package version 1.1.1,
+<p>Wilke C (2023). <em>cowplot: Streamlined Plot Theme and Plot Annotations
+for 'ggplot2'</em>. R package version 1.1.2,
 <a href="https://CRAN.R-project.org/package=cowplot" class="external-link">https://CRAN.R-project.org/package=cowplot</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">plsda_feature_importance_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      n_features <span class="op">=</span> <span class="fl">50</span>,</span></span>
+<span class="r-in"><span>      metric <span class="op">=</span> <span class="st">"vip"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/plsda_predicted_plot.html b/reference/plsda_predicted_plot.html
index 29f0e03..82f3cfc 100644
--- a/reference/plsda_predicted_plot.html
+++ b/reference/plsda_predicted_plot.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="A plot of the regression coefficients from a PLSDA model."><title>PLSDA predicted plot — plsda_predicted_plot • structToolbox</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="PLSDA predicted plot — plsda_predicted_plot"><meta property="og:description" content="A plot of the regression coefficients from a PLSDA model."><!-- 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="A plot of the regression coefficients from a PLSDA model."><title>PLSDA predicted plot — plsda_predicted_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSDA predicted plot — plsda_predicted_plot"><meta property="og:description" content="A plot of the regression coefficients from a PLSDA model."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,13 +64,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>style</dt>
-<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"violin"</code>: A violin plot.</p></li>
+<dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"boxplot"</code>: A boxplot.</p></li>
+<li><p><code>"violin"</code>: A violin plot.</p></li>
 <li><p><code>"density"</code>: A density plot.</p></li>
 </ul><p>The default is <code>"boxplot"</code>.</p></dd>
 
 
 <dt>ycol</dt>
-<dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.</p></dd>
+<dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -88,12 +89,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>ggplot2</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
+<li><p><code>ggplot2</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>plsda_predicted_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>plsda_predicted_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Liland K, Mevik B, Wehrens R (2023). <em>pls: Partial Least Squares and
-Principal Component Regression</em>. R package version 2.8-2,
+Principal Component Regression</em>. R package version 2.8-3,
 <a href="https://CRAN.R-project.org/package=pls" class="external-link">https://CRAN.R-project.org/package=pls</a>.</p>
 <p>Wickham H (2016). <em>ggplot2: Elegant Graphics for Data Analysis</em>.
 Springer-Verlag New York. ISBN 978-3-319-24277-4,
@@ -102,7 +110,12 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">plsda_predicted_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      style <span class="op">=</span> <span class="st">"boxplot"</span>,</span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/plsda_roc_plot.html b/reference/plsda_roc_plot.html
index d5fe315..fddfaf5 100644
--- a/reference/plsda_roc_plot.html
+++ b/reference/plsda_roc_plot.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="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."><title>PLSDA ROC plot — plsda_roc_plot • structToolbox</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="PLSDA ROC plot — plsda_roc_plot"><meta property="og:description" content="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."><!-- 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="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."><title>PLSDA ROC plot — plsda_roc_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSDA ROC plot — plsda_roc_plot"><meta property="og:description" content="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,7 +64,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>ycol</dt>
-<dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.</p></dd>
+<dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -82,12 +82,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>ggplot2</code></p></li>
+    <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
+<li><p><code>ggplot2</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>plsda_roc_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>plsda_roc_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Liland K, Mevik B, Wehrens R (2023). <em>pls: Partial Least Squares and
-Principal Component Regression</em>. R package version 2.8-2,
+Principal Component Regression</em>. R package version 2.8-3,
 <a href="https://CRAN.R-project.org/package=pls" class="external-link">https://CRAN.R-project.org/package=pls</a>.</p>
 <p>Wickham H (2016). <em>ggplot2: Elegant Graphics for Data Analysis</em>.
 Springer-Verlag New York. ISBN 978-3-319-24277-4,
@@ -96,7 +103,11 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">plsda_roc_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="PLSDA.html">PLSDA</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/plsr_cook_dist.html b/reference/plsr_cook_dist.html
index fd469ce..f185321 100644
--- a/reference/plsr_cook_dist.html
+++ b/reference/plsr_cook_dist.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="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."><title>Cook's distance barchart — plsr_cook_dist • structToolbox</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="Cook's distance barchart — plsr_cook_dist"><meta property="og:description" content="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."><!-- 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="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."><title>Cook's distance barchart — plsr_cook_dist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Cook's distance barchart — plsr_cook_dist"><meta property="og:description" content="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>ycol</dt>
-<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -76,10 +76,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>plsr_cook_dist</code> object inherits the following <code>struct</code> classes: <br><br><code>plsr_cook_dist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_cook_dist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">plsr_cook_dist</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_cook_dist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/plsr_prediction_plot.html b/reference/plsr_prediction_plot.html
index 9b28303..bf06093 100644
--- a/reference/plsr_prediction_plot.html
+++ b/reference/plsr_prediction_plot.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="A scatter plot of the true response values against the predicted values for a PLSR model."><title>PLSR prediction plot — plsr_prediction_plot • structToolbox</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="PLSR prediction plot — plsr_prediction_plot"><meta property="og:description" content="A scatter plot of the true response values against the predicted values for a PLSR model."><!-- 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="A scatter plot of the true response values against the predicted values for a PLSR model."><title>PLSR prediction plot — plsr_prediction_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSR prediction plot — plsr_prediction_plot"><meta property="og:description" content="A scatter plot of the true response values against the predicted values for a PLSR model."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>ycol</dt>
-<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -76,10 +76,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>plsr_prediction_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>plsr_prediction_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_prediction_plot</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">plsr_prediction_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_prediction_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/plsr_qq_plot.html b/reference/plsr_qq_plot.html
index df25667..e108345 100644
--- a/reference/plsr_qq_plot.html
+++ b/reference/plsr_qq_plot.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="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."><title>PLSR QQ plot — plsr_qq_plot • structToolbox</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="PLSR QQ plot — plsr_qq_plot"><meta property="og:description" content="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."><!-- 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="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."><title>PLSR QQ plot — plsr_qq_plot • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSR QQ plot — plsr_qq_plot"><meta property="og:description" content="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>ycol</dt>
-<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -76,10 +76,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>plsr_qq_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>plsr_qq_plot()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_qq_plot</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">plsr_qq_plot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_qq_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/plsr_residual_hist.html b/reference/plsr_residual_hist.html
index 2896e98..1d57f7c 100644
--- a/reference/plsr_residual_hist.html
+++ b/reference/plsr_residual_hist.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="A histogram of the residuals for a PLSR model."><title>PLSR residuals histogram — plsr_residual_hist • structToolbox</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="PLSR residuals histogram — plsr_residual_hist"><meta property="og:description" content="A histogram of the residuals for a PLSR model."><!-- 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="A histogram of the residuals for a PLSR model."><title>PLSR residuals histogram — plsr_residual_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PLSR residuals histogram — plsr_residual_hist"><meta property="og:description" content="A histogram of the residuals for a PLSR model."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>ycol</dt>
-<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -76,10 +76,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>plsr_residual_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>plsr_residual_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_residual_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">plsr_residual_hist</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_residual_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/pqn_norm.html b/reference/pqn_norm.html
index 219b57a..72d6126 100644
--- a/reference/pqn_norm.html
+++ b/reference/pqn_norm.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="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."><title>Probabilistic Quotient Normalisation (PQN) — pqn_norm • structToolbox</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="Probabilistic Quotient Normalisation (PQN) — pqn_norm"><meta property="og:description" content="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."><!-- 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="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."><title>Probabilistic Quotient Normalisation (PQN) — pqn_norm • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Probabilistic Quotient Normalisation (PQN) — pqn_norm"><meta property="og:description" content="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -76,15 +76,16 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>qc_frac</dt>
-<dd><p>(numeric) A value between 0 and 1 to indicate the minimum proportion of QC samples a feature must be present in for it to be included when computing the reference. Default qc_frac = 0. . The default is <code>0</code>.</p></dd>
+<dd><p>(numeric) A value between 0 and 1 to indicate the minimum proportion of QC samples a feature must be present in for it to be included when computing the reference. Default qc_frac = 0. . The default is <code>0</code>.<br></p></dd>
 
 
 <dt>sample_frac</dt>
-<dd><p>(numeric) A value between 0 and 1 to indicate the minimum proportion of samples a feature must be present in for it to be considered when computing the normalisation coefficients. . The default is <code>0</code>.</p></dd>
+<dd><p>(numeric) A value between 0 and 1 to indicate the minimum proportion of samples a feature must be present in for it to be considered when computing the normalisation coefficients. . The default is <code>0</code>.<br></p></dd>
 
 
 <dt>ref_method</dt>
-<dd><p>(character) Reference computation method. Allowed values are limited to the following:</p><ul><li><p><code>"median"</code>: The reference is computed as the median of the samples matching the qc_label_input.</p></li>
+<dd><p>(character) Reference computation method. Allowed values are limited to the following:</p><ul><li><p><code>"mean"</code>: The reference is computed as the mean of the samples matching the qc_label input.</p></li>
+<li><p><code>"median"</code>: The reference is computed as the median of the samples matching the qc_label_input.</p></li>
 </ul><p>The default is <code>"mean"</code>.</p></dd>
 
 
@@ -105,17 +106,31 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pqn_norm</code> object inherits the following <code>struct</code> classes: <br><br><code>pqn_norm()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pqn_norm</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      qc_frac <span class="op">=</span> <span class="fl">0</span>,</span></span>
+<span class="r-in"><span>      sample_frac <span class="op">=</span> <span class="fl">0</span>,</span></span>
+<span class="r-in"><span>      ref_mean <span class="op">=</span> <span class="cn">NULL</span>,</span></span>
+<span class="r-in"><span>      ref_method <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pqn_norm</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,qc_label<span class="op">=</span><span class="st">'all'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/pqn_norm_hist.html b/reference/pqn_norm_hist.html
index 53a1a2b..2ba8a5d 100644
--- a/reference/pqn_norm_hist.html
+++ b/reference/pqn_norm_hist.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="A histogram of the PQN coefficients for all features"><title>PQN coefficient histogram — pqn_norm_hist • structToolbox</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="PQN coefficient histogram — pqn_norm_hist"><meta property="og:description" content="A histogram of the PQN coefficients for all features"><!-- 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="A histogram of the PQN coefficients for all features"><title>PQN coefficient histogram — pqn_norm_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="PQN coefficient histogram — pqn_norm_hist"><meta property="og:description" content="A histogram of the PQN coefficients for all features"><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>pqn_norm_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>pqn_norm_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pqn_norm_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">pqn_norm_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pqn_norm_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/prop_na.html b/reference/prop_na.html
index 61c5ca2..ad82ede 100644
--- a/reference/prop_na.html
+++ b/reference/prop_na.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="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."><title>Fisher's exact test for missing values — prop_na • structToolbox</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="Fisher's exact test for missing values — prop_na"><meta property="og:description" content="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."><!-- 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="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."><title>Fisher's exact test for missing values — prop_na • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Fisher's exact test for missing values — prop_na"><meta property="og:description" content="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,11 +60,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -83,10 +84,21 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>A  <code>prop_na</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threshold (alpha).</td></tr><tr><td><code>na_count</code></td><td>(data.frame) The number of NA values per group of the chosen factor.</td></tr></table><p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>prop_na</code> object inherits the following <code>struct</code> classes: <br><br><code>prop_na()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">prop_na</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">prop_na</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">prop_na</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/r_squared.html b/reference/r_squared.html
index b474466..095af4e 100644
--- a/reference/r_squared.html
+++ b/reference/r_squared.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="R-squared is a metric used to assess the goodness of fit for regression models. It measures how much variance of one variable can be explained by another variable."><title>Coefficient of determination (R-squared) — r_squared • structToolbox</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="Coefficient of determination (R-squared) — r_squared"><meta property="og:description" content="R-squared is a metric used to assess the goodness of fit for regression models. It measures how much variance of one variable can be explained by another variable."><!-- 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="R-squared is a metric used to assess the goodness of fit for regression models. It measures how much variance of one variable can be explained by another variable."><title>Coefficient of determination (R-squared) — r_squared • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Coefficient of determination (R-squared) — r_squared"><meta property="og:description" content="R-squared is a metric used to assess the goodness of fit for regression models. It measures how much variance of one variable can be explained by another variable."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,10 +71,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 r_squared
 </code> object. This object has no <code>output</code> slots.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>r_squared</code> object inherits the following <code>struct</code> classes: <br><br><code>r_squared()</code> ⭢ <code>metric()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">MET</span> <span class="op">=</span> <span class="fu">r_squared</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">r_squared</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">MET</span> <span class="op">=</span> <span class="fu">r_squared</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/resample.html b/reference/resample.html
index b002e74..36e97f4 100644
--- a/reference/resample.html
+++ b/reference/resample.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="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."><title>Data resampling — resample • structToolbox</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="Data resampling — resample"><meta property="og:description" content="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."><!-- 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="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."><title>Data resampling — resample • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Data resampling — resample"><meta property="og:description" content="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -67,11 +67,12 @@ <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>number_of_iterations</dt>
-<dd><p>(numeric, integer) The number of training sets to generate. The default is <code>10</code>.</p></dd>
+<dd><p>(numeric, integer) The number of training sets to generate. The default is <code>10</code>.<br></p></dd>
 
 
 <dt>method</dt>
-<dd><p>(character) Resampling method. Allowed values are limited to the following:</p><ul><li><p><code>"stratified_split"</code>: Samples for the training set are randomly selected from each level of the chosen factor.</p></li>
+<dd><p>(character) Resampling method. Allowed values are limited to the following:</p><ul><li><p><code>"split_data"</code>: Samples for the training set are selected at random from the full dataset.</p></li>
+<li><p><code>"stratified_split"</code>: Samples for the training set are randomly selected from each level of the chosen factor.</p></li>
 <li><p><code>"equal_split"</code>: Samples for the training set are selected at random from each level of the main factor such that all group sizes are equal.</p></li>
 </ul><p>The default is <code>"split_data"</code>.</p></dd>
 
@@ -81,7 +82,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>p_train</dt>
-<dd><p>(numeric) The proportion of samples selected for the training set. The default is <code>0.8</code>.</p></dd>
+<dd><p>(numeric) The proportion of samples selected for the training set. The default is <code>0.8</code>.<br></p></dd>
 
 
 <dt>collect</dt>
@@ -97,10 +98,23 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>resample</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results.training</code></td><td>(data.frame)</td></tr><tr><td><code>results.testing</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>collected</code></td><td>(list)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr><tr><td><code>metric.test</code></td><td>(numeric)</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>resample</code> object inherits the following <code>struct</code> classes: <br><br><code>resample()</code> ⭢ <code>resampler()</code> ⭢ <code>iterator()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu">resample</span><span class="op">(</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">resample</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      number_of_iterations <span class="op">=</span> <span class="fl">100</span>,</span></span>
+<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"split_data"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      p_train <span class="op">=</span> <span class="fl">0.75</span>,</span></span>
+<span class="r-in"><span>      collect <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu">resample</span><span class="op">(</span></span></span>
 <span class="r-in"><span>    number_of_iterations <span class="op">=</span> <span class="fl">10</span>, </span></span>
 <span class="r-in"><span>    factor_name <span class="op">=</span> <span class="st">'Species'</span>, </span></span>
 <span class="r-in"><span>    method <span class="op">=</span> <span class="st">'split_data'</span>,</span></span>
diff --git a/reference/resample_chart.html b/reference/resample_chart.html
index 5f7d02d..d8a9a27 100644
--- a/reference/resample_chart.html
+++ b/reference/resample_chart.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="Plots the results of a resampling."><title>resample_chart class — resample_chart • structToolbox</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="resample_chart class — resample_chart"><meta property="og:description" content="Plots the results of a resampling."><!-- 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="Plots the results of a resampling."><title>resample_chart class — resample_chart • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="resample_chart class — resample_chart"><meta property="og:description" content="Plots the results of a resampling."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/rsd_filter.html b/reference/rsd_filter.html
index 67b1327..9723d56 100644
--- a/reference/rsd_filter.html
+++ b/reference/rsd_filter.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="An RSD filter calculates the relative standard deviation (the ratio of the standard deviation to the mean) for all features. Any feature with an RSD greater than a predefined threshold is excluded."><title>RSD filter — rsd_filter • structToolbox</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="RSD filter — rsd_filter"><meta property="og:description" content="An RSD filter calculates the relative standard deviation (the ratio of the standard deviation to the mean) for all features. Any feature with an RSD greater than a predefined threshold is excluded."><!-- 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="An RSD filter calculates the relative standard deviation (the ratio of the standard deviation to the mean) for all features. Any feature with an RSD greater than a predefined threshold is excluded."><title>RSD filter — rsd_filter • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="RSD filter — rsd_filter"><meta property="og:description" content="An RSD filter calculates the relative standard deviation (the ratio of the standard deviation to the mean) for all features. Any feature with an RSD greater than a predefined threshold is excluded."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,7 +60,7 @@ <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>rsd_threshold</dt>
-<dd><p>(numeric) The RSD threshold above which features are removed. The default is <code>20</code>.</p></dd>
+<dd><p>(numeric) The RSD threshold above which features are removed. The default is <code>20</code>.<br></p></dd>
 
 
 <dt>qc_label</dt>
@@ -84,17 +84,28 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>rsd_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>rsd_filter()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">rsd_filter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">rsd_filter</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      rsd_threshold <span class="op">=</span> <span class="fl">20</span>,</span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">rsd_filter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/rsd_filter_hist.html b/reference/rsd_filter_hist.html
index 6697de6..641d53f 100644
--- a/reference/rsd_filter_hist.html
+++ b/reference/rsd_filter_hist.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="A histogram of the calculated RSD values."><title>RSD histogram — rsd_filter_hist • structToolbox</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="RSD histogram — rsd_filter_hist"><meta property="og:description" content="A histogram of the calculated RSD values."><!-- 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="A histogram of the calculated RSD values."><title>RSD histogram — rsd_filter_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="RSD histogram — rsd_filter_hist"><meta property="og:description" content="A histogram of the calculated RSD values."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>rsd_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>rsd_filter_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">rsd_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">rsd_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">rsd_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/run.html b/reference/run.html
index c7918d3..9a85f52 100644
--- a/reference/run.html
+++ b/reference/run.html
@@ -2,7 +2,7 @@
 <!-- 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="Running an iterator will apply the iterator a number of times to a DatasetExperiment.
 For example, in cross-validation the same model is applied multiple times to
 the same data, splitting it into training and test sets. The input metric
-object can be calculated and collected for each iteration as an output."><title>Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method • structToolbox</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="Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method"><meta property="og:description" content="Running an iterator will apply the iterator a number of times to a DatasetExperiment.
+object can be calculated and collected for each iteration as an output."><title>Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method"><meta property="og:description" content="Running an iterator will apply the iterator a number of times to a DatasetExperiment.
 For example, in cross-validation the same model is applied multiple times to
 the same data, splitting it into training and test sets. The input metric
 object can be calculated and collected for each iteration as an output."><!-- 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]>
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/sb_corr.html b/reference/sb_corr.html
index a2d2b7d..23b422e 100644
--- a/reference/sb_corr.html
+++ b/reference/sb_corr.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="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."><title>Signal/batch correction for mass spectrometry data — sb_corr • structToolbox</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="Signal/batch correction for mass spectrometry data — sb_corr"><meta property="og:description" content="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."><!-- 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="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."><title>Signal/batch correction for mass spectrometry data — sb_corr • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Signal/batch correction for mass spectrometry data — sb_corr"><meta property="og:description" content="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -83,16 +83,17 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 <dt>smooth</dt>
 <dd><p>(numeric) The amount of smoothing applied (0 to 1). If set to 0 the smoothing parameter will be estimated using
-leave-one-out cross-validation. The default is <code>0</code>.</p></dd>
+leave-one-out cross-validation. The default is <code>0</code>.<br></p></dd>
 
 
 <dt>use_log</dt>
-<dd><p>(logical) Log tranformation. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Signal correction is applied to the input data.</p></li>
-</ul><p>The default is <code>TRUE</code>.</p></dd>
+<dd><p>(logical) Log tranformation. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is log transformed prior to performing signal correction.</p></li>
+<li><p><code>"FALSE"</code>: Signal correction is applied to the input data.</p></li>
+</ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
 <dt>min_qc</dt>
-<dd><p>(numeric) The minimum number of QC samples required for signal correction. The default is <code>4</code>.</p></dd>
+<dd><p>(numeric) The minimum number of QC samples required for signal correction. The default is <code>4</code>.<br></p></dd>
 
 
 <dt>qc_label</dt>
@@ -117,12 +118,18 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>sb_corr</code> object inherits the following <code>struct</code> classes: <br><br><code>sb_corr()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
 and signal batch correction for metabolomics datasets</em>.
 doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.12.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
 <p>Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013). "Characterising
 and correcting batch variation in an automated direct infusion mass
 spectrometry (DIMS) metabolomics workflow." <em>Analytical and
@@ -131,7 +138,17 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">sb_corr</span><span class="op">(</span>order_col<span class="op">=</span><span class="st">'run_order'</span>,batch_col<span class="op">=</span><span class="st">'batch_no'</span>,qc_col<span class="op">=</span><span class="st">'class'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">sb_corr</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      order_col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      batch_col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      qc_col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      smooth <span class="op">=</span> <span class="fl">0</span>,</span></span>
+<span class="r-in"><span>      use_log <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      min_qc <span class="op">=</span> <span class="fl">4</span>,</span></span>
+<span class="r-in"><span>      qc_label <span class="op">=</span> <span class="st">"QC"</span>,</span></span>
+<span class="r-in"><span>      spar_lim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.5</span>, <span class="fl">1.5</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">sb_corr</span><span class="op">(</span>order_col<span class="op">=</span><span class="st">'run_order'</span>,batch_col<span class="op">=</span><span class="st">'batch_no'</span>,qc_col<span class="op">=</span><span class="st">'class'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/scatter_chart.html b/reference/scatter_chart.html
index f07dbbf..ab9ab89 100644
--- a/reference/scatter_chart.html
+++ b/reference/scatter_chart.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="Plots a 2d scatter plot of the input data."><title>Group scatter chart — scatter_chart • structToolbox</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="Group scatter chart — scatter_chart"><meta property="og:description" content="Plots a 2d scatter plot of the input data."><!-- 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="Plots a 2d scatter plot of the input data."><title>Group scatter chart — scatter_chart • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Group scatter chart — scatter_chart"><meta property="og:description" content="Plots a 2d scatter plot of the input data."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,15 +72,16 @@ <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>xcol</dt>
-<dd><p>(numeric, integer, character) The column name, or index, of data to plot on the x-axis. The default is <code>1</code>.</p></dd>
+<dd><p>(numeric, integer, character) The column name, or index, of data to plot on the x-axis. The default is <code>1</code>.<br></p></dd>
 
 
 <dt>ycol</dt>
-<dd><p>(numeric, integer, character) The column name, or index, of data to plot on the y-axis. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric, integer, character) The column name, or index, of data to plot on the y-axis. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>points_to_label</dt>
-<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
+<dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
+<li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
@@ -90,19 +91,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>ellipse</dt>
-<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
+<dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Ellipses are plotted for all groups and all samples.</p></li>
+<li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
 <li><p><code>"sample"</code>: An ellipse is plotted for all samples (ignoring group).</p></li>
 </ul><p>The default is <code>"all"</code>.</p></dd>
 
 
 <dt>ellipse_type</dt>
-<dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
+<dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"norm"</code>: Multivariate normal (p = 0.95).</p></li>
+<li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
 </ul><p>The default is <code>"norm"</code>.</p></dd>
 
 
 <dt>ellipse_confidence</dt>
-<dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>label_filter</dt>
@@ -114,7 +117,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>label_size</dt>
-<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.</p></dd>
+<dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -130,10 +133,28 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>scatter_chart</code> object inherits the following <code>struct</code> classes: <br><br><code>scatter_chart()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">scatter_chart</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      xcol <span class="op">=</span> <span class="fl">1</span>,</span></span>
+<span class="r-in"><span>      ycol <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      points_to_label <span class="op">=</span> <span class="st">"none"</span>,</span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      ellipse <span class="op">=</span> <span class="st">"all"</span>,</span></span>
+<span class="r-in"><span>      label_filter <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      label_factor <span class="op">=</span> <span class="st">"rownames"</span>,</span></span>
+<span class="r-in"><span>      label_size <span class="op">=</span> <span class="fl">3.88</span>,</span></span>
+<span class="r-in"><span>      ellipse_type <span class="op">=</span> <span class="st">"norm"</span>,</span></span>
+<span class="r-in"><span>      ellipse_confidence <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">scatter_chart</span><span class="op">(</span></span></span>
 <span class="r-in"><span>        xcol <span class="op">=</span> <span class="st">'Petal.Width'</span>,</span></span>
 <span class="r-in"><span>        ycol <span class="op">=</span> <span class="st">'Sepal.Width'</span>,</span></span>
diff --git a/reference/split_data.html b/reference/split_data.html
index e50dfb3..1b9b85a 100644
--- a/reference/split_data.html
+++ b/reference/split_data.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="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."><title>Split data — split_data • structToolbox</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="Split data — split_data"><meta property="og:description" content="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."><!-- 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="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."><title>Split data — split_data • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Split data — split_data"><meta property="og:description" content="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,10 +72,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>split_data</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>split_data</code> object inherits the following <code>struct</code> classes: <br><br><code>split_data()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">split_data</span><span class="op">(</span>p_train<span class="op">=</span><span class="fl">0.75</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">split_data</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      p_train <span class="op">=</span> <span class="fl">0.75</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">split_data</span><span class="op">(</span>p_train<span class="op">=</span><span class="fl">0.75</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/stratified_split.html b/reference/stratified_split.html
index f31cff8..efb224a 100644
--- a/reference/stratified_split.html
+++ b/reference/stratified_split.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="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."><title>Stratified sampling — stratified_split • structToolbox</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="Stratified sampling — stratified_split"><meta property="og:description" content="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."><!-- 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="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."><title>Stratified sampling — stratified_split • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Stratified sampling — stratified_split"><meta property="og:description" content="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -76,10 +76,20 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>stratified_split</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>stratified_split</code> object inherits the following <code>struct</code> classes: <br><br><code>stratified_split()</code> ⭢ <code><a href="split_data.html">split_data()</a></code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">stratified_split</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      p_train <span class="op">=</span> <span class="fl">0.75</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">stratified_split</span><span class="op">(</span>p_train<span class="op">=</span><span class="fl">0.75</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/structToolbox.html b/reference/structToolbox.html
index 6142985..aa0cec0 100644
--- a/reference/structToolbox.html
+++ b/reference/structToolbox.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="This package extends the classes defined in the struct package"><title>structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox • structToolbox</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="structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox"><meta property="og:description" content="This package extends the classes defined in the struct package"><!-- 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 package extends the classes defined in the struct package"><title>structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox"><meta property="og:description" content="This package extends the classes defined in the struct package"><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/svm_plot_2d-1.png b/reference/svm_plot_2d-1.png
index 0e2172f..d908bab 100644
Binary files a/reference/svm_plot_2d-1.png and b/reference/svm_plot_2d-1.png differ
diff --git a/reference/svm_plot_2d.html b/reference/svm_plot_2d.html
index fd133e8..6e2998b 100644
--- a/reference/svm_plot_2d.html
+++ b/reference/svm_plot_2d.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="A scatter plot of the input data by group and the calculated boundary of a SVM model."><title>SVM scatter plot — svm_plot_2d • structToolbox</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="SVM scatter plot — svm_plot_2d"><meta property="og:description" content="A scatter plot of the input data by group and the calculated boundary of a SVM model."><!-- 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="A scatter plot of the input data by group and the calculated boundary of a SVM model."><title>SVM scatter plot — svm_plot_2d • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="SVM scatter plot — svm_plot_2d"><meta property="og:description" content="A scatter plot of the input data by group and the calculated boundary of a SVM model."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,7 +64,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>npoints</dt>
-<dd><p>(numeric) The number of grid points used to plot the boundary. The default is <code>100</code>.</p></dd>
+<dd><p>(numeric) The number of grid points used to plot the boundary. The default is <code>100</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -84,17 +84,27 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>e1071</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>svm_plot_2d</code> object inherits the following <code>struct</code> classes: <br><br><code>svm_plot_2d()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2023).
 <em>e1071: Misc Functions of the Department of Statistics, Probability
-Theory Group (Formerly: E1071), TU Wien</em>. R package version 1.7-13,
+Theory Group (Formerly: E1071), TU Wien</em>. R package version 1.7-14,
 <a href="https://CRAN.R-project.org/package=e1071" class="external-link">https://CRAN.R-project.org/package=e1071</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">svm_plot_2d</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      npoints <span class="op">=</span> <span class="fl">100</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="filter_smeta.html">filter_smeta</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'exclude'</span>,levels<span class="op">=</span><span class="st">'setosa'</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span> <span class="op">+</span></span></span>
 <span class="r-in"><span>    <span class="fu"><a href="mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="PCA.html">PCA</a></span><span class="op">(</span>number_components<span class="op">=</span><span class="fl">2</span><span class="op">)</span><span class="op">+</span></span></span>
 <span class="r-in"><span>    <span class="fu"><a href="SVM.html">SVM</a></span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,kernel<span class="op">=</span><span class="st">'linear'</span><span class="op">)</span></span></span>
diff --git a/reference/tSNE.html b/reference/tSNE.html
index 4fb894d..eabb878 100644
--- a/reference/tSNE.html
+++ b/reference/tSNE.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="t-Distributed Stochastic Neighbor Embedding."><title>tSNE — tSNE • structToolbox</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="tSNE — tSNE"><meta property="og:description" content="t-Distributed Stochastic Neighbor Embedding."><!-- 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="t-Distributed Stochastic Neighbor Embedding."><title>tSNE — tSNE • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="tSNE — tSNE"><meta property="og:description" content="t-Distributed Stochastic Neighbor Embedding."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -69,24 +69,25 @@ <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>dims</dt>
-<dd><p>(numeric) The number of tSNE dimensions computed. The default is <code>2</code>.</p></dd>
+<dd><p>(numeric) The number of tSNE dimensions computed. The default is <code>2</code>.<br></p></dd>
 
 
 <dt>perplexity</dt>
-<dd><p>(numeric) Perplexity parameter. The default is <code>30</code>.</p></dd>
+<dd><p>(numeric) Perplexity parameter. The default is <code>30</code>.<br></p></dd>
 
 
 <dt>max_iter</dt>
-<dd><p>(numeric) The maximum number of tSNE iterations. The default is <code>100</code>.</p></dd>
+<dd><p>(numeric) The maximum number of tSNE iterations. The default is <code>100</code>.<br></p></dd>
 
 
 <dt>theta</dt>
-<dd><p>(numeric) Speed/accuracy trade-off. A value of 0 gives an exact tSNE. The default is <code>0.5</code>.</p></dd>
+<dd><p>(numeric) Speed/accuracy trade-off. A value of 0 gives an exact tSNE. The default is <code>0.5</code>.<br></p></dd>
 
 
 <dt>check_duplicates</dt>
-<dd><p>(logical) Check for duplicates. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: Does not check for exact duplicate samples.</p></li>
-</ul><p>The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Check for duplicates. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Checks for the presence of exact duplicate samples.</p></li>
+<li><p><code>"FALSE"</code>: Does not check for exact duplicate samples.</p></li>
+</ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>init</dt>
@@ -94,7 +95,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>eta</dt>
-<dd><p>(numeric) The learning rate parameter. The default is <code>200</code>.</p></dd>
+<dd><p>(numeric) The learning rate parameter. The default is <code>200</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -110,20 +111,35 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>Rtsne</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>tSNE</code> object inherits the following <code>struct</code> classes: <br><br><code>tSNE()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>van der Maaten L, Hinton G (2008). "Visualizing High-Dimensional Data
+    <p>Krijthe JH (2015). <em>Rtsne: T-Distributed Stochastic Neighbor Embedding
+using Barnes-Hut Implementation</em>. R package version 0.17,
+<a href="https://github.com/jkrijthe/Rtsne" class="external-link">https://github.com/jkrijthe/Rtsne</a>.</p>
+<p>van der Maaten L, Hinton G (2008). "Visualizing High-Dimensional Data
 Using t-SNE." <em>Journal of Machine Learning Research</em>, <em>9</em>, 2579-2605.</p>
 <p>van der Maaten L (2014). "Accelerating t-SNE using Tree-Based
 Algorithms." <em>Journal of Machine Learning Research</em>, <em>15</em>, 3221-3245.</p>
-<p>Krijthe JH (2015). <em>Rtsne: T-Distributed Stochastic Neighbor Embedding
-using Barnes-Hut Implementation</em>. R package version 0.16,
-<a href="https://github.com/jkrijthe/Rtsne" class="external-link">https://github.com/jkrijthe/Rtsne</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">tSNE</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">tSNE</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      dims <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>      perplexity <span class="op">=</span> <span class="fl">30</span>,</span></span>
+<span class="r-in"><span>      max_iter <span class="op">=</span> <span class="fl">1000</span>,</span></span>
+<span class="r-in"><span>      theta <span class="op">=</span> <span class="fl">0.5</span>,</span></span>
+<span class="r-in"><span>      check_duplicates <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      init <span class="op">=</span> <span class="cn">NULL</span>,</span></span>
+<span class="r-in"><span>      eta <span class="op">=</span> <span class="fl">200</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">tSNE</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/tSNE_scatter.html b/reference/tSNE_scatter.html
index cb52885..1a8f889 100644
--- a/reference/tSNE_scatter.html
+++ b/reference/tSNE_scatter.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="plots the new representation of data after applying tSNE."><title>Feature boxplot — tSNE_scatter • structToolbox</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="Feature boxplot — tSNE_scatter"><meta property="og:description" content="plots the new representation of data after applying tSNE."><!-- 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="plots the new representation of data after applying tSNE."><title>Feature boxplot — tSNE_scatter • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Feature boxplot — tSNE_scatter"><meta property="og:description" content="plots the new representation of data after applying tSNE."><!-- 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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@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,20 +80,29 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>Rtsne</code></p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>tSNE_scatter</code> object inherits the following <code>struct</code> classes: <br><br><code>tSNE_scatter()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>van der Maaten L, Hinton G (2008). "Visualizing High-Dimensional Data
+    <p>Krijthe JH (2015). <em>Rtsne: T-Distributed Stochastic Neighbor Embedding
+using Barnes-Hut Implementation</em>. R package version 0.17,
+<a href="https://github.com/jkrijthe/Rtsne" class="external-link">https://github.com/jkrijthe/Rtsne</a>.</p>
+<p>van der Maaten L, Hinton G (2008). "Visualizing High-Dimensional Data
 Using t-SNE." <em>Journal of Machine Learning Research</em>, <em>9</em>, 2579-2605.</p>
 <p>van der Maaten L (2014). "Accelerating t-SNE using Tree-Based
 Algorithms." <em>Journal of Machine Learning Research</em>, <em>15</em>, 3221-3245.</p>
-<p>Krijthe JH (2015). <em>Rtsne: T-Distributed Stochastic Neighbor Embedding
-using Barnes-Hut Implementation</em>. R package version 0.16,
-<a href="https://github.com/jkrijthe/Rtsne" class="external-link">https://github.com/jkrijthe/Rtsne</a>.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">tSNE_scatter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">tSNE_scatter</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">tSNE_scatter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/tic_chart.html b/reference/tic_chart.html
index e2b8aa5..0ef02ff 100644
--- a/reference/tic_chart.html
+++ b/reference/tic_chart.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="A scatter plot of Total Ion Count (sum of each sample) versus run order."><title>Total Ion Count chart. — tic_chart • structToolbox</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="Total Ion Count chart. — tic_chart"><meta property="og:description" content="A scatter plot of Total Ion Count (sum of each sample) versus run order."><!-- 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="A scatter plot of Total Ion Count (sum of each sample) versus run order."><title>Total Ion Count chart. — tic_chart • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Total Ion Count chart. — tic_chart"><meta property="og:description" content="A scatter plot of Total Ion Count (sum of each sample) versus run order."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -68,7 +68,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>connected</dt>
-<dd><p>(logical) Plot samples connected by a grey line. The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Plot samples connected by a grey line. The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -84,10 +84,21 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>tic_chart</code> object inherits the following <code>struct</code> classes: <br><br><code>tic_chart()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">tic_chart</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      factor_name <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      run_order <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      connected <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">D</span><span class="op">$</span><span class="va">sample_meta</span><span class="op">$</span><span class="va">run_order</span><span class="op">=</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">tic_chart</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,run_order<span class="op">=</span><span class="st">'run_order'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
diff --git a/reference/ttest.html b/reference/ttest.html
index 4b5e92a..fedeaab 100644
--- a/reference/ttest.html
+++ b/reference/ttest.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="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."><title>t-test — ttest • structToolbox</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="t-test — ttest"><meta property="og:description" content="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."><!-- 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="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."><title>t-test — ttest • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="t-test — ttest"><meta property="og:description" content="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -69,11 +69,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -83,7 +84,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>paired</dt>
-<dd><p>(logical) Apply a paired t-test. The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Apply a paired t-test. The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>paired_factor</dt>
@@ -91,12 +92,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>equal_variance</dt>
-<dd><p>(logical) Equal variance. Allowed values are limited to the following:</p><ul><li><p><code>"FALSE"</code>: The variance of each group is not assumed to be equal and the Welch (or Satterthwaite) approximation is used.</p></li>
-</ul><p>The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Equal variance. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The variance of each group is treated as being equal using the pooled variance to estimate the variance.</p></li>
+<li><p><code>"FALSE"</code>: The variance of each group is not assumed to be equal and the Welch (or Satterthwaite) approximation is used.</p></li>
+</ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>conf_level</dt>
-<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -108,10 +110,25 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A  <code>ttest</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>t_statistic</code></td><td>(data.frame) The value of the calculate statistics which is converted to a p-value when compared to a t-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated t-statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>conf_int</code></td><td>(data.frame) Confidence interval for t statistic.</td></tr><tr><td><code>estimates</code></td><td>(data.frame) The group means estimated when computing the t-statistic.</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>ttest</code> object inherits the following <code>struct</code> classes: <br><br><code>ttest()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">ttest</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">ttest</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_names <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      paired <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      paired_factor <span class="op">=</span> <span class="st">"NA"</span>,</span></span>
+<span class="r-in"><span>      equal_variance <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      conf_level <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">ttest</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/vec_norm.html b/reference/vec_norm.html
index 06b03e0..1a0b671 100644
--- a/reference/vec_norm.html
+++ b/reference/vec_norm.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="The samples in the data matrix are normalised to account for differences in concentration by scaling each sample such that the sum of squares is equal to 1."><title>Vector normalisation — vec_norm • structToolbox</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="Vector normalisation — vec_norm"><meta property="og:description" content="The samples in the data matrix are normalised to account for differences in concentration by scaling each sample such that the sum of squares is equal to 1."><!-- 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="The samples in the data matrix are normalised to account for differences in concentration by scaling each sample such that the sum of squares is equal to 1."><title>Vector normalisation — vec_norm • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Vector normalisation — vec_norm"><meta property="og:description" content="The samples in the data matrix are normalised to account for differences in concentration by scaling each sample such that the sum of squares is equal to 1."><!-- 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">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -69,11 +69,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>A  <code>vec_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The normalisation coefficients calculated by PQN.</td></tr></table><p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>vec_norm</code> object inherits the following <code>struct</code> classes: <br><br><code>vec_norm()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">vec_norm</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">vec_norm</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/wilcox_p_hist.html b/reference/wilcox_p_hist.html
index ef14e3c..005c999 100644
--- a/reference/wilcox_p_hist.html
+++ b/reference/wilcox_p_hist.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="A histogram of p values for the wilcoxon signed rank test"><title>Histogram of p values — wilcox_p_hist • structToolbox</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="Histogram of p values — wilcox_p_hist"><meta property="og:description" content="A histogram of p values for the wilcoxon signed rank test"><!-- 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="A histogram of p values for the wilcoxon signed rank test"><title>Histogram of p values — wilcox_p_hist • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="Histogram of p values — wilcox_p_hist"><meta property="og:description" content="A histogram of p values for the wilcoxon signed rank test"><!-- 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 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -72,11 +72,19 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>wilcox_p_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>wilcox_p_hist()</code> ⭢ <code>chart()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">wilcox_p_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">wilcox_p_hist</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/wilcox_test.html b/reference/wilcox_test.html
index 6dd8382..cd0eaa5 100644
--- a/reference/wilcox_test.html
+++ b/reference/wilcox_test.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="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."><title>wilcoxon signed rank test — wilcox_test • structToolbox</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="wilcoxon signed rank test — wilcox_test"><meta property="og:description" content="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."><!-- 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="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."><title>wilcoxon signed rank test — wilcox_test • structToolbox</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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/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="wilcoxon signed rank test — wilcox_test"><meta property="og:description" content="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."><!-- 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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 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.13.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -68,11 +68,12 @@ <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>alpha</dt>
-<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.</p></dd>
+<dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
 <dt>mtc</dt>
-<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
+<dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
+<li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
@@ -82,7 +83,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>paired</dt>
-<dd><p>(logical) Apply a paired test. The default is <code>FALSE</code>.</p></dd>
+<dd><p>(logical) Apply a paired test. The default is <code>FALSE</code>.<br></p></dd>
 
 
 <dt>paired_factor</dt>
@@ -90,7 +91,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>conf_level</dt>
-<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.</p></dd>
+<dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
 <dt>...</dt>
@@ -103,10 +104,24 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 <p>A  <code>wilcox_test</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>statistic</code></td><td>(data.frame) The value of the calculated statistic which is converted to a p-value.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated t-statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>conf_int</code></td><td>(data.frame) Confidence interval for t statistic.</td></tr><tr><td><code>estimates</code></td><td>(data.frame) The group estimates used when computing the statistic.</td></tr></table><p>struct object</p>
     </div>
+    <div class="section level2">
+    <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
+    
+
+<p>A <code>wilcox_test</code> object inherits the following <code>struct</code> classes: <br><br><code>wilcox_test()</code> ⭢ <code>model()</code> ⭢ <code>struct_class()</code></p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">wilcox_test</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">wilcox_test</span><span class="op">(</span></span></span>
+<span class="r-in"><span>      alpha <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
+<span class="r-in"><span>      mtc <span class="op">=</span> <span class="st">"fdr"</span>,</span></span>
+<span class="r-in"><span>      factor_names <span class="op">=</span> <span class="st">"V1"</span>,</span></span>
+<span class="r-in"><span>      paired <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
+<span class="r-in"><span>      paired_factor <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      conf_level <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">wilcox_test</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/search.json b/search.json
index 0d45b89..4d4f7d8 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"‘structToolbox’ includes extensive set data (pre-)processing analysis tools metabolomics omics, strong emphasis statistics machine learning. methods tools implemented using class-based templates available via struct (Statistics R Using Class-based Templates) package. aim vignette introduce reader basic advanced structToolbox-based operations implementations, use struct objects, getting/setting methods/parameters, building workflows analysis mass spectrometry (MS) nuclear magnetic resonance (NMR)-based Metabolomics proteomics datasets. workflows demonstrated include wide range methods tools including pre-processing filtering, normalisation scaling, followed univariate /multivariate statistics, machine learning approaches.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"latest version structToolbox compatible current R version can installed using BiocManager. number additional packages needed vignette.","code":"# install BiocManager if not present if (!requireNamespace(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  # install structToolbox and dependencies BiocManager::install(\"structToolbox\") ## install additional bioc packages for vignette if needed #BiocManager::install(c('pmp', 'ropls', 'BiocFileCache'))  ## install additional CRAN packages if needed #install.packages(c('cowplot', 'openxlsx'))  suppressPackageStartupMessages({     # Bioconductor packages     library(structToolbox)     library(pmp)     library(ropls)     library(BiocFileCache)        # CRAN libraries     library(ggplot2)     library(gridExtra)     library(cowplot)     library(openxlsx) })   # use the BiocFileCache bfc <- BiocFileCache(ask = FALSE)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-1","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PCA (Principal Component Analysis) PLS (Partial Least Squares) commonly applied methods exploring analysing multivariate datasets. use two statistical methods demonstrate different types struct (STatistics R Using Class Templates) objects available part structToolbox objects (.e. class templates) can used conduct unsupervised supervised multivariate statistical analysis.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes use “Iris” dataset. famous (Fisher’s Anderson’s) dataset contains measurements sepal length width petal length width, centimeters, 50 flowers 3 class Iris. class Iris setosa, versicolor, virginica. See (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/iris.html) information. Note: vignette also compatible Direct infusion mass spectrometry metabolomics “benchmark” dataset described Kirwan et al., Sci Data 1, 140012 (2014) (https://doi.org/10.1038/sdata.2014.12). datasets available part structToolbox package already prepared DatasetExperiment object.","code":"## Iris dataset (comment if using MTBLS79 benchmark data) D = iris_DatasetExperiment() D$sample_meta$class = D$sample_meta$Species  ## MTBLS (comment if using Iris data) # D = MTBLS79_DatasetExperiment(filtered=TRUE) # M = pqn_norm(qc_label='QC',factor_name='sample_type') +  #   knn_impute(neighbours=5) + #   glog_transform(qc_label='QC',factor_name='sample_type') + #   filter_smeta(mode='exclude',levels='QC',factor_name='sample_type') # M = model_apply(M,D) # D = predicted(M)  # show info D ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Fisher's Iris dataset ## description:   This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of ##                  the variables sepal length and width and petal length and width, ##                  respectively, for 50 flowers from each of 3 species of iris. The species are ##                  Iris setosa, versicolor, and virginica. ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"datasetexperiment-objects","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Dataset","what":"DatasetExperiment objects","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"DatasetExperiment object extension SummarizedExperiment class used Bioconductor community. contains three main parts: data data frame containing measured data sample. sample_meta data frame additional information related samples e.g. group labels. variable_meta data frame additional information related variables (features) e.g. annotations Like struct objects also contains name description fields (called “slots” R language). key difference DatasetExperiment SummarizedExperiment objects data transposed. .e. DatasetExperiment objects samples rows features columns, opposite true SummarizedExperiment objects. slots accessible using dollar notation.","code":"# show some data head(D$data[,1:4]) ##   Sepal.Length Sepal.Width Petal.Length Petal.Width ## 1          5.1         3.5          1.4         0.2 ## 2          4.9         3.0          1.4         0.2 ## 3          4.7         3.2          1.3         0.2 ## 4          4.6         3.1          1.5         0.2 ## 5          5.0         3.6          1.4         0.2 ## 6          5.4         3.9          1.7         0.4"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"can apply e.g. PCA first need create PCA object. object contains inputs, outputs methods needed apply PCA. can set parameters number components PCA model created, can also use dollar notation change/view later. inputs model can listed using param_ids(object): summary object can printed console:","code":"P = PCA(number_components=15) P$number_components=5 P$number_components ## [1] 5 param_ids(P) ## [1] \"number_components\" P ## A \"PCA\" object ## -------------- ## name:          Principal Component Analysis (PCA) ## description:   PCA is a multivariate data reduction technique. It summarises the data in a smaller number of ##                  Principal Components that maximise variance. ## input params:  number_components  ## outputs:       scores, loadings, eigenvalues, ssx, correlation, that  ## predicted:     that ## seq_in:        data"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-sequences","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model sequences","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Unless good reason , usually sensible mean centre columns data PCA. Using STRUCT framework can create model sequence mean centre apply PCA mean centred data. structToolbox mean centring PCA model objects, joining using “+” creates model_sequence object. model_sequence outputs first object (mean centring) automatically passed inputs second object (PCA), allows chain together modelling steps order build workflow. objects model_sequence can accessed indexing, can combine dollar notation. example, PCA object second object sequence can access number components follows:","code":"M = mean_centre() + PCA(number_components = 4) M[2]$number_components ## [1] 4"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"trainingtesting-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Training/testing models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Model model_sequence objects need trained using data form DatasetExperiment object. example, PCA model sequence created (M) can trained using iris DatasetExperiment object (‘D’). model sequence now mean centred original data calculated PCA scores loadings. Model objects can used generate predictions test datasets. PCA model sequence involves mean centring test data using mean training data, projecting centred test data onto PCA model using loadings. outputs stored model sequence can accessed using dollar notation. example just use training data (sometimes called autoprediction), PCA allows us explore training data detail. Sometimes models don’t make use training/test approach e.g. univariate statsitics, filtering etc. models model_apply method can used instead. models provide training/test methods, model_apply applies autoprediction default .e. short-cut applying model_train model_predict data. available outputs object can listed accessed like input params, using dollar notation:","code":"M = model_train(M,D) M = model_predict(M,D) M = model_apply(M,D) output_ids(M[2]) ## [1] \"scores\"      \"loadings\"    \"eigenvalues\" \"ssx\"         \"correlation\" ## [6] \"that\" M[2]$scores ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-charts","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model charts","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"struct framework includes chart objects. Charts associated model object can listed. Like model objects, chart objects need created can used. plot PCA scores plot mean centred PCA model.  Note indexing PCA model required pca_scores_plot object requires PCA object input, model_sequence. make changes input parameters chart, chart_plot must called see effects.  chart_plot method returns ggplot object can easily combine plots using gridExtra cowplot packages example.","code":"chart_names(M[2]) ## [1] \"pca_biplot\"           \"pca_correlation_plot\" \"pca_dstat_plot\"       ## [4] \"pca_loadings_plot\"    \"pca_scores_plot\"      \"pca_scree_plot\" C = pca_scores_plot(factor_name='class') # colour by class chart_plot(C,M[2]) # add petal width to meta data of pca scores M[2]$scores$sample_meta$example=D$data[,1] # update plot C$factor_name='example' chart_plot(C,M[2]) # scores plot C1 = pca_scores_plot(factor_name='class') # colour by class g1 = chart_plot(C1,M[2])  # scree plot C2 = pca_scree_plot() g2 = chart_plot(C2,M[2])  # arange in grid grid.arrange(grobs=list(g1,g2),nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"ontology","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Ontology","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Within struct framework (structToolbox) ontology slot provided allow standardardised definitions objects inputs outputs using Ontology Lookup Service (OLS). example, STATO general purpose STATistics Ontology (http://stato-ontology.org). webpage: aim provide coverage processes statistical tests, conditions application, information needed resulting statistical methods, probability distributions, variables, spread variation metrics. STATO also covers aspects experimental design description plots graphical representations commonly used provide visual cues data distribution layout assist review results. ontology object can set assigning ontology term identifier ontology slot struct_class object design time. ids can listed using $ notation: ontology method can used obtain detailed ontology information. cache = NULL struct package automatically attempt use OLS API (via rols package) obtain name description provided identifiers. used cached versions ontology definitions provided structToolbox package prevent issues connecting OLS API building package. Note ontology method returns definitions object (PCA) inputs/outputs (number_of_components).","code":"# create an example PCA object P=PCA()  # ontology for the PCA object P$ontology ## [1] \"OBI:0200051\" ontology(P,cache = ontology_cache()) # set cache = NULL (default) for online use ## [[1]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       OBI:0200051 ## ontology:      obi ## label:         principal components analysis dimensionality reduction ## description:   A principal components analysis dimensionality reduction is a dimensionality reduction ##                  achieved by applying principal components analysis and by keeping low-order principal ##                  components and excluding higher-order ones. ## iri:           http://purl.obolibrary.org/obo/OBI_0200051 ##  ##  ##  ## [[2]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       STATO:0000555 ## ontology:      stato ## label:         number of predictive components ## description:   number of predictive components is a count used as input to the principle component analysis ##                  (PCA) ## iri:           http://purl.obolibrary.org/obo/STATO_0000555"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"validating-supervised-statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Validating supervised statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Validation important aspect chemometric modelling. struct framework enables kind iterative model testing iterator objects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"cross-validation","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Validating supervised statistical models","what":"Cross-validation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Cross validation common technique assessing performance classification models. example use Partial least squares-discriminant analysis (PLS-DA) model. Data mean centred prior PLS, build model sequence first. iterator objects like k-fold cross-validation object (kfold_xval) can created just like struct object. Parameters can set creation using equals sign, accessed changed later using dollar notation. model cross-validated can set/accessed using models method. Alternatively, iterators can combined models using multiplication symbol shorthand models assignement method: run method can used iterator object. iterator run set model model sequence multiple times. case run cross-validation 5 times, splitting data different training test sets time. run method also needs metric specified, another type struct object. metric may calculated iterations, iteration, depending iterator type (resampling, permutation etc). cross-validation calculate “balanced accuracy” iterations. Note balanced_accuracy metric actually reports 1-accuracy, value 0 indicates perfect performance. standard deviation “sd” NA example one permutation. Like struct objects, iterators can chart objects associated . chart_names function list object. Charts iterator objects can plotted way charts object.  possible combine multiple iterators using multiplication symbol. equivalent nesting one iterator inside . example, can repeat cross-validation multiple times permuting sample order.","code":"M = mean_centre() + PLSDA(number_components=2,factor_name='class') M ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # create object XCV = kfold_xval(folds=5,factor_name='class')  # change the number of folds XCV$folds=10 XCV$folds ## [1] 10 models(XCV)=M models(XCV) ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # cross validation of a mean centred PLSDA model XCV = kfold_xval(         folds=5,         method='venetian',         factor_name='class') *        (mean_centre() + PLSDA(factor_name='class')) XCV = run(XCV,D,balanced_accuracy()) XCV$metric ##              metric mean sd ## 1 balanced_accuracy 0.11 NA chart_names(XCV) ## [1] \"kfoldxcv_grid\"   \"kfoldxcv_metric\" C = kfoldxcv_grid(   factor_name='class',   level=levels(D$sample_meta$class)[2]) # first level chart_plot(C,XCV) # permute sample order 10 times and run cross-validation P = permute_sample_order(number_of_permutations = 10) *      kfold_xval(folds=5,factor_name='class')*     (mean_centre() + PLSDA(factor_name='class',number_components=2)) P = run(P,D,balanced_accuracy()) P$metric ##              metric   mean          sd ## 1 balanced_accuracy 0.1095 0.004972145"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-2","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"vignette provides overview structToolbox workflow implemented process (e.g. filter features, signal drift batch correction, normalise missing value imputation) mass spectrometry data. workflow exists methods part Peak Matrix Processing (pmp) package, including range additional filters described Kirwan et al., 2013, 2014. packages required vignette addition structToolbox:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-1","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes process analyse MTBLS79 dataset (‘Dataset 7:SFPM’ Kirwan et al., 2014. dataset represents systematic evaluation reproducibility multi-batch direct-infusion mass spectrometry (DIMS)-based metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step data processing workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79). MTBLS79_DatasetExperiment object included structToolbox package processed version MTBLS79 dataset available peak matrix processing (pmp) package. vignette describes step step structToolbox version created pmp version (.e. ‘Dataset 7:SFPM’ Scientific Data publication - https://doi.org/10.1038/sdata.2014.12). SummarizedExperiment object pmp package needs converted DatasetExperiment object use structToolbox. Full processing data set requires number steps. applied using single struct model sequence (model_seq).","code":"# the pmp SE object SE = MTBLS79  # convert to DE DE = as.DatasetExperiment(SE) DE$name = 'MTBLS79' DE$description = 'Converted from SE provided by the pmp package'  # add a column indicating the order the samples were measured in DE$sample_meta$run_order = 1:nrow(DE)  # add a column indicating if the sample is biological or a QC Type=as.character(DE$sample_meta$Class) Type[Type != 'QC'] = 'Sample' DE$sample_meta$Type = factor(Type)  # add a column for plotting batches DE$sample_meta$batch_qc = DE$sample_meta$Batch DE$sample_meta$batch_qc[DE$sample_meta$Type=='QC']='QC'  # convert to factors DE$sample_meta$Batch = factor(DE$sample_meta$Batch) DE$sample_meta$Type = factor(DE$sample_meta$Type) DE$sample_meta$Class = factor(DE$sample_meta$Class) DE$sample_meta$batch_qc = factor(DE$sample_meta$batch_qc)  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          MTBLS79 ## description:   Converted from SE provided by the pmp package ## data:          172 rows x 2488 columns ## sample_meta:   172 rows x 7 columns ## variable_meta: 2488 rows x 0 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"signal-drift-and-batch-correction","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Signal drift and batch correction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"batch correction algorithm applied reduce intra- inter- batch variations dataset. Quality Control-Robust Spline Correction (QC-RSC) provided pmp package, wrapped structToolbox object called sb_corr. figure shows plot feature vs run order, correction. fitted spline batch shown grey. can seen correction removed instrument drift within batches.   additional step added published workflow remove feature corrected QCRCMS. can occur enough measured QC values within batch. QCRMS pmp package currently returns NA samples feature occurs. Features occurs excluded. output step output MTBLS79_DatasetExperiment(filtered=FALSE).","code":"M = # batch correction     sb_corr(       order_col='run_order',       batch_col='Batch',        qc_col='Type',        qc_label='QC',       spar_lim = c(0.6,0.8)      )  M = model_apply(M,DE) C = feature_profile(       run_order='run_order',       qc_label='QC',       qc_column='Type',       colour_by='batch_qc',       feature_to_plot='200.03196',       plot_sd=FALSE   )  # plot and modify using ggplot2  chart_plot(C,M,DE)+ylab('Peak area')+ggtitle('Before') chart_plot(C,predicted(M))+ylab('Peak area')+ggtitle('After') M2 = filter_na_count(       threshold=3,       factor_name='Batch'     ) M2 = model_apply(M2,predicted(M))  # calculate number of features removed nc = ncol(DE) - ncol(predicted(M2))  cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 425"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"feature-filtering","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Feature filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"journal article three spectral cleaning steps applied. first filter Kruskal-Wallis test used identify features reliably detected QC samples (p < 0.0001) batches. follow parameters original article use multiple test correction (mtc = 'none'). make use univariate tests kw_rank_sum filter advanced features struct needed. Slots predicted, seq_in used ensure correct output univariate test connected correct input feature filter using filter_by_name. Another slot seq_fcn used extract relevant column predicted output compatible seq_in input. placeholder used “names” parameter (names = 'place_holder') input replaced output seq_fcn. second filter Wilcoxon Signed-Rank test. used identify features representative average biological samples (p < 1e-14). make use seq_in seq_fcn. Finally, RSD filter used remove features high analytical variation (QC RSD > 20 removed) output filter output MTBLS79_DatasetExperiment(filtered=TRUE).","code":"M3 = kw_rank_sum(       alpha=0.0001,       mtc='none',       factor_names='Batch',       predicted='significant'     ) +     filter_by_name(       mode='exclude',       dimension = 'variable',       seq_in = 'names',        names='seq_fcn', # this is a placeholder and will be replaced by seq_fcn       seq_fcn=function(x){return(x[,1])}     ) M3 = model_apply(M3, predicted(M2))  nc = ncol(predicted(M2)) - ncol(predicted(M3)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 262 M4 = wilcox_test(       alpha=1e-14,       factor_names='Type',        mtc='none',        predicted = 'significant'     ) +     filter_by_name(       mode='exclude',       dimension='variable',       seq_in='names',        names='place_holder',       seq_fcn=function(x){return(x$significant)}     ) M4 = model_apply(M4, predicted(M3))  nc = ncol(predicted(M3)) - ncol(predicted(M4)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 169 M5 = rsd_filter(      rsd_threshold=20,      factor_name='Type' ) M5 = model_apply(M5,predicted(M4))  nc = ncol(predicted(M4)) - ncol(predicted(M5)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 53"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"normalisation-missing-value-imputation-and-scaling","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Normalisation, missing value imputation and scaling","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"apply number common pre-processing steps filtered peak matrix identical steps applied described Kirwan et al. 2013, 2014. Probabilistic Quotient Normalisation (PQN) k-nearest neighbours imputation (k = 5) Generalised log transform (glog) steps prepare data multivariate analysis accounting sample concentration differences, imputing missing values scaling data.","code":"# peak matrix processing M6 = pqn_norm(qc_label='QC',factor_name='Type') +       knn_impute(neighbours=5) +      glog_transform(qc_label='QC',factor_name='Type') M6 = model_apply(M6,predicted(M5))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Exploratory Analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Principal Component Analysis (PCA) can used visualise high-dimensional data. unsupervised method maximises variance reduced number latent variables, principal components.  plot similar Figure 3b original publication link. Sample replicates represented colours samples groups (C = cow S = Sheep) different shapes. Plotting scores colouring Batch indicates signal/batch correction effective batches overlapping.","code":"# PCA M7  = mean_centre() + PCA(number_components = 2)  # apply model sequence to data M7 = model_apply(M7,predicted(M6))  # plot pca scores C = pca_scores_plot(factor_name=c('Sample_Rep','Class'),ellipse='none') chart_plot(C,M7[2]) + coord_fixed() +guides(colour=FALSE) ## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use \"none\" instead as ## of ggplot2 3.3.4. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. # chart object C = pca_scores_plot(factor_name=c('Batch'),ellipse='none') # plot chart_plot(C,M7[2]) + coord_fixed()"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-3","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette demonstrate 1) apply validate Partial Least Squares (PLS) analysis using structToolbox, 2) reproduce statistical analysis Thevenot et al. (2015) 3. compare different implementations PLS.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-2","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"objective original study : > …study influence age, body mass index (bmi), gender metabolite concentrations urine, analysing 183 samples cohort adults liquid chromatography coupled high-resolution mass spectrometry. Thevenot et al. (2015) “Sacurine” dataset needs converted DatasetExperiment object. ropls package provides data list containing dataMatrix, sampleMetadata variableMetadata.","code":"data('sacurine',package = 'ropls') # the 'sacurine' list should now be available  # move the annotations to a new column and rename the features by index to avoid issues # later when data.frames get transposed and names get checked/changed sacurine$variableMetadata$annotation=rownames(sacurine$variableMetadata) rownames(sacurine$variableMetadata)=1:nrow(sacurine$variableMetadata) colnames(sacurine$dataMatrix)=1:ncol(sacurine$dataMatrix)  # create DatasetExperiment DE = DatasetExperiment(data = data.frame(sacurine$dataMatrix),                        sample_meta = sacurine$sampleMetadata,                        variable_meta = sacurine$variableMetadata,                        name = 'Sacurine data',                        description = 'See ropls package documentation for details')  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Sacurine data ## description:   See ropls package documentation for details ## data:          183 rows x 109 columns ## sample_meta:   183 rows x 3 columns ## variable_meta: 109 rows x 4 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Sacurine dataset used within vignette already pre-processed: signal drift batch effect correction intensities, urine profile normalized osmolality sample. Finally, data log10 transformed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-data-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Exploratory data analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Since data already processed data can visualised using Principal Component Analysis (PCA) without pre-processing. ropls package automatically applies unit variance scaling (autoscaling) default. approach applied .  third plot coloured gender (C) identical Figure 2 ropls package vignette. structToolbox package provides range PCA-related diagnostic plots, including D-statistic, scree, loadings plots. plots can used explore variance data.","code":"# prepare model sequence M = autoscale() + PCA(number_components = 5) # apply model sequence to dataset M = model_apply(M,DE)  # pca scores plots g=list() for (k in colnames(DE$sample_meta)) {     C = pca_scores_plot(factor_name = k)     g[[k]] = chart_plot(C,M[2]) } # plot using cowplot plot_grid(plotlist=g, nrow=1, align='vh', labels=c('A','B','C')) C = pca_scree_plot() g1 = chart_plot(C,M[2])  C = pca_loadings_plot() g2 = chart_plot(C,M[2])  C = pca_dstat_plot(alpha=0.95) g3 = chart_plot(C,M[2])  p1=plot_grid(plotlist = list(g1,g2),align='h',nrow=1,axis='b') p2=plot_grid(plotlist = list(g3),nrow=1) plot_grid(p1,p2,nrow=2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"partial-least-squares-pls-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Partial Least Squares (PLS) analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"ropls package uses implementation (O)PLS algorithms. structToolbox uses pls package, interesting compare outputs approaches. simplicity scores plots compared.  plot similar fig.3 ropls vignette. Differences due inverted LV axes, common occurrence NIPALS algorithm (used structToolbox ropls) depends algorithm initialised. compare R2 values model structToolbox use regression model, instead discriminant model. convert gender factor numeric variable applying model.  ropls package automatically applies cross-validation asses performance PLSDA model. structToolbox applied separately give control approach used desired. default cross-validation used ropls package 7-fold cross-validation replicate . Training set R2: 0.6975706 0.6798415 0.646671 0.6532914 0.7109769 0.670777 0.6935344 Test set Q2: 0.5460723 validity model can assessed using permutation testing. return discriminant model.  permuted models balanced accuracy around 50%, expected dataset two groups. unpermuted models balanced accuracy around 90% therefore much better might expected occur chance.","code":"# prepare model sequence M = autoscale() + PLSDA(factor_name='gender') M = model_apply(M,DE)  C = pls_scores_plot(factor_name = 'gender') chart_plot(C,M[2]) # convert gender to numeric DE$sample_meta$gender=as.numeric(DE$sample_meta$gender)  # models sequence M = autoscale(mode='both') + PLSR(factor_name='gender',number_components=3) M = model_apply(M,DE)  # some diagnostic charts C = plsr_cook_dist() g1 = chart_plot(C,M[2])  C = plsr_prediction_plot() g2 = chart_plot(C,M[2])  C = plsr_qq_plot() g3 = chart_plot(C,M[2])  C = plsr_residual_hist() g4 = chart_plot(C,M[2])  plot_grid(plotlist = list(g1,g2,g3,g4), nrow=2,align='vh') # model sequence M = kfold_xval(folds=7, factor_name='gender') *      (autoscale(mode='both') + PLSR(factor_name='gender')) M = run(M,DE,r_squared()) # reset gender to original factor DE$sample_meta$gender=sacurine$sampleMetadata$gender # model sequence M = permutation_test(number_of_permutations = 10, factor_name='gender') *     kfold_xval(folds=7,factor_name='gender') *     (autoscale() + PLSDA(factor_name='gender',number_components = 3)) M = run(M,DE,balanced_accuracy())  C = permutation_test_plot(style='boxplot') chart_plot(C,M)+ylab('1 - balanced accuracy')"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-4","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"purpose vignette demonstrate different functionalities methods available part structToolbox reproduce data analysis reported Mendez et al., (2020) Chan et al., (2016).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-3","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). experimental raw data available Metabolomics Workbench (PR000699) processed version available Excel data file. first step need reorganise convert Excel data file DatasetExperiment object. Using openxlsx package file can read directly R data.frame manipulated required.","code":"url = 'https://github.com/CIMCB/MetabWorkflowTutorial/raw/master/GastricCancer_NMR.xlsx'  # read in file directly from github... # X=read.xlsx(url)  # ...or use BiocFileCache path = bfcrpath(bfc,url) ## adding rname 'https://github.com/CIMCB/MetabWorkflowTutorial/raw/master/GastricCancer_NMR.xlsx' X = read.xlsx(path)  # sample meta data SM=X[,1:4] rownames(SM)=SM$SampleID # convert to factors SM$SampleType=factor(SM$SampleType) SM$Class=factor(SM$Class) # keep a numeric version of class for regression SM$Class_num = as.numeric(SM$Class)  ## data matrix # remove meta data X[,1:4]=NULL rownames(X)=SM$SampleID  # feature meta data VM=data.frame(idx=1:ncol(X)) rownames(VM)=colnames(X)  # prepare DatasetExperiment DE = DatasetExperiment(     data=X,     sample_meta=SM,     variable_meta=VM,     description='1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer',     name='Gastric cancer (NMR)')  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 149 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 149 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-pre-processing-and-quality-assessment","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Data pre-processing and quality assessment","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"good practice remove features may low quality, assess quality data general. Tutorial features QC-RSD > 20% 10% features missing retained. Note additional feature vs processing reported Mendez et. al. filters use >= <= instead > <. suitable scaling transformation PCA can used assess data quality. expected biological variance (samples) larger technical variance (QCs). workflow reproducing (link) following steps applied: log10 transform autoscaling (scaled unit variance) knn imputation (3 neighbours) transformed scaled matrix used input PCA. Using struct can chain steps single model sequence.","code":"# prepare model sequence M = rsd_filter(rsd_threshold=20,qc_label='QC',factor_name='Class') +     mv_feature_filter(threshold = 10,method='across',factor_name='Class')  # apply model M = model_apply(M,DE)  # get the model output filtered = predicted(M)  # summary of filtered data filtered ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # prepare the model sequence M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3) +     PCA(number_components = 10)  # apply model sequence to data M = model_apply(M,filtered)  # get the transformed, scaled and imputed matrix TSI = predicted(M[3])  # scores plot C = pca_scores_plot(factor_name = 'SampleType') g1 = chart_plot(C,M[4])  # loadings plot C = pca_loadings_plot() g2 = chart_plot(C,M[4])  plot_grid(g1,g2,align='hv',nrow=1,axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"univariate-statistics","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Univariate statistics","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"structToolbox provides number objects ttest, counting numbers features etc. brevity ttest calculated comparison workflow following (link). QC samples need excluded, data reduced GC groups.","code":"# prepare model TT = filter_smeta(mode='include',factor_name='Class',levels=c('GC','HE')) +        ttest(alpha=0.05,mtc='fdr',factor_names='Class')  # apply model TT = model_apply(TT,filtered)  # keep the data filtered by group for later filtered = predicted(TT[1])  # convert to data frame out=as_data_frame(TT[2])  # show first few features head(out) ##     t_statistic   t_p_value t_significant estimate.mean.GC estimate.mean.HE ## M4   -3.5392652 0.008421042          TRUE         26.47778         51.73947 ## M5    1.4296604 0.410396437         FALSE        265.11860        169.91500 ## M7    2.7456506 0.051494976         FALSE        118.52558         53.98718 ## M8   -2.1294198 0.178392032         FALSE         54.39535         79.26750 ## M11   0.5106536 0.776939682         FALSE        201.34390        171.27949 ## M14  -1.4786810 0.403091881         FALSE         61.53171         83.90250 ##         lower      upper ## M4  -39.56162 -10.961769 ## M5  -38.04747 228.454679 ## M7   17.60818 111.468619 ## M8  -48.20069  -1.543611 ## M11 -87.30604 147.434869 ## M14 -52.57754   7.835950"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"training-and-test-sets","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Training and Test sets","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Splitting data training test sets important aspect machine learning. structToolbox implemented using split_data object random subsampling across whole dataset, stratified_split splitting based group sizes, approach used Mendez et al.","code":"# prepare model M = stratified_split(p_train=0.75,factor_name='Class') # apply to filtered data M = model_apply(M,filtered) # get data from object train = M$training train ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Training set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a training set ## data:          62 rows x 53 columns ## sample_meta:   62 rows x 5 columns ## variable_meta: 53 rows x 1 columns cat('\\n') test = M$testing test ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Testing set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a test set ## data:          21 rows x 53 columns ## sample_meta:   21 rows x 5 columns ## variable_meta: 53 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"optimal-number-of-pls-components","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Optimal number of PLS components","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Mendez et al k-fold cross-validation used determine optimal number PLS components. 100 bootstrap iterations used generate confidence intervals. strucToolbox implemented using “iterator” objects, can combined model objects. R2 used metric optimisation, PLSR model structToolbox used. speed 10 bootstrap iterations used .  chart plotted shows Q2, comparable Figure 13 Mendez et al . Two components selected Mendez et al, use .","code":"# scale/transform training data M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3,by='samples')  # apply model M = model_apply(M,train)  # get scaled/transformed training data train_st = predicted(M)  # prepare model sequence MS = grid_search_1d(         param_to_optimise = 'number_components',         search_values = as.numeric(c(1:6)),         model_index = 2,         factor_name = 'Class_num',         max_min = 'max') *      permute_sample_order(         number_of_permutations = 10) *      kfold_xval(         folds = 5,         factor_name = 'Class_num') *       (mean_centre(mode='sample_meta')+       PLSR(factor_name='Class_num'))  # run the validation MS = struct::run(MS,train_st,r_squared())  # C = gs_line() chart_plot(C,MS)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-model-evalutation","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS model evalutation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"evaluate model discriminant analysis structToolbox PLSDA model appropriate.  Note default cutoff B figure PLS models structToolbox 0, groups encoded +/-1. impact overall performance model.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  # charts C = plsda_predicted_plot(factor_name='Class',style='boxplot') g1 = chart_plot(C,P)  C = plsda_predicted_plot(factor_name='Class',style='density') g2 = chart_plot(C,P)+xlim(c(-2,2))  C = plsda_roc_plot(factor_name='Class') g3 = chart_plot(C,P)  plot_grid(g1,g2,g3,align='vh',axis='tblr',nrow=1, labels=c('A','B','C')) # AUC for comparison with Mendez et al MET = calculate(AUC(),P$y$Class,P$yhat[,1]) MET ## A \"AUC\" object ## -------------- ## name:          Area under ROC curve ## description:   The area under the ROC curve of a classifier is estimated using the trapezoid method. ## value:         0.9739583"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"permutation-test","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Permutation test","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"permutation test can used assess likely observed result occurred chance. structToolbox permutation_test iterator object can combined iterators models.  plot comparable bottom half Figure 17 Mendez et. al.. unpermuted (true) Q2 values consistently better permuted (null) models. .e. model reliable.","code":"# model sequence MS = permutation_test(number_of_permutations = 20,factor_name = 'Class_num') *       kfold_xval(folds = 5,factor_name = 'Class_num') *      (mean_centre(mode='sample_meta') + PLSR(factor_name='Class_num', number_components = 2))  # run iterator MS = struct::run(MS,train_st,r_squared())  # chart C = permutation_test_plot(style = 'density')  chart_plot(C,MS) + xlim(c(-1,1)) + xlab('R Squared')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-projection-plots","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS projection plots","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PLS can also used visualise model interpret latent variables.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  C = pls_scores_plot(components=c(1,2),factor_name = 'Class') chart_plot(C,P)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-feature-importance","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS feature importance","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Regression coefficients VIP scores can used estimate importance individual features PLS model. Mendez et al bootstrapping used estimate confidence intervals, brevity skip .","code":"# prepare chart C = pls_vip_plot(ycol = 'HE') g1 = chart_plot(C,P)  C = pls_regcoeff_plot(ycol='HE') g2 = chart_plot(C,P)  plot_grid(g1,g2,align='hv',axis='tblr',nrow=2)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-5","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette illustrate apply SVM analysis Classifying Metabolomics data. Support vector Machines (SVM) commonly used method Machine Learning. classification tasks used generate boundary groups samples training set. well generating linear boundaries, SVM can extended exploit use kernels generate complex non-linear boundaries groups required. structToolbox package, SVM functionality provided e1071 package incorporated model object. chart object (svm_plot_2d) also available plot SVM boundaries data two variables.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-4","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). raw experimental data available Metabolomics Workbench (PR000699) processed version available Excel data file. simplicity use pre-processed version 1H-NMR “Gastric cancer” dataset using structToolbox package. Details regards pre-processing reported “NMR_clinical_metabolomics” vignette `r Biocpkg(“structToolbox”) package. purposes illustrating effect SVM parameters boundary groups, reduce data include GC groups apply PLS reduce data two components. treat PLS scores two group dataset two features.","code":"# summary of DatasetExperiment object DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # model sequence and pls model (NB data already centred) MS = filter_smeta(mode = 'include', levels = c('GC','HE'), factor_name = 'Class') +      PLSDA(factor_name = 'Class',number_components = 2)  # apply PLS model MS = model_apply(MS,DE)  # plot the data C = pls_scores_plot(factor_name = 'Class') chart_plot(C,MS[2]) # new DatasetExperiment object from the PLS scores DE2 = DatasetExperiment(   data = MS[2]$scores$data,    sample_meta = predicted(MS[1])$sample_meta,   variable_meta = data.frame('LV'=c(1,2),row.names = colnames(MS[2]$scores)),   name = 'Illustrativate SVM dataset',   description = 'Generated by applying PLS to the processed Gastric cancer (NMR) dataset' )  DE2 ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Illustrativate SVM dataset ## description:   Generated by applying PLS to the processed Gastric cancer (NMR) dataset ## data:          83 rows x 2 columns ## sample_meta:   83 rows x 5 columns ## variable_meta: 2 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"basic-svm-model","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Basic SVM model","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"simplest SVM model uses linear kernel. structToolbox SVM model can used train apply SVM models. svm_plot_2d chart object provided visualisation boundaries two dimensions.  SVM boundary plotted black, margins grey support vectors indicated grey circles.","code":"# SVM model M = SVM(   factor_name = 'Class',   kernel = 'linear' )  # apply model M = model_apply(M,DE2)  # plot boundary C = svm_plot_2d(factor_name = 'Class') chart_plot(C,M, DE2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"svm-cost-function","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"SVM cost function","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"SVM cost function applies penalty samples wrong side margins. high penalty results narrow margin tries force samples correct side boundary. low penalty makes wider margin less strict samples misclassified. optimal cost use data dependent.","code":"# low cost M$cost=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium cost M$cost=0.05 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high cost M$cost=100 M=model_apply(M,DE2)  C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Low cost\", \"Medium cost\", \"High cost\"),   hjust = -1,   nrow = 2 )  legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") )  plot_grid(prow, legend, ncol=1, rel_heights = c(1, .1))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"kernel-functions","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Kernel functions","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"number different kernels can used support vector machines. structToolbox wrapper ‘linear’, ‘polynomial’,‘radial’ ‘sigmoid’ kernels can specified. Using kernels allows boundary flexible, often require additional parameters specified. best kernel use vary depending dataset, common choice radial kernel allows high flexibility single parameter.  parameters kernel can used control complexity boundary. show radial kernel parameter “gamma” can used change complexity boundary. combination cost parameter (keep constant ) allows highly flexible boundary models.  Note best practice select optimal kernel parameter(s) combination cost parameter (e.g. 2d grid search) best combination identified.","code":"# set a fixed cost for this comparison M$cost=1  # linear kernel M$kernel='linear' M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # polynomial kernel M$kernel='polynomial' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # rbf kernel M$kernel='radial' M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # sigmoid kernel M$kernel='sigmoid' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g4=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   g4 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Linear\", \"Polynomial\", \"Radial\",\"Sigmoid\"),   hjust = -1,   nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +     theme(legend.position = \"bottom\") ) plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1)) # rbf kernel and cost M$kernel = 'radial' M$cost = 1  # low gamma M$gamma=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium gamma M$gamma=0.1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high gamma M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(  g1 + theme(legend.position=\"none\"),  g2 + theme(legend.position=\"none\"),  g3 + theme(legend.position=\"none\"),  align = 'vh',  labels = c(\"Low gamma\", \"Medium gamma\", \"High gamma\"),  hjust = -1,  nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") ) plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1))"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-6","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette conduct data preprocessing exploratory analysis data STATegra project (https://www.nature.com/articles/s41597-019-0202-7). demonstration purposes focus Proteomics Metabolomics datasets publicly available part STATegra multi-omics dataset. …STATegra multi-omics dataset combines measurements 10 different omics technologies applied biological system, namely well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data. Gomez-Cabrero et al STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-proteomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based proteomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based proteomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox. number Reporter genes included study. plot two illustrate trends data.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Proteomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(path, files = \"Proteomics_01_uniprot_canonical_normalized.txt\", exdir=temp) # read samples all_data <-  read.delim(file.path(temp,\"Proteomics_01_uniprot_canonical_normalized.txt\"), as.is = TRUE, header = TRUE, sep = \"\\t\") # extract data matrix data = all_data[1:2527,51:86] # shorten sample names colnames(data) = lapply(colnames(data), function (x) substr(x, 27, nchar(x))) # replace 0 with NA data[data == 0] <- NA # transpose data=as.data.frame(t(data))  # prepare sample meta SM = lapply(rownames(data),function(x) {   s=strsplit(x,'_')[[1]] # split at underscore   out=data.frame(     'treatment' = s[[1]],     'time' = substr(s[[2]],1,nchar(s[[2]])-1) ,         'batch' = substr(s[[3]],6,nchar(s[[3]])),     'condition' = substr(x,1,6) # interaction between treatment and time   )   return(out) }) SM = do.call(rbind,SM) rownames(SM)=rownames(data) # convert to factors SM$treatment=factor(SM$treatment) SM$time=ordered(SM$time,c(\"0\",\"2\",\"6\",\"12\",\"18\",\"24\")) SM$batch=ordered(SM$batch,c(1,3,4,5,6,7)) SM$condition=factor(SM$condition)  # variable meta data VM = all_data[1:2527,c(1,6,7)] rownames(VM)=colnames(data)  # prepare DatasetExperiment DS = DatasetExperiment(   data = data,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Proteomics',   description = 'downloaded from: https://github.com/STATegraData/STATegraData/' ) DS ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 2527 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 2527 rows x 3 columns # find id of reporters Ldha = which(DS$variable_meta$Gene.names=='Ldha') Hk2 = which(DS$variable_meta$Gene.names=='Hk2')  # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DS)+ggtitle('Ldha')+ylab('expression') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DS)+ggtitle('Hk2')+ylab('expression')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-transformation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Data transformation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"data log2 transformed, scaled mean medians equal conditions. steps available structToolbox using log_transform mean_of_medians objects. Reporter genes plotted comparison.","code":"# prepare model sequence M = log_transform(   base=2) +    mean_of_medians(     factor_name = 'condition') # apply model sequence M = model_apply(M,DS)   # get transformed data DST = predicted(M) # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DST)+ggtitle('Ldha')+ylab('log2(expression)') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DST)+ggtitle('Hk2')+ylab('log2(expression)')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-filtering","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Missing value filtering involves removing feature (gene) least 3 missing values per group least 11 samples. specific filter structToolbox time, can achieved combining filter_na_count filter_by_name objects. Specifically, default output filter_na_count changed return matrix NA counts per class. output connected ‘names’ input filter_by_names converted TRUE/FALSE using ‘seq_fcn’ input. ‘seq_fcn’ function processes NA counts used inputs filter_by_names. data passed along model sequence passes unchanged filter_na_count object becuase default output changed, filter_na_count filter_by_name objects working together single filter.","code":"# build model sequence M2 = filter_na_count(   threshold=2,   factor_name='condition',    predicted='na_count') +    # override the default output   filter_by_name(     mode='exclude',     dimension='variable',     names='place_holder',     seq_in='names',     seq_fcn=function(x) {      # convert NA count pre group to true/false       x=x>2                  # more the two missing per group       x=rowSums(x)>10        # in more than 10 groups       return(x)     }   ) # apply to transformed data M2 = model_apply(M2,DST)  # get the filtered data DSTF = predicted(M2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-imputation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value imputation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra uses two imputation methods available struct objects, create temporary STATegra_impute objects using functions struct package. first imputation method imputes missing values treatment values missing samples using “random value discovery”. create new struct object using set_struct_obj global environment, “method_apply” method implements imputation. second imputation method replacing missing values condition exactly 1 missing value mean values condition. create new struct object corresponding method new object implement filter. new STATegra imputation objects can now used model sequences like struct object. final filter added remove feature missing values imputation.","code":"# create new imputation object set_struct_obj(   class_name = 'STATegra_impute1',   struct_obj = 'model',   params=c(factor_sd='character',factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 1',     description = 'If missing values are present for all one group then they are replaced with min/2 + \"random value below discovery\".',     predicted = 'imputed'   )  )  # create method_apply for imputation method 1 set_obj_method(   class_name='STATegra_impute1',   method_name='model_apply',   definition=function(M,D) {          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })     # count number of samples in each group     count=summary(D$sample_meta[[M$factor_name]])     # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_sd]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == matrix(count,nrow=2,ncol=ncol(D)) # all missing in one class          # impute matrix     mi = D$data     for (j in 1:nrow(mi)) {       # index of group for this sample       g = which(levels(D$sample_meta[[M$factor_name]])==D$sample_meta[[M$factor_name]][j])       iv=rnorm(ncol(D),min(D$data[j,],na.rm=TRUE)/2,sd)       mi[j,is.na(mi[j,]) & check[g,]] = iv[is.na(mi[j,]) & check[g,]]     }     D$data = mi     M$imputed=D     return(M)   } ) # create new imputation object set_struct_obj(   class_name = 'STATegra_impute2',   struct_obj = 'model',   params=c(factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 2',     description = 'For those conditions with only 1 NA impute with the mean of the condition.',     predicted = 'imputed'   )  )  # create method_apply for imputation method 2 set_obj_method(   class_name='STATegra_impute2',   method_name='model_apply',   definition=function(M,D) {     # levels in condition     L = levels(D$sample_meta[[M$factor_name]])          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })          # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_name]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == 1 # only one missing for a condition          # index of samples for each condition     IDX = list()     for (k in L) {       IDX[[k]]=which(D$sample_meta[[M$factor_name]]==k)     }          ## impute     # for each feature     for (k in 1:ncol(D)) {       # for each condition       for (j in 1:length(L)) {         # if passes test         if (check[j,k]) {           # mean of samples in group           m = mean(D$data[IDX[[j]],k],na.rm=TRUE)           # imputed value           im = rnorm(1,m,sd)           # replace NA with imputed           D$data[is.na(D$data[,k]) & D$sample_meta[[M$factor_name]]==L[j],k]=im         }       }     }     M$imputed=D     return(M)   } ) # model sequence M3 = STATegra_impute1(factor_name='treatment',factor_sd='condition') +      STATegra_impute2(factor_name = 'condition') +       filter_na_count(threshold = 3, factor_name='condition') # apply model M3 = model_apply(M3,DSTF) # get imputed data DSTFI = predicted(M3) DSTFI ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 864 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 864 rows x 3 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"often useful visualise distribution values across samples verify transformations/normalisation/filtering etc effective.  values longer skewed show approximately normal distribution. boxplots comparable width outliers indicated, transformations etc overall positive effect. PCA used provide graphical representation data. comparison outputs STATegra filter included reduce data include treated samples (IKA)  appear strong batch effect. PC1 dominated time point “24” potentially outlying points time points “2” “0”.","code":"# model sequence P = filter_smeta(mode='include',factor_name='treatment',levels='IKA') +     mean_centre() +      PCA(number_components = 2) # apply model P = model_apply(P,DSTFI)  # scores plots coloured by factors g = list() for (k in c('batch','time')) {   C = pca_scores_plot(factor_name=k,ellipse='none')   g[[k]]=chart_plot(C,P[3]) }  plot_grid(plotlist = g,nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-metabolomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based metabolomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based metabolomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Metabolomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(zipfile=path, files = \"LC_MS_raw_data.xlsx\", exdir=temp)  # read samples data <- as.data.frame(read.xlsx(file.path(temp,\"LC_MS_raw_data.xlsx\"),sheet = 'Data')) # extract sample meta data SM = data[ ,1:8]  # add coloumn for sample type (QC, blank etc) blanks=c(1,2,33,34,65,66) QCs=c(3,4,11,18,25,32,35,36,43,50,57,64) SM$sample_type='Sample' SM$sample_type[blanks]='Blank' SM$sample_type[QCs]='QC'  # put qc/blank labels in all factors for plotting later SM$biol.batch[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$time.point[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$condition[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample']  # convert to factors SM$biol.batch=ordered(SM$biol.batch,c('9','10','11','12','QC','Blank')) SM$time.point=ordered(SM$time.point,c('0h','2h','6h','12h','18h','24h','QC','Blank')) SM$condition=factor(SM$condition) SM$sample_type=factor(SM$sample_type)  # variable meta data VM = data.frame('annotation'=colnames(data)[9:ncol(data)])  # raw data X = data[,9:ncol(data)] # convert 0 to NA X[X==0]=NA # force to numeric; any non-numerics will become NA X=data.frame(lapply(X,as.numeric),check.names = FALSE)  # make sure row/col names match rownames(X)=data$label rownames(SM)=data$label rownames(VM)=colnames(X)   # create DatasetExperiment object DE = DatasetExperiment(   data = X,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Metabolomics LCMS',   description = 'https://www.nature.com/articles/s41597-019-0202-7' )  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Metabolomics LCMS ## description:   https://www.nature.com/articles/s41597-019-0202-7 ## data:          66 rows x 152 columns ## sample_meta:   66 rows x 9 columns ## variable_meta: 152 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra project LCMS data combined GCMS data multiblock analysis conducted. LCMS explored, data processed differently comparison Gomez-Cabrero et al. basic processing steps applied order generate valid PCA plot biological QC samples.","code":"# prepare model sequence MS = filter_smeta(mode = 'include', levels='QC', factor_name = 'sample_type') +      knn_impute(neighbours=5) +      vec_norm() +       log_transform(base = 10)   # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 3 rows with more than 50 % entries missing; ##  mean imputation used for these rows"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-2","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"First use PCA look QC samples order make assessment data quality.  QC labelled “36” clearly different QCs. STATegra QC removed, exclude well. corresponds QC H1. STATegra also excluded QC samples measured immediately blank, also .  Now plot QC samples context samples. several possible approaches, apply approach applying PCA full dataset including QCs. exclude blanks likely dominate plot removed. samples batch 12 excluded STATegra replicate . QCs appear representative samples, strong clusters data, including QC samples biological variation. likely number ‘low quality’ features excluded, now, use sophisticated normalisation (PQN) scaling methods (glog).  can see now QCs tightly clustered. indicates biological variance remaining high quality features much greater technical variance represented QCs. appear trend measurement order (), important indicator instrument drift throughout run large source variation dataset. appear strong clustering related biological batch (B). appear strong trend time (C) likely subtle variation might masked sources variance stage. clustering related condition (D) overlap. explore trends time, split data condition factor explore Ikaros group. Removing condition factor variation potentially make easier spot subtle trends. extract glog transformed matrix previous model sequence continue .  Colouring groups () makes time point trend difficult see, adding ggplot continuous colour scale “viridis” (B) trend time along PC1 becomes much clearer.","code":"# pca model sequence M =  mean_centre() +      PCA(number_components = 3)  # apply model M = model_apply(M,predicted(MS))   # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,M[2]) # prepare model sequence MS = filter_smeta(       mode = 'include',        levels='QC',        factor_name = 'sample_type') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1','1358BZU_0001QC_A1','1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 4 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,MS[7]) # prepare model sequence MS = filter_smeta(       mode = 'exclude',        levels='Blank',        factor_name = 'sample_type') +         filter_smeta(       mode = 'exclude',        levels='12',        factor_name = 'biol.batch') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1',                 '1358BZU_0001QC_A1',                 '1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 2 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plots C = pca_scores_plot(factor_name = 'sample_type') # plot chart_plot(C,MS[8]) MS =  filter_smeta(        mode = 'exclude',         levels = '12',         factor_name = 'biol.batch') +          filter_by_name(        mode = 'exclude',         dimension='sample',        names = c('1358BZU_0001QC_H1',                  '1358BZU_0001QC_A1',                  '1358BZU_0001QC_G1')) +        blank_filter(        fold_change = 20,        qc_label = 'QC',        factor_name = 'sample_type') +        filter_smeta(        mode='exclude',        levels='Blank',        factor_name='sample_type') +          mv_feature_filter(        threshold = 80,         qc_label = 'QC',         factor_name = 'sample_type',         method = 'QC') +             mv_feature_filter(         threshold = 50,          factor_name = 'sample_type',          method='across') +         rsd_filter(        rsd_threshold=20,         qc_label='QC',        factor_name='sample_type') +         mv_sample_filter(        mv_threshold = 50) +            pqn_norm(        qc_label='QC',        factor_name='sample_type') +            knn_impute(        neighbours=5,         by='samples') +            glog_transform(        qc_label = 'QC',        factor_name = 'sample_type') +            mean_centre() +             PCA(        number_components = 10)  # apply model sequence MS = model_apply(MS, DE)   # PCA plots using different factors g=list() for (k in c('order','biol.batch','time.point','condition')) {   C = pca_scores_plot(factor_name = k,ellipse='none')   # plot   g[[k]]=chart_plot(C,MS[length(MS)]) }  plot_grid(plotlist = g,align='vh',axis='tblr',nrow=2,labels=c('A','B','C','D')) # get the glog scaled data GL = predicted(MS[11])  # extract the Ikaros group and apply PCA IK = filter_smeta(       mode='include',       factor_name='condition',       levels='Ikaros') +      mean_centre() +       PCA(number_components = 5)  # apply the model sequence to glog transformed data IK = model_apply(IK,GL)  # plot the PCA scores C = pca_scores_plot(factor_name='time.point',ellipse = 'sample') g1=chart_plot(C,IK[3]) g2=g1 + scale_color_viridis_d() # add continuous scale colouring  plot_grid(g1,g2,nrow=2,align='vh',axis = 'tblr',labels=c('A','B'))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"","code":"sessionInfo() ## R version 4.3.1 (2023-06-16) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 22.04.3 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        ##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    ##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           ## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    ##  ## time zone: UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats     graphics  grDevices utils     datasets  methods   base      ##  ## other attached packages: ##  [1] openxlsx_4.2.5.2     cowplot_1.1.1        gridExtra_2.3        ##  [4] ggplot2_3.4.4        BiocFileCache_2.8.0  dbplyr_2.3.4         ##  [7] ropls_1.32.0         pmp_1.12.0           structToolbox_1.13.5 ## [10] struct_1.12.0        BiocStyle_2.28.1     ##  ## loaded via a namespace (and not attached): ##   [1] DBI_1.1.3                   bitops_1.0-7                ##   [3] MultiDataSet_1.28.0         rlang_1.1.1                 ##   [5] magrittr_2.0.3              e1071_1.7-13                ##   [7] matrixStats_1.0.0           compiler_4.3.1              ##   [9] RSQLite_2.3.1               systemfonts_1.0.5           ##  [11] vctrs_0.6.4                 reshape2_1.4.4              ##  [13] stringr_1.5.0               pkgconfig_2.0.3             ##  [15] crayon_1.5.2                fastmap_1.1.1               ##  [17] XVector_0.40.0              labeling_0.4.3              ##  [19] utf8_1.2.3                  rmarkdown_2.25              ##  [21] itertools_0.1-3             ragg_1.2.6                  ##  [23] bit_4.0.5                   purrr_1.0.2                 ##  [25] xfun_0.40                   MultiAssayExperiment_1.26.0 ##  [27] randomForest_4.7-1.1        zlibbioc_1.46.0             ##  [29] cachem_1.0.8                GenomeInfoDb_1.36.4         ##  [31] jsonlite_1.8.7              progress_1.2.2              ##  [33] blob_1.2.4                  DelayedArray_0.26.7         ##  [35] prettyunits_1.2.0           parallel_4.3.1              ##  [37] rols_2.28.0                 R6_2.5.1                    ##  [39] bslib_0.5.1                 stringi_1.7.12              ##  [41] limma_3.56.2                GenomicRanges_1.52.1        ##  [43] jquerylib_0.1.4             Rcpp_1.0.11                 ##  [45] bookdown_0.35               SummarizedExperiment_1.30.2 ##  [47] iterators_1.0.14            knitr_1.44                  ##  [49] IRanges_2.34.1              Matrix_1.6-1.1              ##  [51] tidyselect_1.2.0            abind_1.4-5                 ##  [53] yaml_2.3.7                  codetools_0.2-19            ##  [55] curl_5.1.0                  doRNG_1.8.6                 ##  [57] lattice_0.21-8              tibble_3.2.1                ##  [59] plyr_1.8.9                  withr_2.5.1                 ##  [61] Biobase_2.60.0              evaluate_0.22               ##  [63] ontologyIndex_2.11          desc_1.4.2                  ##  [65] isoband_0.2.7               proxy_0.4-27                ##  [67] zip_2.3.0                   filelock_1.0.2              ##  [69] pillar_1.9.0                BiocManager_1.30.22         ##  [71] MatrixGenerics_1.12.3       rngtools_1.5.2              ##  [73] foreach_1.5.2               stats4_4.3.1                ##  [75] generics_0.1.3              rprojroot_2.0.3             ##  [77] sp_2.1-0                    RCurl_1.98-1.12             ##  [79] hms_1.1.3                   S4Vectors_0.38.2            ##  [81] munsell_0.5.0               scales_1.2.1                ##  [83] calibrate_1.7.7             class_7.3-22                ##  [85] glue_1.6.2                  tools_4.3.1                 ##  [87] fs_1.6.3                    grid_4.3.1                  ##  [89] impute_1.74.1               missForest_1.5              ##  [91] colorspace_2.1-0            GenomeInfoDbData_1.2.10     ##  [93] cli_3.6.1                   textshaping_0.3.7           ##  [95] fansi_1.0.5                 viridisLite_0.4.2           ##  [97] ggthemes_4.2.4              S4Arrays_1.0.6              ##  [99] dplyr_1.1.3                 pls_2.8-2                   ## [101] pcaMethods_1.92.0           gtable_0.3.4                ## [103] sass_0.4.7                  digest_0.6.33               ## [105] BiocGenerics_0.46.0         farver_2.1.1                ## [107] memoise_2.0.1               htmltools_0.5.6.1           ## [109] pkgdown_2.0.7               lifecycle_1.0.3             ## [111] httr_1.4.7                  qqman_0.1.9                 ## [113] bit64_4.0.5                 MASS_7.3-60"},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gavin Rhys Lloyd. Author, maintainer. Ralf Johannes Maria Weber. Author.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lloyd, G., Jankevics, ., Weber, R. 2020. struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond. Bioinformatics, 36(22-23), p.5551-5552.","code":"@Article{,   title = {struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond},   author = {Gavin Rhys Lloyd and Andris Jankevics and Ralf Johannes Maria Weber},   journal = {Bioinformatics},   year = {2020},   volume = {36},   number = {22-23},   pages = {5551-5552},   url = {https://doi.org/10.1093/bioinformatics/btaa1031}, }"},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"structtoolbox","dir":"","previous_headings":"","what":"Data processing & analysis tools for Metabolomics and other omics","title":"Data processing & analysis tools for Metabolomics and other omics","text":"extensive set data (pre-)processing analysis methods tools metabolomics omics, strong emphasis statistics machine learning. toolbox allows user build extensive standardised workflows data analysis. methods tools implemented using class-based templates provided struct (Statistics R Using Class-based Templates) package. toolbox includes pre-processing methods (e.g. signal drift batch correction, normalisation, missing value imputation scaling), univariate (e.g. ttest, various forms ANOVA, Kruskal–Wallis test ) multivariate statistical methods (e.g. PCA PLS, including cross-validation permutation testing) well machine learning methods (e.g. Support Vector Machines). STATistics Ontology (STATO) integrated implemented provide standardised definitions different methods, inputs outputs.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Data processing & analysis tools for Metabolomics and other omics","text":"install package: install development version:","code":"if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"structToolbox\") if (!require(\"remotes\", quietly = TRUE))     install.packages(\"remotes\")  remotes::install_github(\"computational-metabolomics/structToolbox\")"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis of Variance — ANOVA","title":"Analysis of Variance — ANOVA","text":"Analysis Variance (ANOVA) univariate method used analyse difference among group means. Multiple test corrected p-values computed indicate significance feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis of Variance — ANOVA","text":"","code":"ANOVA(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"III\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis of Variance — ANOVA","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) ANOVA sum squares. Allowed values limited following: \"II\": Type II sum squares. \"III\": Type III sum squares. default \"III\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis of Variance — ANOVA","text":" ANOVA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analysis of Variance — ANOVA","text":"object makes use functionality following packages: car","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Analysis of Variance — ANOVA","text":"Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analysis of Variance — ANOVA","text":"","code":"D = iris_DatasetExperiment() M = ANOVA(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":null,"dir":"Reference","previous_headings":"","what":"Area under ROC curve — AUC","title":"Area under ROC curve — AUC","text":"area ROC curve classifier estimated using trapezoid method.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area under ROC curve — AUC","text":"","code":"AUC(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Area under ROC curve — AUC","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Area under ROC curve — AUC","text":"  AUC  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Area under ROC curve — AUC","text":"","code":"D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = AUC() XCV = run(XCV,D,MET) #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":null,"dir":"Reference","previous_headings":"","what":"Discriminant Factor Analysis — DFA","title":"Discriminant Factor Analysis — DFA","text":"Discriminant Factor Analysis (DFA) supervised classification method. Using linear combination input variables, DFA finds new orthogonal axes (canonical values) minimize variance within given class maximize variance classes.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discriminant Factor Analysis — DFA","text":"","code":"DFA(factor_name, number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discriminant Factor Analysis — DFA","text":"factor_name (character) name sample-meta column use. number_components (numeric, integer) number DFA components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discriminant Factor Analysis — DFA","text":" DFA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Discriminant Factor Analysis — DFA","text":"Manly B (1986). Multivariate Statistical Methods: Primer. Chapman Hall, Boca Raton.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discriminant Factor Analysis — DFA","text":"","code":"D = iris_DatasetExperiment() M = DFA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_boxplot","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"boxplot visualise distribution values within subset features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"DatasetExperiment_boxplot(   factor_name,   by_sample = TRUE,   per_class = TRUE,   number = 50,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"factor_name (character) name sample-meta column use. by_sample (logical) Plot sample. Allowed values limited following: \"FALSE\": data plotted across samples subset features. default TRUE. per_class (logical) Plot per class. Allowed values limited following: \"FALSE\": data plotted samples. default TRUE. number (numeric, integer) number features/samples plotted. default 50. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"  DatasetExperiment_boxplot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"D = MTBLS79_DatasetExperiment() C = DatasetExperiment_boxplot(factor_name='Class',number=10,per_class=FALSE) chart_plot(C,D) #> Warning: Removed 585 rows containing non-finite values (`stat_boxplot()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_dist","title":"Feature distribution histogram — DatasetExperiment_dist","text":"histogram visualise distribution values within features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"DatasetExperiment_dist(factor_name, per_class = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_dist","text":"factor_name (character) name sample-meta column use. per_class (logical) Plot per class. Allowed values limited following: \"FALSE\": distribution plotted samples. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_dist","text":"  DatasetExperiment_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"D = MTBLS79_DatasetExperiment() C = DatasetExperiment_dist(factor_name='Class') chart_plot(C,D) #> Warning: Removed 9222 rows containing non-finite values (`stat_bin()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Factor boxplot — DatasetExperiment_factor_boxplot","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"Boxplot feature visualise distribution values within group","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"DatasetExperiment_factor_boxplot(feature_to_plot, factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"feature_to_plot (character, numeric, integer) name plotted feature. factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"  DatasetExperiment_factor_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"D = iris_DatasetExperiment() C = DatasetExperiment_factor_boxplot(factor_names='Species',feature_to_plot='Petal.Width') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"DatasetExperiment heatmap — DatasetExperiment_heatmap","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"heatmap visualise measured values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"DatasetExperiment_heatmap(na_colour = \"#FF00E4\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"na_colour (character) hex colour code used plot missing values. default \"#FF00E4\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"  DatasetExperiment_heatmap  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"object makes use functionality following packages: reshape2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"D = iris_DatasetExperiment() C = DatasetExperiment_heatmap() chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Cluster Analysis — HCA","title":"Hierarchical Cluster Analysis — HCA","text":"Hierarchical Cluster Analysis numerical technique uses agglomerative clustering identify clusters groupings samples.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"HCA(   dist_method = \"euclidean\",   cluster_method = \"complete\",   minkowski_power = 2,   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Cluster Analysis — HCA","text":"dist_method (character) Distance measure. Allowed values limited following: \"maximum\": maximum distance. \"manhattan\": absolute distance (1 norm). \"canberra\": weighted version mahattan distance. \"minkowski\": generalisation manhattan euclidean distance nth norm. default \"euclidean\". cluster_method (character) Agglomeration method. Allowed values limited following: \"ward.D2\": Ward clustering using sqaured distances. \"single\": Single linkage. \"complete\": Complete linkage. \"average\": Average linkage (UPGMA). \"mcquitty\": McQuitty linkage (WPGMA). \"median\": Median linkage (WPGMC). \"centroid\": Centroid linkage (UPGMC). default \"complete\". minkowski_power (numeric)  default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Cluster Analysis — HCA","text":" HCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical Cluster Analysis — HCA","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hierarchical Cluster Analysis — HCA","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"D = iris_DatasetExperiment() M = HCA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference — HSD","title":"Tukey's Honest Significant Difference — HSD","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"HSD(alpha = 0.05, mtc = \"fdr\", formula, unbalanced = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference — HSD","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. unbalanced (logical) Unbalanced model. Allowed values limited following: \"FALSE\": correction applied unbalanced designs. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference — HSD","text":" HSD object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference — HSD","text":"object makes use functionality following packages: agricolae","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference — HSD","text":"de Mendiburu F (2023). agricolae: Statistical Procedures Agricultural Research. R package version 1.3-6, https://CRAN.R-project.org/package=agricolae.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"D = iris_DatasetExperiment() M = HSD(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature. mixed effects models estimated marginal means used.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"HSDEM(alpha = 0.05, mtc = \"fdr\", formula, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":" HSDEM object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"object makes use functionality following packages: nlme","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Lenth R (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.8.7, https://CRAN.R-project.org/package=emmeans. Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-162, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = HSDEM(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"Direct-infusion mass spectrometry (DIMS) metabolomics important approach characterising molecular responses organisms disease, drugs environment. Increasingly large-scale metabolomics studies conducted, necessitating improvements bioanalytical computational workflows maintain data quality. dataset represents systematic evaluation reproducibility multi-batch DIMS metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"MTBLS79_DatasetExperiment(filtered = FALSE)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"filtered TRUE load data quality control filters already applied, FALSE load unfiltered data. Default FALSE. raw data available (https://www.ebi.ac.uk/metabolights/MTBLS79) R dataset pmp package, available Bioconductor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"D = MTBLS79_DatasetExperiment() summary(D) #> [1] \"DatasetExperiment object of length 172 with 7 metadata columns\""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSDA","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"OPLSDA(number_components = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"number_components (numeric, integer) number orthgonal components. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":" OPLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSR","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"OPLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"number_components (numeric, integer) number orthgonal components. default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSR","text":" OPLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (PCA) — PCA","title":"Principal Component Analysis (PCA) — PCA","text":"PCA multivariate data reduction technique. summarises data smaller number Principal Components maximise variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"PCA(number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (PCA) — PCA","text":"number_components (numeric, integer) number Principal Components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (PCA) — PCA","text":" PCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"M = PCA()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares discriminant analysis — PLSDA","title":"Partial least squares discriminant analysis — PLSDA","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. Discriminant Analysis variant uses group labels response variable. >2 groups 1-vs-approach used. Group membership can predicted test samples based probability estimate group membership, estimated y-value.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"PLSDA(number_components = 2, factor_name, pred_method = \"max_prob\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares discriminant analysis — PLSDA","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample-meta column use. pred_method (character) Prediction method. Allowed values limited following: \"max_prob\": predicted group selected based largest probability group membership. default \"max_prob\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares discriminant analysis — PLSDA","text":" PLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares discriminant analysis — PLSDA","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares discriminant analysis — PLSDA","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-2, https://CRAN.R-project.org/package=pls. Perez NF, Ferre J, Boque R (2009). \"Calculation reliability classification discriminant partial least-squares binary classification.\" Chemometrics Intelligent Laboratory Systems, 95(2), 122-128. Barker M, Rayens W (2003). \"Partial least squares discrimination.\" Journal Chemometrics, 17(3), 166-173.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"M = PLSDA('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares regression — PLSR","title":"Partial least squares regression — PLSR","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. regression response continuous variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares regression — PLSR","text":"","code":"PLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares regression — PLSR","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares regression — PLSR","text":" PLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares regression — PLSR","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares regression — PLSR","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-2, https://CRAN.R-project.org/package=pls.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares regression — PLSR","text":"","code":"M = PLSR(factor_name='run_order')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":null,"dir":"Reference","previous_headings":"","what":"Support Vector Machine Classifier — SVM","title":"Support Vector Machine Classifier — SVM","text":"Support Vector Machines (SVM) machine learning algorithm classification. can make use kernel functions generate highly non-linear boundaries groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Support Vector Machine Classifier — SVM","text":"","code":"SVM(   factor_name,   kernel = \"linear\",   degree = 3,   gamma = 1,   coef0 = 0,   cost = 1,   class_weights = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Support Vector Machine Classifier — SVM","text":"factor_name (character) name sample-meta column use. kernel (character) Kernel type. Allowed values limited following: \"polynomial\": . \"radial\": . \"sigmoid\": . default \"linear\". degree (numeric) polynomial degree. default 3. gamma (numeric) gamma parameter. default 1. coef0 (numeric) offset coefficient. default 0. cost (numeric) cost violating constraints. default 1. class_weights (numeric, character, NULL) named vector weights different classes.  Specifying \"inverse\" choose weights inversely proportional class distribution. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Support Vector Machine Classifier — SVM","text":" SVM object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Support Vector Machine Classifier — SVM","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Support Vector Machine Classifier — SVM","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-13, https://CRAN.R-project.org/package=e1071. Brereton RG, Lloyd GR (2010). \"Support Vector Machines classification regression.\" Analyst, 135(2), 230-267.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Support Vector Machine Classifier — SVM","text":"","code":"M = SVM(factor_name='Species',gamma=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to data.frame — as_data_frame","title":"Convert to data.frame — as_data_frame","text":"Convert outputs input model data.frame.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to data.frame — as_data_frame","text":"","code":"# S4 method for filter_na_count as_data_frame(M)  # S4 method for ttest as_data_frame(M)  # S4 method for wilcox_test as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to data.frame — as_data_frame","text":"M model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to data.frame — as_data_frame","text":"data.frame model outputs","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to data.frame — as_data_frame","text":"","code":"D = iris_DatasetExperiment() M = filter_na_count(threshold=50,factor_name='Species') M= model_apply(M,D) df = as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Autoscaling — autoscale","title":"Autoscaling — autoscale","text":"variable/feature mean centred scaled standard deviation. transformed variables zero-mean unit-variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autoscaling — autoscale","text":"","code":"autoscale(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autoscaling — autoscale","text":"mode (character) Mode action. Allowed values limited following: \"sample_meta\": Autoscaling applied sample_meta data . \"\": Autoscaling applied data matrix meta data. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autoscaling — autoscale","text":" autoscale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autoscaling — autoscale","text":"","code":"D = iris_DatasetExperiment() M = autoscale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Balanced Accuracy — balanced_accuracy","title":"Balanced Accuracy — balanced_accuracy","text":"Balanced Accuracy average proportion correctly classified samples across groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"balanced_accuracy(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Balanced Accuracy — balanced_accuracy","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Balanced Accuracy — balanced_accuracy","text":"  balanced_accuracy  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = balanced_accuracy() XCV = run(XCV,D,MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Blank filter — blank_filter","title":"Blank filter — blank_filter","text":"blank filter filters features comparing median intensity blank samples median intensity samples. Features relative intensity (fold change) large compared blank removed. number times feature detected across blank samples may also considered. feature detected high enough proportion blanks removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blank filter — blank_filter","text":"","code":"blank_filter(   fold_change = 20,   blank_label = \"blank\",   qc_label = \"QC\",   factor_name,   fraction_in_blank = 0,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blank filter — blank_filter","text":"fold_change (numeric) Features fold change less value removed. default 20. blank_label (character) label used identify blank samples. default \"blank\". qc_label (character, NULL) label used identify QC samples. set NULL median samples used. default \"QC\". factor_name (character) name sample-meta column use. fraction_in_blank (numeric) Features present less proportion blanks considered removal. default 0. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Blank filter — blank_filter","text":" blank_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Blank filter — blank_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Blank filter — blank_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Blank filter — blank_filter","text":"","code":"D = iris_DatasetExperiment() M = blank_filter(fold_change=2,                  factor_name='Species',                  blank_label='setosa',                  qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of blank filter fold changes — blank_filter_hist","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"histogram calculated fold changes blank filter (median samples divided median blanks)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"blank_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"  blank_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"C = blank_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap resampling — bootstrap","title":"Bootstrap resampling — bootstrap","text":"bootstrap resampling subset samples selected random replacement form training set. sample selected training included test set. process repeated many times, performance metrics computed repetition.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap resampling — bootstrap","text":"","code":"bootstrap(number_of_repetitions = 100, collect, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap resampling — bootstrap","text":"number_of_repetitions (numeric, integer) number bootstrap repetitions. default 100. collect (character) name model output collect bootstrap repetitions, addition input metric. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap resampling — bootstrap","text":" bootstrap object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap resampling — bootstrap","text":"","code":"I = bootstrap(number_of_repetitions = 10, collect = 'vip')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate metric — calculate,AUC-method","title":"Calculate metric — calculate,AUC-method","text":"Calculate metric","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate metric — calculate,AUC-method","text":"","code":"# S4 method for AUC calculate(obj, Y, Yhat)  # S4 method for balanced_accuracy calculate(obj, Y, Yhat)  # S4 method for r_squared calculate(obj, Y, Yhat)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate metric — calculate,AUC-method","text":"obj metric object Y true values/group labels Yhat predicted values/group labels","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate metric — calculate,AUC-method","text":"modified metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate metric — calculate,AUC-method","text":"","code":"MET = metric() calculate(MET) #> Warning: no calculation provided for this metric #> A \"metric\" object #> ----------------- #> name:           #> description:    #> value:          #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"Plots chart object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"# S4 method for dfa_scores_plot,DFA chart_plot(obj, dobj)  # S4 method for scatter_chart,DatasetExperiment chart_plot(obj, dobj)  # S4 method for pca_correlation_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_scores_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_biplot,PCA chart_plot(obj, dobj)  # S4 method for pca_loadings_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_scree_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_dstat_plot,PCA chart_plot(obj, dobj)  # S4 method for plsr_prediction_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsr_residual_hist,PLSR chart_plot(obj, dobj)  # S4 method for plsr_qq_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsr_cook_dist,PLSR chart_plot(obj, dobj)  # S4 method for pls_scores_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsda_predicted_plot,PLSDA chart_plot(obj, dobj)  # S4 method for plsda_roc_plot,PLSDA chart_plot(obj, dobj)  # S4 method for pls_vip_plot,PLSR chart_plot(obj, dobj)  # S4 method for pls_regcoeff_plot,PLSR chart_plot(obj, dobj)  # S4 method for blank_filter_hist,blank_filter chart_plot(obj, dobj)  # S4 method for confounders_lsq_barchart,confounders_clsq chart_plot(obj, dobj)  # S4 method for confounders_lsq_boxplot,confounders_clsq chart_plot(obj, dobj)  # S4 method for feature_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for mv_histogram,DatasetExperiment chart_plot(obj, dobj)  # S4 method for mv_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_dist,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for compare_dist,DatasetExperiment chart_plot(obj, dobj, eobj)  # S4 method for DatasetExperiment_heatmap,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_factor_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for feature_profile_array,DatasetExperiment chart_plot(obj, dobj)  # S4 method for feature_profile,DatasetExperiment chart_plot(obj, dobj)  # S4 method for fold_change_plot,fold_change chart_plot(obj, dobj)  # S4 method for fs_line,forward_selection_by_rank chart_plot(obj, dobj)  # S4 method for glog_opt_plot,glog_transform chart_plot(obj, dobj, gobj)  # S4 method for gs_line,grid_search_1d chart_plot(obj, dobj)  # S4 method for hca_dendrogram,HCA chart_plot(obj, dobj)  # S4 method for kfoldxcv_grid,kfold_xval chart_plot(obj, dobj)  # S4 method for kfoldxcv_metric,kfold_xval chart_plot(obj, dobj)  # S4 method for kw_p_hist,kw_rank_sum chart_plot(obj, dobj)  # S4 method for mv_feature_filter_hist,mv_feature_filter chart_plot(obj, dobj)  # S4 method for mv_sample_filter_hist,mv_sample_filter chart_plot(obj, dobj)  # S4 method for permutation_test_plot,permutation_test chart_plot(obj, dobj)  # S4 method for plsda_feature_importance_plot,PLSDA chart_plot(obj, dobj)  # S4 method for pqn_norm_hist,pqn_norm chart_plot(obj, dobj)  # S4 method for resample_chart,resample chart_plot(obj, dobj)  # S4 method for rsd_filter_hist,rsd_filter chart_plot(obj, dobj)  # S4 method for feature_profile,sb_corr chart_plot(obj, dobj, gobj)  # S4 method for svm_plot_2d,SVM chart_plot(obj, dobj, gobj)  # S4 method for tSNE_scatter,tSNE chart_plot(obj, dobj)  # S4 method for tic_chart,DatasetExperiment chart_plot(obj, dobj)  # S4 method for wilcox_p_hist,wilcox_test chart_plot(obj, dobj)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"obj chart object dobj struct object eobj second DatasetExperiment object compare first gobj DatasetExperiment object signal correction applied.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"plot object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"C = example_chart() chart_plot(C,iris_DatasetExperiment()) #> Warning: no chart defined for \"DatasetExperiment\" #> A \"example_chart\" object #> ------------------------ #> name:           #> description:"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Univariate Classical Least Squares Regression — classical_lsq","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"univariate classical least squares regression line fitted feature/variable response variable. fitted line minimises sum squared differences true response predicted response. coefficients (offset, gradient) fit can tested significance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"classical_lsq(alpha = 0.05, mtc = \"fdr\", factor_names, intercept = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character, list) column names regress . character vector list used ofr features. list character vectors provided assumed different set columns feature. intercept (logical) Model intercept. Allowed values limited following: \"FALSE\": intercept term included model. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Univariate Classical Least Squares Regression — classical_lsq","text":" classical_lsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"D = iris_DatasetExperiment() M = classical_lsq(factor_names = 'Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare distributions — compare_dist","title":"Compare distributions — compare_dist","text":"Histograms boxplots computed across samples features used visually compare two datasets e.g. filtering /normalisation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare distributions — compare_dist","text":"","code":"compare_dist(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare distributions — compare_dist","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare distributions — compare_dist","text":"  compare_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare distributions — compare_dist","text":"","code":"D1=MTBLS79_DatasetExperiment(filtered=FALSE) D2=MTBLS79_DatasetExperiment(filtered=TRUE) C = compare_dist(factor_name='Class') chart_plot(C,D1,D2) #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 8012 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 8012 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 10623 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 6 rows containing missing values (`geom_path()`). #> Warning: Removed 10623 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 6 rows containing missing values (`geom_path()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`).  #> TableGrob (4 x 2) \"arrange\": 4 grobs #>   z     cells    name           grob #> 1 1 (1-1,1-1) arrange gtable[layout] #> 2 2 (1-1,2-2) arrange gtable[layout] #> 3 3 (2-4,1-1) arrange gtable[layout] #> 4 4 (2-4,2-2) arrange gtable[layout]"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for confounding factors — confounders_clsq","title":"Check for confounding factors — confounders_clsq","text":"Univariate least squares regression models used compare models without potential confounding factors included. change coefficients (delta) computed potential confounding factor. Factors large delta said large impact model therefore confounding. p-values computed models confounders included reduce potential false positives. suitable main factors 2 levels.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for confounding factors — confounders_clsq","text":"","code":"confounders_clsq(   alpha = 0.05,   mtc = \"fdr\",   factor_name,   confounding_factors,   threshold = 0.15,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for confounding factors — confounders_clsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name main factor factors may confounding. confounding_factors (character) name(s) factor(s) potential confounding factors. threshold (numeric) Factors delta greater threshold considered confounding. default 0.15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for confounding factors — confounders_clsq","text":" confounders_clsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for confounding factors — confounders_clsq","text":"","code":"D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change barchart — confounders_lsq_barchart","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"barchart relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"confounders_lsq_barchart(feature_to_plot, threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"feature_to_plot (numeric, character, integer) column name feature plotted. threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"  confounders_lsq_barchart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_barchart(feature_to_plot=1,threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change boxplot — confounders_lsq_boxplot","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"boxplot relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"confounders_lsq_boxplot(threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"  confounders_lsq_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_boxplot(threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalisation to constant sum — constant_sum_norm","title":"Normalisation to constant sum — constant_sum_norm","text":"sample normalised total signal equal one (scaling factor specified).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"constant_sum_norm(scaling_factor = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalisation to constant sum — constant_sum_norm","text":"scaling_factor (numeric) scaling factor applied normalisation. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalisation to constant sum — constant_sum_norm","text":" constant_sum_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"M = constant_sum_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlation coefficient — corr_coef","title":"Correlation coefficient — corr_coef","text":"correlation features set continuous factor calculated. Multiple-test corrected p-values used indicate whether computed coefficients may occurred chance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlation coefficient — corr_coef","text":"","code":"corr_coef(alpha = 0.05, mtc = \"fdr\", factor_names, method = \"spearman\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlation coefficient — corr_coef","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. method (character) Type correlation. Allowed values limited following: \"pearson\": Pearson product moment correlation computed. \"spearman\": Spearman's rho statistic computed. default \"spearman\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlation coefficient — corr_coef","text":" corr_coef object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correlation coefficient — corr_coef","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correlation coefficient — corr_coef","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correlation coefficient — corr_coef","text":"","code":"D = MTBLS79_DatasetExperiment(filtered=TRUE)  # subset for this example D = D[,1:10]  # convert to numeric for this example D$sample_meta$sample_order=as.numeric(D$sample_meta$run_order) D$sample_meta$sample_rep=as.numeric(D$sample_meta$Sample_Rep)  M = corr_coef(factor_names=c('sample_order','sample_rep')) M = model_apply(M,D) #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"DFA scores plot — dfa_scores_plot","title":"DFA scores plot — dfa_scores_plot","text":"scatter plot selected DFA components.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DFA scores plot — dfa_scores_plot","text":"","code":"dfa_scores_plot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DFA scores plot — dfa_scores_plot","text":"components (numeric) components selected plotting. default c(1, 2). points_to_label (character) Points label. Allowed values limited following: \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"group\": Hotelling T2 ellipses (p=0.95) plotted groups. \"none\": Ellipses included plot. \"sample\": Hotelling T2 ellipse (p=0.95) plotted samples (ignoring group). default \"\". label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DFA scores plot — dfa_scores_plot","text":"  dfa_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DFA scores plot — dfa_scores_plot","text":"object makes use functionality following packages: ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DFA scores plot — dfa_scores_plot","text":"Wickham H, Seidel D (2022). scales: Scale Functions Visualization. R package version 1.2.1, https://CRAN.R-project.org/package=scales. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DFA scores plot — dfa_scores_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre() + DFA(factor_name='Species') M = model_apply(M,D) C = dfa_scores_plot(factor_name = 'Species') chart_plot(C,M[2]) #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor?"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Dispersion ratio filter — dratio_filter","title":"Dispersion ratio filter — dratio_filter","text":"dispersion ratio (d-ratio) compares standard deviation (non-parametric equivalent) Quality Control (QC) samples relative standard deviation (non-parametric equivalent) samples feature. d-ratio greater predefined threshold observed sample variance due technical variance feature removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dispersion ratio filter — dratio_filter","text":"","code":"dratio_filter(   threshold = 20,   qc_label = \"QC\",   factor_name,   method = \"ratio\",   dispersion = \"sd\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dispersion ratio filter — dratio_filter","text":"threshold (numeric) threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. method (character) dratio method. Allowed values limited following: \"euclidean\": Dispersion QCs divided euclidean length total dispersion. Total dispersion estimated QC Sample dispersion assuming orthogonal. Corresponds Eq 5 Broadhurst et al (2018). default \"ratio\". dispersion (character) Dispersion method. Allowed values limited following: \"mad\": Dispersion estimated using median absolute deviation. default \"sd\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dispersion ratio filter — dratio_filter","text":" dratio_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dispersion ratio filter — dratio_filter","text":"Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB (2018). \"Guidelines considerations use system suitability quality control samples mass spectrometry assays applied untargeted clinical metabolomic studies.\" Metabolomics, 14(6).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dispersion ratio filter — dratio_filter","text":"","code":"D = MTBLS79_DatasetExperiment() M = dratio_filter(threshold=20,qc_label='QC',factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Equal group sized sampling — equal_split","title":"Equal group sized sampling — equal_split","text":"Samples randomly chosen level training set equal numbers samples levels. number samples based input proportion smallest group size.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Equal group sized sampling — equal_split","text":"","code":"equal_split(p_train = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Equal group sized sampling — equal_split","text":"p_train (numeric) proportion samples selected training set. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Equal group sized sampling — equal_split","text":" equal_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Equal group sized sampling — equal_split","text":"","code":"D = iris_DatasetExperiment() M = equal_split(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — feature_boxplot","title":"Feature boxplot — feature_boxplot","text":"boxplot visualise distribution values within feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — feature_boxplot","text":"","code":"feature_boxplot(   label_outliers = TRUE,   feature_to_plot,   factor_name,   show_counts = TRUE,   style = \"boxplot\",   jitter = FALSE,   fill = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — feature_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"FALSE\": labels displayed. default TRUE. feature_to_plot (character, numeric, integer) column name plotted feature. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"FALSE\": number samples box displayed. default TRUE. style (character) Plot style. Allowed values limited following: \"violin\": Violon plot style. default \"boxplot\". jitter (logical) Include points plotted added jitter. default FALSE. fill (logical) Block fill boxes violins group colour. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — feature_boxplot","text":"  feature_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — feature_boxplot","text":"","code":"D = MTBLS79_DatasetExperiment C = feature_boxplot(factor_name='Species',feature_to_plot='Petal.Width') chart_plot(C,D) #> Warning: no chart defined for \"function\" #> A \"feature_boxplot\" object #> -------------------------- #> name:          Feature boxplot #> description:   A boxplot to visualise the distribution of values within a feature. #> input params:  label_outliers, feature_to_plot, factor_name, show_counts, style, jitter, fill"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile","title":"Feature profile — feature_profile","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile","text":"","code":"feature_profile(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   plot_sd = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. plot_sd (logical) Plot standard deviation. Allowed values limited following: \"FALSE\": Standard deviation plotted. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile","text":"  feature_profile  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile","text":"","code":"D = MTBLS79_DatasetExperiment() C = feature_profile(run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1) chart_plot(C,D) #> Warning: Removed 18 rows containing missing values (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile_array","title":"Feature profile — feature_profile_array","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile_array","text":"","code":"feature_profile_array(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   nrow = 5,   log = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile_array","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. nrow (numeric, integer) number rows plot. default 5. log (logical) Log transform. Allowed values limited following: \"FALSE\": data transformed plotting. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile_array","text":"  feature_profile_array  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile_array","text":"","code":"D = MTBLS79_DatasetExperiment() C = feature_profile_array(     run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1:3,     nrow=1,     log=TRUE) chart_plot(C,D) #> Warning: Removed 23 rows containing missing values (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by name — filter_by_name","title":"Filter by name — filter_by_name","text":"Filter samples/variables row/column name, index logicals.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by name — filter_by_name","text":"","code":"filter_by_name(mode = \"exclude\", dimension = \"sample\", names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by name — filter_by_name","text":"mode (character) filtering mode controls whether samples/features mode=\"included\" mode=\"excluded\" based name. default \"exclude\". dimension (character) filtering dimensions controls whether dimension=\"sample\" dimension=\"variable\" filtered based name. default \"sample\". names (character, numeric, logical) name features/samples filtered. Must exact match. Can also provide indexes (numeric) logical. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by name — filter_by_name","text":" filter_by_name object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by name — filter_by_name","text":"","code":"D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='exclude',dimension='variable',names=c(1,2,3)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum number of measured values filter — filter_na_count","title":"Minimum number of measured values filter — filter_na_count","text":"number measured values counted feature, feature less predefined minimum number values group removed. several factors, threshold applied minimum number samples present combinations (interactions) groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"filter_na_count(threshold, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum number of measured values filter — filter_na_count","text":"threshold (numeric) minimum number samples group/interaction. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Minimum number of measured values filter — filter_na_count","text":" filter_na_count object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"D = MTBLS79_DatasetExperiment() M = filter_na_count(threshold=3,factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by sample meta data — filter_smeta","title":"Filter by sample meta data — filter_smeta","text":"data filtered named levels factor included/excluded dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by sample meta data — filter_smeta","text":"","code":"filter_smeta(mode = \"include\", levels, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by sample meta data — filter_smeta","text":"mode (character) Mode action. Allowed values limited following: \"exclude\": Samples specified levels excluded. default \"include\". levels (character) level name(s) filtering. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by sample meta data — filter_smeta","text":" filter_smeta object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by sample meta data — filter_smeta","text":"","code":"D = MTBLS79_DatasetExperiment() M = filter_smeta(mode='exclude',levels='QC',factor_name='QC') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher Exact Test — fisher_exact","title":"Fisher Exact Test — fisher_exact","text":"fisher exact test used analyse contingency tables comparing number correctly/incorrectly predicted group labels. multiple test corrected p-value indicates whether number measured values significantly different groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher Exact Test — fisher_exact","text":"","code":"fisher_exact(alpha = 0.05, mtc = \"fdr\", factor_name, factor_pred, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher Exact Test — fisher_exact","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. factor_pred (data.frame) data.frame, column factor predicted group labels compare true groups labels. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher Exact Test — fisher_exact","text":" fisher_exact object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher Exact Test — fisher_exact","text":"","code":"# load some data D=MTBLS79_DatasetExperiment()  # prepare predictions based on NA pred=as.data.frame(is.na(D$data)) pred=lapply(pred,factor,levels=c(TRUE,FALSE)) pred=as.data.frame(pred)  # apply method M = fisher_exact(alpha=0.05,mtc='fdr',factor_name='Class',factor_pred=pred) M=model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change — fold_change","title":"Fold change — fold_change","text":"Fold change relative change mean (non-parametric equivalent) intensities feature pairs levels factor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change — fold_change","text":"","code":"fold_change(   factor_name,   paired = FALSE,   sample_name = character(0),   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change — fold_change","text":"factor_name (character) name sample-meta column use. paired (logical) Paired fold change. Allowed values limited following: \"FALSE\": Fold change calculated assuming paired sampling. default FALSE. sample_name (character) name sample_meta column containing sample identifiers paired sampling. default character(0). threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level name group used denominator (possible) computing fold change. default character(0). method (character) Fold change method. Allowed values limited following: \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change — fold_change","text":" fold_change object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change — fold_change","text":"Price Jr RM, Bonett DG (2020). \"Confidence Intervals Ratios Means Medians.\" Journal Educational Behavioral Statistics, 45(6), 750-770.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change — fold_change","text":"","code":"D = MTBLS79_DatasetExperiment() M = fold_change(factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change for interactions between factors — fold_change_int","title":"Fold change for interactions between factors — fold_change_int","text":"one factor fold change calculation extended include combinations levels (interactions) factors. Paired fold changes possible computation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"fold_change_int(   factor_name,   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change for interactions between factors — fold_change_int","text":"factor_name (character) name sample-meta column use. threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level names groups used denominator (possible) computing fold change. One level factor, assumed order factor_name. default character(0). method (character) Fold change method. Allowed values limited following: \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change for interactions between factors — fold_change_int","text":" fold_change_int object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change for interactions between factors — fold_change_int","text":"Lloyd GR, Jankevics , Weber RJM (2020). \"struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond.\" Bioinformatics, 36(22-23), 5551-5552. https://doi.org/10.1093/bioinformatics/btaa1031.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"D = MTBLS79_DatasetExperiment() D=D[,1:10,drop=FALSE] M = filter_smeta(mode='exclude',levels='QC',factor_name='Class') +     fold_change_int(factor_name=c('Class','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change plot — fold_change_plot","title":"Fold change plot — fold_change_plot","text":"plot fold changes calculated chosen subset features. predefined fold change threshold indicated shaded regions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change plot — fold_change_plot","text":"","code":"fold_change_plot(number_features = 20, orientation = \"portrait\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change plot — fold_change_plot","text":"number_features (numeric) number randomly selected features plot, list column numbers. default 20. orientation (character) Plot orientation. Allowed values limited following: \"portrait\": Features plotted x-axis. default \"portrait\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change plot — fold_change_plot","text":"  fold_change_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change plot — fold_change_plot","text":"","code":"C = fold_change_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection by rank — forward_selection_by_rank","title":"Forward selection by rank — forward_selection_by_rank","text":"model trained performance metric computed including increasing numbers features model. features included step defined rank, computed another variable e.g. VIP score. \"optimal\"subset features suggested minimising input performance metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"forward_selection_by_rank(   min_no_vars = 1,   max_no_vars = 100,   step_size = 1,   factor_name,   variable_rank,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection by rank — forward_selection_by_rank","text":"min_no_vars (numeric) minimum number variables include model. default 1. max_no_vars (numeric) maximum number variables include model. default 100. step_size (numeric) incremental change number features model. default 1. factor_name (character) name sample-meta column use. variable_rank (numeric, integer) values used rank features. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection by rank — forward_selection_by_rank","text":" forward_selection_by_rank object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"# some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection line plot — fs_line","title":"Forward selection line plot — fs_line","text":"line plot forward selection. computed model performance metric plotted number features included model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection line plot — fs_line","text":"","code":"fs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection line plot — fs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection line plot — fs_line","text":"  fs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection line plot — fs_line","text":"","code":"# some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())  # chart C = fs_line() chart_plot(C,M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Glog optimisation — glog_opt_plot","title":"Glog optimisation — glog_opt_plot","text":"plot sum squares error (SSE) vs different values lambda glog transform. indicated optimum value lambda minimises SSE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glog optimisation — glog_opt_plot","text":"","code":"glog_opt_plot(plot_grid = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glog optimisation — glog_opt_plot","text":"plot_grid (numeric)  default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glog optimisation — glog_opt_plot","text":"  glog_opt_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glog optimisation — glog_opt_plot","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Glog optimisation — glog_opt_plot","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glog optimisation — glog_opt_plot","text":"","code":"D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard C = glog_opt_plot() chart_plot(C,M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Generalised logarithmic transform — glog_transform","title":"Generalised logarithmic transform — glog_transform","text":"generalised logarithm (glog) transformation applies log transformation applying offset account technical variation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generalised logarithmic transform — glog_transform","text":"","code":"glog_transform(qc_label = \"QC\", factor_name, lambda = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generalised logarithmic transform — glog_transform","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. lambda (numeric, NULL) value lambda use. NULL pmp package used determine \"optimal\" value lambda. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generalised logarithmic transform — glog_transform","text":" glog_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generalised logarithmic transform — glog_transform","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generalised logarithmic transform — glog_transform","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp. Durbin B, Hardin J, Hawkins D, Rocke D (2002). \"variance-stabilizing transformation gene-expression microarray data.\" Bioinformatics, 18(Suppl 1), S105-S110. Parsons HM, Ludwig C, Gunther UL, Viant MR (2007). \"Improved classification accuracy 1- ', '2-dimensional NMR metabolomics data using variance ', 'stabilising generalised logarithm transformation.\" Bioinformatics, 8(1), 234.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generalised logarithmic transform — glog_transform","text":"","code":"D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"One dimensional grid search — grid_search_1d","title":"One dimensional grid search — grid_search_1d","text":"one dimensional grid search calculates performance metric model evenly spaced values model input parameter. \"optimum\" value parameter suggested one maximises performance, minimises error (whichever appropriate chosen metric)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One dimensional grid search — grid_search_1d","text":"","code":"grid_search_1d(   param_to_optimise,   search_values,   model_index,   factor_name,   max_min = \"min\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One dimensional grid search — grid_search_1d","text":"param_to_optimise (character) name model input parameter focus search. search_values () values input parameter optimised. model_index (numeric, integer) index model sequence uses parameter optimised. factor_name (character) name sample-meta column use. max_min (character) Maximise minimise. Allowed values limited following: \"min\": optimium parameter value suggested based minimising performance metric. default \"min\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One dimensional grid search — grid_search_1d","text":" grid_search_1d object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One dimensional grid search — grid_search_1d","text":"","code":"D = MTBLS79_DatasetExperiment() # some preprocessing M = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute() +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(factor_name='Class',levels='QC',mode='exclude') M=model_apply(M,D) D=predicted(M)  # reduce number of features for this example D=D[,1:10]  # optmise number of components for PLS model I = grid_search_1d(param_to_optimise='number_components',search_values=1:5,         model_index=2,factor_name='Class') *         (mean_centre()+PLSDA(factor_name='Class')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Grid search line plot — gs_line","title":"Grid search line plot — gs_line","text":"plot calculated performance metric model input parameter values used train model. optimum parameter value indicated based minimising (maximising) chosen metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grid search line plot — gs_line","text":"","code":"gs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grid search line plot — gs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grid search line plot — gs_line","text":"  gs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Grid search line plot — gs_line","text":"","code":"C = gs_line()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"HCA dendrogram — hca_dendrogram","title":"HCA dendrogram — hca_dendrogram","text":"dendrogram visualising clustering HCA.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HCA dendrogram — hca_dendrogram","text":"","code":"hca_dendrogram(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HCA dendrogram — hca_dendrogram","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HCA dendrogram — hca_dendrogram","text":"  hca_dendrogram  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"HCA dendrogram — hca_dendrogram","text":"object makes use functionality following packages: ggdendro","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"HCA dendrogram — hca_dendrogram","text":"de Vries , Ripley BD (2022). ggdendro: Create Dendrograms Tree Diagrams Using 'ggplot2'. R package version 0.1.23, https://CRAN.R-project.org/package=ggdendro.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HCA dendrogram — hca_dendrogram","text":"","code":"C = hca_dendrogram()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross-validation — kfold_xval","title":"k-fold cross-validation — kfold_xval","text":"k-fold cross-validation iterative approach applied validate models. samples divided k \"folds\", subsets. subset excluded model training used model validation , resulting single left-prediction sample. Model performance metrics computed training test sets across folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross-validation — kfold_xval","text":"","code":"kfold_xval(folds = 10, method = \"venetian\", factor_name, collect = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross-validation — kfold_xval","text":"folds (numeric, integer) number cross-validation folds. default 10. method (character) Fold selection method. Allowed values limited following: \"blocks\": Blocks adjacent samples assigned fold. \"random\": Samples randomly assigned fold. default \"venetian\". factor_name (character) name sample-meta column use. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross-validation — kfold_xval","text":" kfold_xval object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross-validation — kfold_xval","text":"","code":"D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross validation plot — kfoldxcv_grid","title":"k-fold cross validation plot — kfoldxcv_grid","text":"graphic visualising true class predicted class samples groups cross-validation folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"kfoldxcv_grid(factor_name, level, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross validation plot — kfoldxcv_grid","text":"factor_name (character) name sample-meta column use. level (character) level/group plot. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross validation plot — kfoldxcv_grid","text":"  kfoldxcv_grid  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())  C = kfoldxcv_grid(factor_name='Species',level='setosa') chart_plot(C,I) #> Warning: Removed 900 rows containing missing values (`geom_tile()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"kfoldxcv metric plot — kfoldxcv_metric","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"boxplot performance metric computed fold k-fold cross-validation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"kfoldxcv_metric(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"  kfoldxcv_metric  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"C = kfoldxcv_metric()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":null,"dir":"Reference","previous_headings":"","what":"kNN missing value imputation — knn_impute","title":"kNN missing value imputation — knn_impute","text":"k-nearest neighbour missing value imputation replaces missing values data average predefined number similar neighbours value present","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kNN missing value imputation — knn_impute","text":"","code":"knn_impute(   neighbours = 5,   sample_max = 50,   feature_max = 50,   by = \"features\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kNN missing value imputation — knn_impute","text":"neighbours (numeric) number neighbours (k) use imputation. default 5. sample_max (numeric) maximum percent missing values per sample. default 50. feature_max (numeric) maximum percent missing values per feature. default 50. (character) Impute using similar \"samples\" \"features\". Default features. default \"features\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kNN missing value imputation — knn_impute","text":" knn_impute object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"kNN missing value imputation — knn_impute","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"kNN missing value imputation — knn_impute","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kNN missing value imputation — knn_impute","text":"","code":"M = knn_impute()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — kw_p_hist","title":"Histogram of p values — kw_p_hist","text":"histogram p-values computed kruskal-wallis method","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — kw_p_hist","text":"","code":"kw_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — kw_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — kw_p_hist","text":"  kw_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — kw_p_hist","text":"","code":"C = kw_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Kruskal-Wallis rank sum test — kw_rank_sum","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"Kruskal-Wallis test univariate hypothesis testing method allows multiple (n>=2) groups compared without making assumption values normally distributed. non-parametric equivalent 1-way ANOVA. test applied variables/features individually, multiple test corrected p-values computed indicate significance variables/features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"kw_rank_sum(alpha = 0.05, mtc = \"fdr\", factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":" kw_rank_sum object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"D = iris_DatasetExperiment() M = kw_rank_sum(factor_names='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear model — linear_model","title":"Linear model — linear_model","text":"Linear models can used carry regression, single stratum analysis variance analysis covariance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear model — linear_model","text":"","code":"linear_model(formula, na_action = \"na.omit\", contrasts = list(), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear model — linear_model","text":"formula (formula) symbolic description model fitted. na_action (character) NA action. Allowed values limited following: \"na.fail\": error thrown NA present. \"na.exclude\": Incomplete cases removed, output result padded correct size using NA. \"na.pass\": apply linear model NA present. default \"na.omit\". contrasts (list) contrasts associated factor. default list. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear model — linear_model","text":" linear_model object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Linear model — linear_model","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Linear model — linear_model","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear model — linear_model","text":"","code":"D = iris_DatasetExperiment() M = linear_model(formula = y~Species)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"logarithm transform — log_transform","title":"logarithm transform — log_transform","text":"logarithmic transform applied values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"logarithm transform — log_transform","text":"","code":"log_transform(base = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"logarithm transform — log_transform","text":"base (numeric) base logarithm used transform. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"logarithm transform — log_transform","text":" log_transform object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"logarithm transform — log_transform","text":"","code":"M = log_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean centre — mean_centre","title":"Mean centre — mean_centre","text":"mean sample subtracted samples data matrix. features centred matrix zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean centre — mean_centre","text":"","code":"mean_centre(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean centre — mean_centre","text":"mode (character) Mode action. Allowed values limited following: \"sample_meta\": Centring applied sample_meta block. \"\": Centring applied data sample_meta blocks. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean centre — mean_centre","text":" mean_centre object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean centre — mean_centre","text":"","code":"M = mean_centre()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean of medians — mean_of_medians","title":"Mean of medians — mean_of_medians","text":"data matrix normalised mean median factor level.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean of medians — mean_of_medians","text":"","code":"mean_of_medians(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean of medians — mean_of_medians","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean of medians — mean_of_medians","text":" mean_of_medians object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean of medians — mean_of_medians","text":"","code":"D = iris_DatasetExperiment() M = mean_of_medians(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixed effects model — mixed_effect","title":"Mixed effects model — mixed_effect","text":"mixed effects model extension ANOVA fixed random effects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixed effects model — mixed_effect","text":"","code":"mixed_effect(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"marginal\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixed effects model — mixed_effect","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) Sum squares type. Allowed values limited following: \"sequential\": Type II sum squares. default \"marginal\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mixed effects model — mixed_effect","text":" mixed_effect object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mixed effects model — mixed_effect","text":"object makes use functionality following packages: emmeans","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mixed effects model — mixed_effect","text":"Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-162, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882. Lenth R (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.8.7, https://CRAN.R-project.org/package=emmeans. Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixed effects model — mixed_effect","text":"","code":"D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = mixed_effect(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply method — model_apply,ANOVA,DatasetExperiment-method","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Applies method input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"# S4 method for ANOVA,DatasetExperiment model_apply(M, D)  # S4 method for HSD,DatasetExperiment model_apply(M, D)  # S4 method for mixed_effect,DatasetExperiment model_apply(M, D)  # S4 method for HSDEM,DatasetExperiment model_apply(M, D)  # S4 method for classical_lsq,DatasetExperiment model_apply(M, D)  # S4 method for confounders_clsq,DatasetExperiment model_apply(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_apply(M, D)  # S4 method for corr_coef,DatasetExperiment model_apply(M, D)  # S4 method for split_data,DatasetExperiment model_apply(M, D)  # S4 method for equal_split,DatasetExperiment model_apply(M, D)  # S4 method for filter_smeta,DatasetExperiment model_apply(M, D)  # S4 method for fisher_exact,DatasetExperiment model_apply(M, D)  # S4 method for fold_change,DatasetExperiment model_apply(M, D)  # S4 method for fold_change_int,DatasetExperiment model_apply(M, D)  # S4 method for HCA,DatasetExperiment model_apply(M, D)  # S4 method for knn_impute,DatasetExperiment model_apply(M, D)  # S4 method for kw_rank_sum,DatasetExperiment model_apply(M, D)  # S4 method for log_transform,DatasetExperiment model_apply(M, D)  # S4 method for mean_of_medians,DatasetExperiment model_apply(M, D)  # S4 method for nroot_transform,DatasetExperiment model_apply(M, D)  # S4 method for pairs_filter,DatasetExperiment model_apply(M, D)  # S4 method for prop_na,DatasetExperiment model_apply(M, D)  # S4 method for rsd_filter,DatasetExperiment model_apply(M, D)  # S4 method for sb_corr,DatasetExperiment model_apply(M, D)  # S4 method for stratified_split,DatasetExperiment model_apply(M, D)  # S4 method for tSNE,DatasetExperiment model_apply(M, D)  # S4 method for ttest,DatasetExperiment model_apply(M, D)  # S4 method for vec_norm,DatasetExperiment model_apply(M, D)  # S4 method for wilcox_test,DatasetExperiment model_apply(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"M method object D another object used first","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Returns modified method object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"M=model() model_apply(M,DatasetExperiment()) #> Warning: no training implemented for this model #> A \"model\" object #> ---------------- #> name:           #> description:    #> predicted:      #> seq_in:        data #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Model prediction — model_predict,DFA,DatasetExperiment-method","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Apply model using input DatasetExperiment. Assumes model trained first.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"# S4 method for DFA,DatasetExperiment model_predict(M, D)  # S4 method for PCA,DatasetExperiment model_predict(M, D)  # S4 method for PLSR,DatasetExperiment model_predict(M, D)  # S4 method for PLSDA,DatasetExperiment model_predict(M, D)  # S4 method for autoscale,DatasetExperiment model_predict(M, D)  # S4 method for blank_filter,DatasetExperiment model_predict(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_predict(M, D)  # S4 method for dratio_filter,DatasetExperiment model_predict(M, D)  # S4 method for filter_by_name,DatasetExperiment model_predict(M, D)  # S4 method for filter_na_count,DatasetExperiment model_predict(M, D)  # S4 method for filter_smeta,DatasetExperiment model_predict(M, D)  # S4 method for glog_transform,DatasetExperiment model_predict(M, D)  # S4 method for linear_model,DatasetExperiment model_predict(M, D)  # S4 method for mean_centre,DatasetExperiment model_predict(M, D)  # S4 method for mv_feature_filter,DatasetExperiment model_predict(M, D)  # S4 method for mv_sample_filter,DatasetExperiment model_predict(M, D)  # S4 method for OPLSR,DatasetExperiment model_predict(M, D)  # S4 method for OPLSDA,DatasetExperiment model_predict(M, D)  # S4 method for pareto_scale,DatasetExperiment model_predict(M, D)  # S4 method for pqn_norm,DatasetExperiment model_predict(M, D)  # S4 method for SVM,DatasetExperiment model_predict(M, D)  # S4 method for vec_norm,DatasetExperiment model_predict(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_predict(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Reverse effect preprocessing step DatasetExperiment.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"# S4 method for autoscale,DatasetExperiment model_reverse(M, D)  # S4 method for mean_centre,DatasetExperiment model_reverse(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Returns modified DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"M = example_model() D = model_reverse(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model — model_train,DFA,DatasetExperiment-method","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Trains model using input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"# S4 method for DFA,DatasetExperiment model_train(M, D)  # S4 method for PCA,DatasetExperiment model_train(M, D)  # S4 method for PLSR,DatasetExperiment model_train(M, D)  # S4 method for PLSDA,DatasetExperiment model_train(M, D)  # S4 method for autoscale,DatasetExperiment model_train(M, D)  # S4 method for blank_filter,DatasetExperiment model_train(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_train(M, D)  # S4 method for dratio_filter,DatasetExperiment model_train(M, D)  # S4 method for filter_by_name,DatasetExperiment model_train(M, D)  # S4 method for filter_na_count,DatasetExperiment model_train(M, D)  # S4 method for filter_smeta,DatasetExperiment model_train(M, D)  # S4 method for glog_transform,DatasetExperiment model_train(M, D)  # S4 method for linear_model,DatasetExperiment model_train(M, D)  # S4 method for mean_centre,DatasetExperiment model_train(M, D)  # S4 method for mv_feature_filter,DatasetExperiment model_train(M, D)  # S4 method for mv_sample_filter,DatasetExperiment model_train(M, D)  # S4 method for OPLSR,DatasetExperiment model_train(M, D)  # S4 method for OPLSDA,DatasetExperiment model_train(M, D)  # S4 method for pareto_scale,DatasetExperiment model_train(M, D)  # S4 method for pqn_norm,DatasetExperiment model_train(M, D)  # S4 method for SVM,DatasetExperiment model_train(M, D)  # S4 method for vec_norm,DatasetExperiment model_train(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_train(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value boxplots — mv_boxplot","title":"Missing value boxplots — mv_boxplot","text":"Boxplots number missing values per sample/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value boxplots — mv_boxplot","text":"","code":"mv_boxplot(   label_outliers = TRUE,   by_sample = TRUE,   factor_name,   show_counts = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value boxplots — mv_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"FALSE\": Missing values plotted per feature. default TRUE. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"FALSE\": number samples box displayed. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value boxplots — mv_boxplot","text":"  mv_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value boxplots — mv_boxplot","text":"","code":"D = MTBLS79_DatasetExperiment() C = mv_boxplot(factor_name='Class') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter features by missing values — mv_feature_filter","title":"Filter features by missing values — mv_feature_filter","text":"Removes features percentage non-missing values falls threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter features by missing values — mv_feature_filter","text":"","code":"mv_feature_filter(   threshold = 20,   qc_label = \"QC\",   method = \"QC\",   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter features by missing values — mv_feature_filter","text":"threshold (numeric) minimum percentage non-missing values. default 20. qc_label (character) label used identify QC/group samples using \"QC\" (within named group) filtering method. default \"QC\". method (character) Filtering method. Allowed values limited following: \"within_one\": Features removed threshold non-missing values met least one group. \"QC\": Features removed threshold non-missing values met named group. \"across\": filter applied ignoring sample group. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter features by missing values — mv_feature_filter","text":" mv_feature_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter features by missing values — mv_feature_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Filter features by missing values — mv_feature_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter features by missing values — mv_feature_filter","text":"","code":"D = iris_DatasetExperiment() M = mv_feature_filter(factor_name='Species',qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per feature — mv_feature_filter_hist","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"histogram proportion missing values per feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"mv_feature_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"  mv_feature_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"C = mv_feature_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value histogram — mv_histogram","title":"Missing value histogram — mv_histogram","text":"histogram numbers missing values per sample/feature","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value histogram — mv_histogram","text":"","code":"mv_histogram(label_outliers = TRUE, by_sample = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value histogram — mv_histogram","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"FALSE\": Missing values plotted per feature. default TRUE. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value histogram — mv_histogram","text":"  mv_histogram  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value histogram — mv_histogram","text":"","code":"D = MTBLS79_DatasetExperiment() C = mv_histogram(label_outliers=FALSE,by_sample=FALSE) chart_plot(C,D) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. #> Warning: Removed 2 rows containing missing values (`geom_bar()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value sample filter — mv_sample_filter","title":"Missing value sample filter — mv_sample_filter","text":"Removes samples percent number missing values exceeds threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value sample filter — mv_sample_filter","text":"","code":"mv_sample_filter(mv_threshold = 20, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value sample filter — mv_sample_filter","text":"mv_threshold (numeric) maximum percentage features missing values sample. default 20. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value sample filter — mv_sample_filter","text":" mv_sample_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing value sample filter — mv_sample_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing value sample filter — mv_sample_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value sample filter — mv_sample_filter","text":"","code":"C = mv_sample_filter()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per sample — mv_sample_filter_hist","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"histogram proportion missing values per sample","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"mv_sample_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"  mv_sample_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"C = mv_sample_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"nth root transform — nroot_transform","title":"nth root transform — nroot_transform","text":"values data matrix transformed raising power 1/n.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nth root transform — nroot_transform","text":"","code":"nroot_transform(root = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nth root transform — nroot_transform","text":"root (numeric) nth root used transform. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nth root transform — nroot_transform","text":" nroot_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nth root transform — nroot_transform","text":"","code":"M = nroot_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"ontology cache — ontology_cache","title":"ontology cache — ontology_cache","text":"cached list ontology terms obtained ontology lookup service (OLS) ontology terms specified objects structToolbox.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ontology cache — ontology_cache","text":"","code":"ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ontology cache — ontology_cache","text":"list cached ontology terms","code":""},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ontology cache — ontology_cache","text":"","code":"cache = ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairs filter — pairs_filter","title":"Pairs filter — pairs_filter","text":"filter used study designs paired sampling ensure  measurements source (e.g. patient) represented factor levels interactions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairs filter — pairs_filter","text":"","code":"pairs_filter(factor_name, sample_id, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairs filter — pairs_filter","text":"factor_name (character) name sample-meta column use. sample_id (character) Name sample meta column containing sample identifiers. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairs filter — pairs_filter","text":" pairs_filter object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairs filter — pairs_filter","text":"","code":"M=pairs_filter(factor_name='Class',sample_id='ids')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Pareto scaling — pareto_scale","title":"Pareto scaling — pareto_scale","text":"mean sample subtracted samples scaled square root standard deviation. transformed data zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pareto scaling — pareto_scale","text":"","code":"pareto_scale(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pareto scaling — pareto_scale","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pareto scaling — pareto_scale","text":" pareto_scale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pareto scaling — pareto_scale","text":"","code":"D = iris_DatasetExperiment() M = pareto_scale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA biplot — pca_biplot","title":"PCA biplot — pca_biplot","text":"scatter plot selected principal component scores overlaid corresponding principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA biplot — pca_biplot","text":"","code":"pca_biplot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   scale_factor = 0.95,   style = \"points\",   label_features = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA biplot — pca_biplot","text":"components (numeric) principal components used generate plot. default c(1, 2). points_to_label (character) points_to_label. Allowed values limited following: \"\": samples labelled plot. \"outliers\": Potential outliers labelled plot. default \"none\". factor_name (character) name sample-meta column use. scale_factor (numeric) scaling factor applied loadings. default 0.95. style (character) Plot style. Allowed values limited following: \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (logical) Add feature labels. Allowed values limited following: \"FALSE\": Features labelled. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA biplot — pca_biplot","text":"  pca_biplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA biplot — pca_biplot","text":"","code":"C = pca_biplot(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA correlation plot — pca_correlation_plot","title":"PCA correlation plot — pca_correlation_plot","text":"plot correlation variables/features selected principal component scores. Features high correlation well represented selected component(s)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"pca_correlation_plot(components = c(1, 2), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA correlation plot — pca_correlation_plot","text":"components (numeric) Principal Components used generate plot. default c(1, 2). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA correlation plot — pca_correlation_plot","text":"  pca_correlation_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"C = pca_correlation_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"d-statistic plot — pca_dstat_plot","title":"d-statistic plot — pca_dstat_plot","text":"bar chart d-statistics samples input PCA model. Samples indicated threshold considered outlying.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"d-statistic plot — pca_dstat_plot","text":"","code":"pca_dstat_plot(number_components = 2, alpha = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"d-statistic plot — pca_dstat_plot","text":"number_components (numeric) number principal components use. default 2. alpha (numeric) confidence threshold rejecting samples based d-statistic. default 0.05. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"d-statistic plot — pca_dstat_plot","text":"  pca_dstat_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"d-statistic plot — pca_dstat_plot","text":"","code":"C = pca_dstat_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA loadings plot — pca_loadings_plot","title":"PCA loadings plot — pca_loadings_plot","text":"barchart (one component) scatter plot (two components) selected principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"pca_loadings_plot(   components = c(1, 2),   style = \"points\",   label_features = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA loadings plot — pca_loadings_plot","text":"components (numeric) principal components used generate plot. default c(1, 2). style (character) Plot style. Allowed values limited following: \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (character, NULL) Feature labels. Allowed values limited following: \"NULL\": labels. \"row.names\": Labels extracted column names data matrix. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA loadings plot — pca_loadings_plot","text":"  pca_loadings_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"C = pca_loadings_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA scores plot — pca_scores_plot","title":"PCA scores plot — pca_scores_plot","text":"Plots 2d scatter plot selected components","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA scores plot — pca_scores_plot","text":"","code":"pca_scores_plot(   xcol = \"PC1\",   ycol = \"PC2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA scores plot — pca_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"PC1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"PC2\". points_to_label (character) Points label. Allowed values limited following: \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA scores plot — pca_scores_plot","text":"  pca_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA scores plot — pca_scores_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre() + PCA() M = model_apply(M,D) C = pca_scores_plot(factor_name = 'Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree plot — pca_scree_plot","title":"Scree plot — pca_scree_plot","text":"plot percent variance cumulative percent variance components PCA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree plot — pca_scree_plot","text":"","code":"pca_scree_plot(max_pc = 15, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree plot — pca_scree_plot","text":"max_pc (numeric, integer) maximum number components include plot. default 15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree plot — pca_scree_plot","text":"  pca_scree_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree plot — pca_scree_plot","text":"","code":"C = pca_scree_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation test — permutation_test","title":"Permutation test — permutation_test","text":"permutation test generates \"null\" model randomising response (regression models) group labels (classification models). repeated many times generate distribution performance metrics null model. distribution can compared performance true model. overlap true null model performances model overfitted.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutation test — permutation_test","text":"","code":"permutation_test(number_of_permutations = 50, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutation test — permutation_test","text":"number_of_permutations (numeric, integer) number permutations. default 50. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutation test — permutation_test","text":" permutation_test object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permutation test — permutation_test","text":"","code":"I=permutation_test(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"permutation_test_plot class — permutation_test_plot","title":"permutation_test_plot class — permutation_test_plot","text":"Plots results permutation test.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"permutation_test_plot(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"permutation_test_plot class — permutation_test_plot","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"permutation_test_plot class — permutation_test_plot","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"C = permutation_test_plot(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":null,"dir":"Reference","previous_headings":"","what":"Permute Sample Order — permute_sample_order","title":"Permute Sample Order — permute_sample_order","text":"order samples data matrix randomly permuted. relationship samples sample meta data maintained.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permute Sample Order — permute_sample_order","text":"","code":"permute_sample_order(number_of_permutations = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permute Sample Order — permute_sample_order","text":"number_of_permutations (numeric, integer) number times sample order permuted. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permute Sample Order — permute_sample_order","text":" permute_sample_order object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permute Sample Order — permute_sample_order","text":"","code":"C = permute_sample_order()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"pls_regcoeff_plot class — pls_regcoeff_plot","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Plots regression coefficients PLSDA model. Plots regression coefficient scores PLSDA model","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"pls_regcoeff_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"ycol (character, numeric, integer) Y column plot. default 1. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"  pls_regcoeff_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"object makes use functionality following packages: ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-2, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_regcoeff_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA scores plot — pls_scores_plot","title":"PLSDA scores plot — pls_scores_plot","text":"scatter plot selected PLSDA scores.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"pls_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )  plsda_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA scores plot — pls_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"LV1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"LV2\". points_to_label (character) Points label. Allowed values limited following: \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA scores plot — pls_scores_plot","text":"  pls_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_scores_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA VIP plot — pls_vip_plot","title":"PLSDA VIP plot — pls_vip_plot","text":"plot Variable Importance Projection (VIP) scores PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"pls_vip_plot(threshold = 1, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA VIP plot — pls_vip_plot","text":"threshold (numeric, integer) threshold indicating significant features. default 1. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA VIP plot — pls_vip_plot","text":"  pls_vip_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA VIP plot — pls_vip_plot","text":"object makes use functionality following packages: ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA VIP plot — pls_vip_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-2, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_vip_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA feature importance summary plot — plsda_feature_importance_plot","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"plot selected feature significance metric PLSDA model top selected features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"plsda_feature_importance_plot(n_features = 30, metric = \"vip\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"n_features (numeric, integer) number features include summary. default 30. metric (character) Metric plot. Allowed values limited following: \"sr_pvalue\": Plot SR p-values. \"vip\": Plot Variable Importance Projection scores. default \"vip\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"  plsda_feature_importance_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"object makes use functionality following packages: ggplot2 reshape2 cowplot","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-2, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org. Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/. Wilke C (2020). cowplot: Streamlined Plot Theme Plot Annotations 'ggplot2'. R package version 1.1.1, https://CRAN.R-project.org/package=cowplot.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_feature_importance_plot(n_features=30,metric='vip') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA predicted plot — plsda_predicted_plot","title":"PLSDA predicted plot — plsda_predicted_plot","text":"plot regression coefficients PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"plsda_predicted_plot(factor_name, style = \"boxplot\", ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA predicted plot — plsda_predicted_plot","text":"factor_name (character) name sample-meta column use. style (character) Plot style. Allowed values limited following: \"violin\": violin plot. \"density\": density plot. default \"boxplot\". ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA predicted plot — plsda_predicted_plot","text":"  plsda_predicted_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA predicted plot — plsda_predicted_plot","text":"object makes use functionality following packages: ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA predicted plot — plsda_predicted_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-2, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_predicted_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA ROC plot — plsda_roc_plot","title":"PLSDA ROC plot — plsda_roc_plot","text":"Receiver Operator Characteristic (ROC) plot PLSDA models computed adjusting threshold assigning group labels PLS predictions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"plsda_roc_plot(factor_name, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA ROC plot — plsda_roc_plot","text":"factor_name (character) name sample-meta column use. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA ROC plot — plsda_roc_plot","text":"  plsda_roc_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA ROC plot — plsda_roc_plot","text":"object makes use functionality following packages: ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA ROC plot — plsda_roc_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-2, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_roc_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Cook's distance barchart — plsr_cook_dist","title":"Cook's distance barchart — plsr_cook_dist","text":"barchart Cook's distance sample used train PLSR model. Cook's distance used estimate influence sample model can used identify potential outliers.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"plsr_cook_dist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cook's distance barchart — plsr_cook_dist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cook's distance barchart — plsr_cook_dist","text":"  plsr_cook_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"C = plsr_cook_dist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR prediction plot — plsr_prediction_plot","title":"PLSR prediction plot — plsr_prediction_plot","text":"scatter plot true response values predicted values PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"plsr_prediction_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR prediction plot — plsr_prediction_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR prediction plot — plsr_prediction_plot","text":"  plsr_prediction_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"C = plsr_prediction_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR QQ plot — plsr_qq_plot","title":"PLSR QQ plot — plsr_qq_plot","text":"plot quantiles residuals PLSR model quantiles normal distribution.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"plsr_qq_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR QQ plot — plsr_qq_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR QQ plot — plsr_qq_plot","text":"  plsr_qq_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"C = plsr_qq_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR residuals histogram — plsr_residual_hist","title":"PLSR residuals histogram — plsr_residual_hist","text":"histogram residuals PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"plsr_residual_hist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR residuals histogram — plsr_residual_hist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR residuals histogram — plsr_residual_hist","text":"  plsr_residual_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"C = plsr_residual_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"PQN used normalise differences concentration samples. makes use Quality Control (QC) samples reference. PQN scales median change relative reference order robust changes caused response perturbation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"pqn_norm(   qc_label = \"QC\",   factor_name,   qc_frac = 0,   sample_frac = 0,   ref_method = \"mean\",   ref_mean = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. qc_frac (numeric) value 0 1 indicate minimum proportion QC samples feature must present included computing reference. Default qc_frac = 0. . default 0. sample_frac (numeric) value 0 1 indicate minimum proportion samples feature must present considered computing normalisation coefficients. . default 0. ref_method (character) Reference computation method. Allowed values limited following: \"median\": reference computed median samples matching qc_label_input. default \"mean\". ref_mean (numeric, NULL) single sample use reference normalisation. set NULL reference computed based input parameters (ref_mean, qc_label etc). . default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":" pqn_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"D = iris_DatasetExperiment() M = pqn_norm(factor_name='Species',qc_label='all') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PQN coefficient histogram — pqn_norm_hist","title":"PQN coefficient histogram — pqn_norm_hist","text":"histogram PQN coefficients features","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"pqn_norm_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PQN coefficient histogram — pqn_norm_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PQN coefficient histogram — pqn_norm_hist","text":"  pqn_norm_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"C = pqn_norm_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher's exact test for missing values — prop_na","title":"Fisher's exact test for missing values — prop_na","text":"Fisher's exact test used compare number missing values group. Multiple test corrected p-values computed indicate whether significant difference number missing values across groups feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher's exact test for missing values — prop_na","text":"","code":"prop_na(alpha = 0.05, mtc = \"fdr\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher's exact test for missing values — prop_na","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher's exact test for missing values — prop_na","text":" prop_na object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher's exact test for missing values — prop_na","text":"","code":"M = prop_na(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of determination (R-squared) — r_squared","title":"Coefficient of determination (R-squared) — r_squared","text":"R-squared metric used assess goodness fit regression models. measures much variance one variable can explained another variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"r_squared(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of determination (R-squared) — r_squared","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of determination (R-squared) — r_squared","text":"  r_squared  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"MET = r_squared()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Data resampling — resample","title":"Data resampling — resample","text":"New training sets generated original data selecting samples random. can based levels factor whole dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data resampling — resample","text":"","code":"resample(   number_of_iterations = 10,   method = \"split_data\",   factor_name,   p_train = 0.8,   collect = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data resampling — resample","text":"number_of_iterations (numeric, integer) number training sets generate. default 10. method (character) Resampling method. Allowed values limited following: \"stratified_split\": Samples training set randomly selected level chosen factor. \"equal_split\": Samples training set selected random level main factor group sizes equal. default \"split_data\". factor_name (character) name sample-meta column use. p_train (numeric) proportion samples selected training set. default 0.8. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Data resampling — resample","text":" resample object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data resampling — resample","text":"","code":"I = resample(     number_of_iterations = 10,      factor_name = 'Species',      method = 'split_data',     p_train = 0.8)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"resample_chart class — resample_chart","title":"resample_chart class — resample_chart","text":"Plots results resampling.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"resample_chart class — resample_chart","text":"","code":"resample_chart(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"resample_chart class — resample_chart","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"resample_chart class — resample_chart","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"resample_chart class — resample_chart","text":"","code":"C = resample_chart(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD filter — rsd_filter","title":"RSD filter — rsd_filter","text":"RSD filter calculates relative standard deviation (ratio standard deviation mean) features. feature RSD greater predefined threshold excluded.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD filter — rsd_filter","text":"","code":"rsd_filter(rsd_threshold = 20, qc_label = \"QC\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD filter — rsd_filter","text":"rsd_threshold (numeric) RSD threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD filter — rsd_filter","text":" rsd_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RSD filter — rsd_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RSD filter — rsd_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD filter — rsd_filter","text":"","code":"M = rsd_filter(factor_name='Class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD histogram — rsd_filter_hist","title":"RSD histogram — rsd_filter_hist","text":"histogram calculated RSD values.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD histogram — rsd_filter_hist","text":"","code":"rsd_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD histogram — rsd_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD histogram — rsd_filter_hist","text":"  rsd_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD histogram — rsd_filter_hist","text":"","code":"C = rsd_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Running iterator apply iterator number times DatasetExperiment. example, cross-validation model applied multiple times data, splitting training test sets. input metric object can calculated collected iteration output.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"# S4 method for bootstrap,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for forward_selection_by_rank,DatasetExperiment,metric run(I, D, MET)  # S4 method for grid_search_1d,DatasetExperiment,metric run(I, D, MET)  # S4 method for kfold_xval,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for permutation_test,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for permute_sample_order,DatasetExperiment,metric run(I, D, MET)  # S4 method for resample,DatasetExperiment,metric run(I, D, MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"iterator object D DatasetExperiment object MET metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Modified iterator object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"D = iris_DatasetExperiment() # get some data MET = metric()  # use a metric I = example_iterator() # initialise iterator models(I) = example_model() # set the model I = run(I,D,MET) # run #> Warning: no calculation provided for this metric"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Signal/batch correction for mass spectrometry data — sb_corr","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Applies Quality Control Robust Spline (QC-RSC) method correct signal drift batch differences mass spectrometry data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"sb_corr(   order_col,   batch_col,   qc_col,   smooth = 0,   use_log = TRUE,   min_qc = 4,   qc_label = \"QC\",   spar_lim = c(-1.5, 1.5),   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"order_col (character) column name sample_meta indicating run order samples. batch_col (character) column name sample_meta indicating batch sample measured . qc_col (character) column name sample_meta indicating group sample member . smooth (numeric) amount smoothing applied (0 1). set 0 smoothing parameter estimated using leave-one-cross-validation. default 0. use_log (logical) Log tranformation. Allowed values limited following: \"FALSE\": Signal correction applied input data. default TRUE. min_qc (numeric) minimum number QC samples required signal correction. default 4. qc_label (character) label used identify QC samples. default \"QC\". spar_lim (numeric) two element vector specifying upper lower limits spar = 0. Allows value spar constrained within limits prevent overfitting. default c(-1.5, 1.5). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":" sb_corr object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.12.0, https://bioconductor.org/packages/pmp. Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013). \"Characterising correcting batch variation automated direct infusion mass spectrometry (DIMS) metabolomics workflow.\" Analytical Bioanalytical Chemistry, 405(15), 5147-5157.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"M = sb_corr(order_col='run_order',batch_col='batch_no',qc_col='class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Group scatter chart — scatter_chart","title":"Group scatter chart — scatter_chart","text":"Plots 2d scatter plot input data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group scatter chart — scatter_chart","text":"","code":"scatter_chart(   xcol = 1,   ycol = 2,   points_to_label = \"none\",   factor_name = \"none\",   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group scatter chart — scatter_chart","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default 1. ycol (numeric, integer, character) column name, index, data plot y-axis. default 2. points_to_label (character) Points label. Allowed values limited following: \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. default \"none\". ellipse (character) Plot ellipses. Allowed values limited following: \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group scatter chart — scatter_chart","text":"  scatter_chart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group scatter chart — scatter_chart","text":"","code":"D = iris_DatasetExperiment() C = scatter_chart(         xcol = 'Petal.Width',         ycol = 'Sepal.Width',         factor_name = 'Species'     ) chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split data — split_data","title":"Split data — split_data","text":"data matrix divided two subsets.predefined proportion samples randomly selected training set, remaining samples used test set.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split data — split_data","text":"","code":"split_data(p_train, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split data — split_data","text":"p_train (numeric) proportion samples selected training set. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split data — split_data","text":" split_data object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split data — split_data","text":"","code":"M = split_data(p_train=0.75)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Stratified sampling — stratified_split","title":"Stratified sampling — stratified_split","text":"dataset divided two subsets. predefined proportion samples level factor selected training set, remaining samples used test set. stratification factor level means relative number samples per level approximately equal original dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stratified sampling — stratified_split","text":"","code":"stratified_split(p_train, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stratified sampling — stratified_split","text":"p_train (numeric) proportion samples selected training set. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stratified sampling — stratified_split","text":" stratified_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stratified sampling — stratified_split","text":"","code":"D = iris_DatasetExperiment() M = stratified_split(p_train=0.75,factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/structToolbox.html","id":null,"dir":"Reference","previous_headings":"","what":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox","title":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox","text":"package extends classes defined struct package","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"SVM scatter plot — svm_plot_2d","title":"SVM scatter plot — svm_plot_2d","text":"scatter plot input data group calculated boundary SVM model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SVM scatter plot — svm_plot_2d","text":"","code":"svm_plot_2d(factor_name, npoints = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SVM scatter plot — svm_plot_2d","text":"factor_name (character) name sample-meta column use. npoints (numeric) number grid points used plot boundary. default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SVM scatter plot — svm_plot_2d","text":"  svm_plot_2d  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SVM scatter plot — svm_plot_2d","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SVM scatter plot — svm_plot_2d","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-13, https://CRAN.R-project.org/package=e1071.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SVM scatter plot — svm_plot_2d","text":"","code":"D = iris_DatasetExperiment() M = filter_smeta(mode='exclude',levels='setosa',factor_name='Species') +     mean_centre()+PCA(number_components=2)+     SVM(factor_name='Species',kernel='linear') M = model_apply(M,D)  C = svm_plot_2d(factor_name='Species') chart_plot(C,M[4],predicted(M[3]))"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":null,"dir":"Reference","previous_headings":"","what":"tSNE — tSNE","title":"tSNE — tSNE","text":"t-Distributed Stochastic Neighbor Embedding.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"tSNE — tSNE","text":"","code":"tSNE(   dims = 2,   perplexity = 30,   max_iter = 100,   theta = 0.5,   check_duplicates = FALSE,   init = NULL,   eta = 200,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"tSNE — tSNE","text":"dims (numeric) number tSNE dimensions computed. default 2. perplexity (numeric) Perplexity parameter. default 30. max_iter (numeric) maximum number tSNE iterations. default 100. theta (numeric) Speed/accuracy trade-. value 0 gives exact tSNE. default 0.5. check_duplicates (logical) Check duplicates. Allowed values limited following: \"FALSE\": check exact duplicate samples. default FALSE. init (NULL, data.frame, DatasetExperiment) set coordinates initialising tSNE algorithm. NULL uses random initialisation. default NULL. eta (numeric) learning rate parameter. default 200. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"tSNE — tSNE","text":" tSNE object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"tSNE — tSNE","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"tSNE — tSNE","text":"van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245. Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.16, https://github.com/jkrijthe/Rtsne.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"tSNE — tSNE","text":"","code":"M = tSNE()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — tSNE_scatter","title":"Feature boxplot — tSNE_scatter","text":"plots new representation data applying tSNE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — tSNE_scatter","text":"","code":"tSNE_scatter(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — tSNE_scatter","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — tSNE_scatter","text":"  tSNE_scatter  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Feature boxplot — tSNE_scatter","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Feature boxplot — tSNE_scatter","text":"van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245. Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.16, https://github.com/jkrijthe/Rtsne.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — tSNE_scatter","text":"","code":"M = tSNE_scatter(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Total Ion Count chart. — tic_chart","title":"Total Ion Count chart. — tic_chart","text":"scatter plot Total Ion Count (sum sample) versus run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total Ion Count chart. — tic_chart","text":"","code":"tic_chart(run_order, factor_name, connected = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total Ion Count chart. — tic_chart","text":"run_order (character) column name sample_meta indicating run order samples. factor_name (character) name sample-meta column use. connected (logical) Plot samples connected grey line. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total Ion Count chart. — tic_chart","text":"  tic_chart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Total Ion Count chart. — tic_chart","text":"","code":"D = iris_DatasetExperiment() D$sample_meta$run_order=1:nrow(D) C = tic_chart(factor_name='Species',run_order='run_order') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":null,"dir":"Reference","previous_headings":"","what":"t-test — ttest","title":"t-test — ttest","text":"t-test compares means two factor levels. Multiple-test corrected p-values used indicate significance computed difference features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"t-test — ttest","text":"","code":"ttest(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   equal_variance = FALSE,   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"t-test — ttest","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. paired (logical) Apply paired t-test. default FALSE. paired_factor (character) factor name encodes sample id pairing. default character(0). equal_variance (logical) Equal variance. Allowed values limited following: \"FALSE\": variance group assumed equal Welch (Satterthwaite) approximation used. default FALSE. conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"t-test — ttest","text":" ttest object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"t-test — ttest","text":"","code":"M = ttest(factor_name='Class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Vector normalisation — vec_norm","title":"Vector normalisation — vec_norm","text":"samples data matrix normalised account differences concentration scaling sample sum squares equal 1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vector normalisation — vec_norm","text":"","code":"vec_norm(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vector normalisation — vec_norm","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vector normalisation — vec_norm","text":" vec_norm object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vector normalisation — vec_norm","text":"","code":"M = vec_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — wilcox_p_hist","title":"Histogram of p values — wilcox_p_hist","text":"histogram p values wilcoxon signed rank test","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — wilcox_p_hist","text":"","code":"wilcox_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — wilcox_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — wilcox_p_hist","text":"  wilcox_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — wilcox_p_hist","text":"","code":"M = wilcox_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":null,"dir":"Reference","previous_headings":"","what":"wilcoxon signed rank test — wilcox_test","title":"wilcoxon signed rank test — wilcox_test","text":"Mann-Whitney-Wilcoxon signed rank test compares ,ranks values two groups. non-parametric equivalent t-test. Multiple test corrected p-values computed indicators significance variable/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"wilcox_test(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wilcoxon signed rank test — wilcox_test","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample-meta column use. paired (logical) Apply paired test. default FALSE. paired_factor (character) factor name containing sample ids paired data. default character(0). conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wilcoxon signed rank test — wilcox_test","text":" wilcox_test object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"M = wilcox_test(factor_name='Class')"}]
+[{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"‘structToolbox’ includes extensive set data (pre-)processing analysis tools metabolomics omics, strong emphasis statistics machine learning. methods tools implemented using class-based templates available via struct (Statistics R Using Class-based Templates) package. aim vignette introduce reader basic advanced structToolbox-based operations implementations, use struct objects, getting/setting methods/parameters, building workflows analysis mass spectrometry (MS) nuclear magnetic resonance (NMR)-based Metabolomics proteomics datasets. workflows demonstrated include wide range methods tools including pre-processing filtering, normalisation scaling, followed univariate /multivariate statistics, machine learning approaches.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"latest version structToolbox compatible current R version can installed using BiocManager. number additional packages needed vignette.","code":"# install BiocManager if not present if (!requireNamespace(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  # install structToolbox and dependencies BiocManager::install(\"structToolbox\") ## install additional bioc packages for vignette if needed #BiocManager::install(c('pmp', 'ropls', 'BiocFileCache'))  ## install additional CRAN packages if needed #install.packages(c('cowplot', 'openxlsx'))  suppressPackageStartupMessages({     # Bioconductor packages     library(structToolbox)     library(pmp)     library(ropls)     library(BiocFileCache)        # CRAN libraries     library(ggplot2)     library(gridExtra)     library(cowplot)     library(openxlsx) })   # use the BiocFileCache bfc <- BiocFileCache(ask = FALSE)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-1","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PCA (Principal Component Analysis) PLS (Partial Least Squares) commonly applied methods exploring analysing multivariate datasets. use two statistical methods demonstrate different types struct (STatistics R Using Class Templates) objects available part structToolbox objects (.e. class templates) can used conduct unsupervised supervised multivariate statistical analysis.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes use “Iris” dataset. famous (Fisher’s Anderson’s) dataset contains measurements sepal length width petal length width, centimeters, 50 flowers 3 class Iris. class Iris setosa, versicolor, virginica. See (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/iris.html) information. Note: vignette also compatible Direct infusion mass spectrometry metabolomics “benchmark” dataset described Kirwan et al., Sci Data 1, 140012 (2014) (https://doi.org/10.1038/sdata.2014.12). datasets available part structToolbox package already prepared DatasetExperiment object.","code":"## Iris dataset (comment if using MTBLS79 benchmark data) D = iris_DatasetExperiment() D$sample_meta$class = D$sample_meta$Species  ## MTBLS (comment if using Iris data) # D = MTBLS79_DatasetExperiment(filtered=TRUE) # M = pqn_norm(qc_label='QC',factor_name='sample_type') +  #   knn_impute(neighbours=5) + #   glog_transform(qc_label='QC',factor_name='sample_type') + #   filter_smeta(mode='exclude',levels='QC',factor_name='sample_type') # M = model_apply(M,D) # D = predicted(M)  # show info D ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Fisher's Iris dataset ## description:   This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of ##                  the variables sepal length and width and petal length and width, ##                  respectively, for 50 flowers from each of 3 species of iris. The species are ##                  Iris setosa, versicolor, and virginica. ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"datasetexperiment-objects","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Dataset","what":"DatasetExperiment objects","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"DatasetExperiment object extension SummarizedExperiment class used Bioconductor community. contains three main parts: data data frame containing measured data sample. sample_meta data frame additional information related samples e.g. group labels. variable_meta data frame additional information related variables (features) e.g. annotations Like struct objects also contains name description fields (called “slots” R language). key difference DatasetExperiment SummarizedExperiment objects data transposed. .e. DatasetExperiment objects samples rows features columns, opposite true SummarizedExperiment objects. slots accessible using dollar notation.","code":"# show some data head(D$data[,1:4]) ##   Sepal.Length Sepal.Width Petal.Length Petal.Width ## 1          5.1         3.5          1.4         0.2 ## 2          4.9         3.0          1.4         0.2 ## 3          4.7         3.2          1.3         0.2 ## 4          4.6         3.1          1.5         0.2 ## 5          5.0         3.6          1.4         0.2 ## 6          5.4         3.9          1.7         0.4"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"can apply e.g. PCA first need create PCA object. object contains inputs, outputs methods needed apply PCA. can set parameters number components PCA model created, can also use dollar notation change/view later. inputs model can listed using param_ids(object): summary object can printed console:","code":"P = PCA(number_components=15) P$number_components=5 P$number_components ## [1] 5 param_ids(P) ## [1] \"number_components\" P ## A \"PCA\" object ## -------------- ## name:          Principal Component Analysis (PCA) ## description:   PCA is a multivariate data reduction technique. It summarises the data in a smaller number of ##                  Principal Components that maximise variance. ## input params:  number_components  ## outputs:       scores, loadings, eigenvalues, ssx, correlation, that  ## predicted:     that ## seq_in:        data"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-sequences","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model sequences","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Unless good reason , usually sensible mean centre columns data PCA. Using STRUCT framework can create model sequence mean centre apply PCA mean centred data. structToolbox mean centring PCA model objects, joining using “+” creates model_sequence object. model_sequence outputs first object (mean centring) automatically passed inputs second object (PCA), allows chain together modelling steps order build workflow. objects model_sequence can accessed indexing, can combine dollar notation. example, PCA object second object sequence can access number components follows:","code":"M = mean_centre() + PCA(number_components = 4) M[2]$number_components ## [1] 4"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"trainingtesting-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Training/testing models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Model model_sequence objects need trained using data form DatasetExperiment object. example, PCA model sequence created (M) can trained using iris DatasetExperiment object (‘D’). model sequence now mean centred original data calculated PCA scores loadings. Model objects can used generate predictions test datasets. PCA model sequence involves mean centring test data using mean training data, projecting centred test data onto PCA model using loadings. outputs stored model sequence can accessed using dollar notation. example just use training data (sometimes called autoprediction), PCA allows us explore training data detail. Sometimes models don’t make use training/test approach e.g. univariate statsitics, filtering etc. models model_apply method can used instead. models provide training/test methods, model_apply applies autoprediction default .e. short-cut applying model_train model_predict data. available outputs object can listed accessed like input params, using dollar notation:","code":"M = model_train(M,D) M = model_predict(M,D) M = model_apply(M,D) output_ids(M[2]) ## [1] \"scores\"      \"loadings\"    \"eigenvalues\" \"ssx\"         \"correlation\" ## [6] \"that\" M[2]$scores ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-charts","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model charts","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"struct framework includes chart objects. Charts associated model object can listed. Like model objects, chart objects need created can used. plot PCA scores plot mean centred PCA model.  Note indexing PCA model required pca_scores_plot object requires PCA object input, model_sequence. make changes input parameters chart, chart_plot must called see effects.  chart_plot method returns ggplot object can easily combine plots using gridExtra cowplot packages example.","code":"chart_names(M[2]) ## [1] \"pca_biplot\"           \"pca_correlation_plot\" \"pca_dstat_plot\"       ## [4] \"pca_loadings_plot\"    \"pca_scores_plot\"      \"pca_scree_plot\" C = pca_scores_plot(factor_name='class') # colour by class chart_plot(C,M[2]) # add petal width to meta data of pca scores M[2]$scores$sample_meta$example=D$data[,1] # update plot C$factor_name='example' chart_plot(C,M[2]) # scores plot C1 = pca_scores_plot(factor_name='class') # colour by class g1 = chart_plot(C1,M[2])  # scree plot C2 = pca_scree_plot() g2 = chart_plot(C2,M[2])  # arange in grid grid.arrange(grobs=list(g1,g2),nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"ontology","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Ontology","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Within struct framework (structToolbox) ontology slot provided allow standardardised definitions objects inputs outputs using Ontology Lookup Service (OLS). example, STATO general purpose STATistics Ontology (http://stato-ontology.org). webpage: aim provide coverage processes statistical tests, conditions application, information needed resulting statistical methods, probability distributions, variables, spread variation metrics. STATO also covers aspects experimental design description plots graphical representations commonly used provide visual cues data distribution layout assist review results. ontology object can set assigning ontology term identifier ontology slot struct_class object design time. ids can listed using $ notation: ontology method can used obtain detailed ontology information. cache = NULL struct package automatically attempt use OLS API (via rols package) obtain name description provided identifiers. used cached versions ontology definitions provided structToolbox package prevent issues connecting OLS API building package. Note ontology method returns definitions object (PCA) inputs/outputs (number_of_components).","code":"# create an example PCA object P=PCA()  # ontology for the PCA object P$ontology ## [1] \"OBI:0200051\" ontology(P,cache = ontology_cache()) # set cache = NULL (default) for online use ## [[1]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       OBI:0200051 ## ontology:      obi ## label:         principal components analysis dimensionality reduction ## description:   A principal components analysis dimensionality reduction is a dimensionality reduction ##                  achieved by applying principal components analysis and by keeping low-order principal ##                  components and excluding higher-order ones. ## iri:           http://purl.obolibrary.org/obo/OBI_0200051 ##  ##  ##  ## [[2]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       STATO:0000555 ## ontology:      stato ## label:         number of predictive components ## description:   number of predictive components is a count used as input to the principle component analysis ##                  (PCA) ## iri:           http://purl.obolibrary.org/obo/STATO_0000555"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"validating-supervised-statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Validating supervised statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Validation important aspect chemometric modelling. struct framework enables kind iterative model testing iterator objects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"cross-validation","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Validating supervised statistical models","what":"Cross-validation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Cross validation common technique assessing performance classification models. example use Partial least squares-discriminant analysis (PLS-DA) model. Data mean centred prior PLS, build model sequence first. iterator objects like k-fold cross-validation object (kfold_xval) can created just like struct object. Parameters can set creation using equals sign, accessed changed later using dollar notation. model cross-validated can set/accessed using models method. Alternatively, iterators can combined models using multiplication symbol shorthand models assignement method: run method can used iterator object. iterator run set model model sequence multiple times. case run cross-validation 5 times, splitting data different training test sets time. run method also needs metric specified, another type struct object. metric may calculated iterations, iteration, depending iterator type (resampling, permutation etc). cross-validation calculate “balanced accuracy” iterations. Note balanced_accuracy metric actually reports 1-accuracy, value 0 indicates perfect performance. standard deviation “sd” NA example one permutation. Like struct objects, iterators can chart objects associated . chart_names function list object. Charts iterator objects can plotted way charts object.  possible combine multiple iterators using multiplication symbol. equivalent nesting one iterator inside . example, can repeat cross-validation multiple times permuting sample order.","code":"M = mean_centre() + PLSDA(number_components=2,factor_name='class') M ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # create object XCV = kfold_xval(folds=5,factor_name='class')  # change the number of folds XCV$folds=10 XCV$folds ## [1] 10 models(XCV)=M models(XCV) ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # cross validation of a mean centred PLSDA model XCV = kfold_xval(         folds=5,         method='venetian',         factor_name='class') *        (mean_centre() + PLSDA(factor_name='class')) XCV = run(XCV,D,balanced_accuracy()) XCV$metric ##              metric mean sd ## 1 balanced_accuracy 0.11 NA chart_names(XCV) ## [1] \"kfoldxcv_grid\"   \"kfoldxcv_metric\" C = kfoldxcv_grid(   factor_name='class',   level=levels(D$sample_meta$class)[2]) # first level chart_plot(C,XCV) # permute sample order 10 times and run cross-validation P = permute_sample_order(number_of_permutations = 10) *      kfold_xval(folds=5,factor_name='class')*     (mean_centre() + PLSDA(factor_name='class',number_components=2)) P = run(P,D,balanced_accuracy()) P$metric ##              metric   mean          sd ## 1 balanced_accuracy 0.1095 0.004972145"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-2","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"vignette provides overview structToolbox workflow implemented process (e.g. filter features, signal drift batch correction, normalise missing value imputation) mass spectrometry data. workflow exists methods part Peak Matrix Processing (pmp) package, including range additional filters described Kirwan et al., 2013, 2014. packages required vignette addition structToolbox:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-1","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes process analyse MTBLS79 dataset (‘Dataset 7:SFPM’ Kirwan et al., 2014. dataset represents systematic evaluation reproducibility multi-batch direct-infusion mass spectrometry (DIMS)-based metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step data processing workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79). MTBLS79_DatasetExperiment object included structToolbox package processed version MTBLS79 dataset available peak matrix processing (pmp) package. vignette describes step step structToolbox version created pmp version (.e. ‘Dataset 7:SFPM’ Scientific Data publication - https://doi.org/10.1038/sdata.2014.12). SummarizedExperiment object pmp package needs converted DatasetExperiment object use structToolbox. Full processing data set requires number steps. applied using single struct model sequence (model_seq).","code":"# the pmp SE object SE = MTBLS79  # convert to DE DE = as.DatasetExperiment(SE) DE$name = 'MTBLS79' DE$description = 'Converted from SE provided by the pmp package'  # add a column indicating the order the samples were measured in DE$sample_meta$run_order = 1:nrow(DE)  # add a column indicating if the sample is biological or a QC Type=as.character(DE$sample_meta$Class) Type[Type != 'QC'] = 'Sample' DE$sample_meta$Type = factor(Type)  # add a column for plotting batches DE$sample_meta$batch_qc = DE$sample_meta$Batch DE$sample_meta$batch_qc[DE$sample_meta$Type=='QC']='QC'  # convert to factors DE$sample_meta$Batch = factor(DE$sample_meta$Batch) DE$sample_meta$Type = factor(DE$sample_meta$Type) DE$sample_meta$Class = factor(DE$sample_meta$Class) DE$sample_meta$batch_qc = factor(DE$sample_meta$batch_qc)  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          MTBLS79 ## description:   Converted from SE provided by the pmp package ## data:          172 rows x 2488 columns ## sample_meta:   172 rows x 7 columns ## variable_meta: 2488 rows x 0 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"signal-drift-and-batch-correction","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Signal drift and batch correction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"batch correction algorithm applied reduce intra- inter- batch variations dataset. Quality Control-Robust Spline Correction (QC-RSC) provided pmp package, wrapped structToolbox object called sb_corr. figure shows plot feature vs run order, correction. fitted spline batch shown grey. can seen correction removed instrument drift within batches.   additional step added published workflow remove feature corrected QCRCMS. can occur enough measured QC values within batch. QCRMS pmp package currently returns NA samples feature occurs. Features occurs excluded. output step output MTBLS79_DatasetExperiment(filtered=FALSE).","code":"M = # batch correction     sb_corr(       order_col='run_order',       batch_col='Batch',        qc_col='Type',        qc_label='QC',       spar_lim = c(0.6,0.8)      )  M = model_apply(M,DE) C = feature_profile(       run_order='run_order',       qc_label='QC',       qc_column='Type',       colour_by='batch_qc',       feature_to_plot='200.03196',       plot_sd=FALSE   )  # plot and modify using ggplot2  chart_plot(C,M,DE)+ylab('Peak area')+ggtitle('Before') chart_plot(C,predicted(M))+ylab('Peak area')+ggtitle('After') M2 = filter_na_count(       threshold=3,       factor_name='Batch'     ) M2 = model_apply(M2,predicted(M))  # calculate number of features removed nc = ncol(DE) - ncol(predicted(M2))  cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 425"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"feature-filtering","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Feature filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"journal article three spectral cleaning steps applied. first filter Kruskal-Wallis test used identify features reliably detected QC samples (p < 0.0001) batches. follow parameters original article use multiple test correction (mtc = 'none'). make use univariate tests kw_rank_sum filter advanced features struct needed. Slots predicted, seq_in used ensure correct output univariate test connected correct input feature filter using filter_by_name. Another slot seq_fcn used extract relevant column predicted output compatible seq_in input. placeholder used “names” parameter (names = 'place_holder') input replaced output seq_fcn. second filter Wilcoxon Signed-Rank test. used identify features representative average biological samples (p < 1e-14). make use seq_in seq_fcn. Finally, RSD filter used remove features high analytical variation (QC RSD > 20 removed) output filter output MTBLS79_DatasetExperiment(filtered=TRUE).","code":"M3 = kw_rank_sum(       alpha=0.0001,       mtc='none',       factor_names='Batch',       predicted='significant'     ) +     filter_by_name(       mode='exclude',       dimension = 'variable',       seq_in = 'names',        names='seq_fcn', # this is a placeholder and will be replaced by seq_fcn       seq_fcn=function(x){return(x[,1])}     ) M3 = model_apply(M3, predicted(M2))  nc = ncol(predicted(M2)) - ncol(predicted(M3)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 262 M4 = wilcox_test(       alpha=1e-14,       factor_names='Type',        mtc='none',        predicted = 'significant'     ) +     filter_by_name(       mode='exclude',       dimension='variable',       seq_in='names',        names='place_holder',       seq_fcn=function(x){return(x$significant)}     ) M4 = model_apply(M4, predicted(M3))  nc = ncol(predicted(M3)) - ncol(predicted(M4)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 169 M5 = rsd_filter(      rsd_threshold=20,      factor_name='Type' ) M5 = model_apply(M5,predicted(M4))  nc = ncol(predicted(M4)) - ncol(predicted(M5)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 53"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"normalisation-missing-value-imputation-and-scaling","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Normalisation, missing value imputation and scaling","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"apply number common pre-processing steps filtered peak matrix identical steps applied described Kirwan et al. 2013, 2014. Probabilistic Quotient Normalisation (PQN) k-nearest neighbours imputation (k = 5) Generalised log transform (glog) steps prepare data multivariate analysis accounting sample concentration differences, imputing missing values scaling data.","code":"# peak matrix processing M6 = pqn_norm(qc_label='QC',factor_name='Type') +       knn_impute(neighbours=5) +      glog_transform(qc_label='QC',factor_name='Type') M6 = model_apply(M6,predicted(M5))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Exploratory Analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Principal Component Analysis (PCA) can used visualise high-dimensional data. unsupervised method maximises variance reduced number latent variables, principal components.  plot similar Figure 3b original publication link. Sample replicates represented colours samples groups (C = cow S = Sheep) different shapes. Plotting scores colouring Batch indicates signal/batch correction effective batches overlapping.","code":"# PCA M7  = mean_centre() + PCA(number_components = 2)  # apply model sequence to data M7 = model_apply(M7,predicted(M6))  # plot pca scores C = pca_scores_plot(factor_name=c('Sample_Rep','Class'),ellipse='none') chart_plot(C,M7[2]) + coord_fixed() +guides(colour=FALSE) ## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use \"none\" instead as ## of ggplot2 3.3.4. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. # chart object C = pca_scores_plot(factor_name=c('Batch'),ellipse='none') # plot chart_plot(C,M7[2]) + coord_fixed()"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-3","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette demonstrate 1) apply validate Partial Least Squares (PLS) analysis using structToolbox, 2) reproduce statistical analysis Thevenot et al. (2015) 3. compare different implementations PLS.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-2","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"objective original study : > …study influence age, body mass index (bmi), gender metabolite concentrations urine, analysing 183 samples cohort adults liquid chromatography coupled high-resolution mass spectrometry. Thevenot et al. (2015) “Sacurine” dataset needs converted DatasetExperiment object. ropls package provides data list containing dataMatrix, sampleMetadata variableMetadata.","code":"data('sacurine',package = 'ropls') # the 'sacurine' list should now be available  # move the annotations to a new column and rename the features by index to avoid issues # later when data.frames get transposed and names get checked/changed sacurine$variableMetadata$annotation=rownames(sacurine$variableMetadata) rownames(sacurine$variableMetadata)=1:nrow(sacurine$variableMetadata) colnames(sacurine$dataMatrix)=1:ncol(sacurine$dataMatrix)  # create DatasetExperiment DE = DatasetExperiment(data = data.frame(sacurine$dataMatrix),                        sample_meta = sacurine$sampleMetadata,                        variable_meta = sacurine$variableMetadata,                        name = 'Sacurine data',                        description = 'See ropls package documentation for details')  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Sacurine data ## description:   See ropls package documentation for details ## data:          183 rows x 109 columns ## sample_meta:   183 rows x 3 columns ## variable_meta: 109 rows x 4 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Sacurine dataset used within vignette already pre-processed: signal drift batch effect correction intensities, urine profile normalized osmolality sample. Finally, data log10 transformed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-data-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Exploratory data analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Since data already processed data can visualised using Principal Component Analysis (PCA) without pre-processing. ropls package automatically applies unit variance scaling (autoscaling) default. approach applied .  third plot coloured gender (C) identical Figure 2 ropls package vignette. structToolbox package provides range PCA-related diagnostic plots, including D-statistic, scree, loadings plots. plots can used explore variance data.","code":"# prepare model sequence M = autoscale() + PCA(number_components = 5) # apply model sequence to dataset M = model_apply(M,DE)  # pca scores plots g=list() for (k in colnames(DE$sample_meta)) {     C = pca_scores_plot(factor_name = k)     g[[k]] = chart_plot(C,M[2]) } # plot using cowplot plot_grid(plotlist=g, nrow=1, align='vh', labels=c('A','B','C')) C = pca_scree_plot() g1 = chart_plot(C,M[2])  C = pca_loadings_plot() g2 = chart_plot(C,M[2])  C = pca_dstat_plot(alpha=0.95) g3 = chart_plot(C,M[2])  p1=plot_grid(plotlist = list(g1,g2),align='h',nrow=1,axis='b') p2=plot_grid(plotlist = list(g3),nrow=1) plot_grid(p1,p2,nrow=2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"partial-least-squares-pls-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Partial Least Squares (PLS) analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"ropls package uses implementation (O)PLS algorithms. structToolbox uses pls package, interesting compare outputs approaches. simplicity scores plots compared.  plot similar fig.3 ropls vignette. Differences due inverted LV axes, common occurrence NIPALS algorithm (used structToolbox ropls) depends algorithm initialised. compare R2 values model structToolbox use regression model, instead discriminant model. convert gender factor numeric variable applying model.  ropls package automatically applies cross-validation asses performance PLSDA model. structToolbox applied separately give control approach used desired. default cross-validation used ropls package 7-fold cross-validation replicate . Training set R2: 0.6975706 0.6798415 0.646671 0.6532914 0.7109769 0.670777 0.6935344 Test set Q2: 0.5460723 validity model can assessed using permutation testing. return discriminant model.  permuted models balanced accuracy around 50%, expected dataset two groups. unpermuted models balanced accuracy around 90% therefore much better might expected occur chance.","code":"# prepare model sequence M = autoscale() + PLSDA(factor_name='gender') M = model_apply(M,DE)  C = pls_scores_plot(factor_name = 'gender') chart_plot(C,M[2]) # convert gender to numeric DE$sample_meta$gender=as.numeric(DE$sample_meta$gender)  # models sequence M = autoscale(mode='both') + PLSR(factor_name='gender',number_components=3) M = model_apply(M,DE)  # some diagnostic charts C = plsr_cook_dist() g1 = chart_plot(C,M[2])  C = plsr_prediction_plot() g2 = chart_plot(C,M[2])  C = plsr_qq_plot() g3 = chart_plot(C,M[2])  C = plsr_residual_hist() g4 = chart_plot(C,M[2])  plot_grid(plotlist = list(g1,g2,g3,g4), nrow=2,align='vh') # model sequence M = kfold_xval(folds=7, factor_name='gender') *      (autoscale(mode='both') + PLSR(factor_name='gender')) M = run(M,DE,r_squared()) # reset gender to original factor DE$sample_meta$gender=sacurine$sampleMetadata$gender # model sequence M = permutation_test(number_of_permutations = 10, factor_name='gender') *     kfold_xval(folds=7,factor_name='gender') *     (autoscale() + PLSDA(factor_name='gender',number_components = 3)) M = run(M,DE,balanced_accuracy())  C = permutation_test_plot(style='boxplot') chart_plot(C,M)+ylab('1 - balanced accuracy')"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-4","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"purpose vignette demonstrate different functionalities methods available part structToolbox reproduce data analysis reported Mendez et al., (2020) Chan et al., (2016).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-3","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). experimental raw data available Metabolomics Workbench (PR000699) processed version available Excel data file. first step need reorganise convert Excel data file DatasetExperiment object. Using openxlsx package file can read directly R data.frame manipulated required.","code":"url = 'https://github.com/CIMCB/MetabWorkflowTutorial/raw/master/GastricCancer_NMR.xlsx'  # read in file directly from github... # X=read.xlsx(url)  # ...or use BiocFileCache path = bfcrpath(bfc,url) ## adding rname 'https://github.com/CIMCB/MetabWorkflowTutorial/raw/master/GastricCancer_NMR.xlsx' X = read.xlsx(path)  # sample meta data SM=X[,1:4] rownames(SM)=SM$SampleID # convert to factors SM$SampleType=factor(SM$SampleType) SM$Class=factor(SM$Class) # keep a numeric version of class for regression SM$Class_num = as.numeric(SM$Class)  ## data matrix # remove meta data X[,1:4]=NULL rownames(X)=SM$SampleID  # feature meta data VM=data.frame(idx=1:ncol(X)) rownames(VM)=colnames(X)  # prepare DatasetExperiment DE = DatasetExperiment(     data=X,     sample_meta=SM,     variable_meta=VM,     description='1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer',     name='Gastric cancer (NMR)')  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 149 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 149 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-pre-processing-and-quality-assessment","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Data pre-processing and quality assessment","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"good practice remove features may low quality, assess quality data general. Tutorial features QC-RSD > 20% 10% features missing retained. Note additional feature vs processing reported Mendez et. al. filters use >= <= instead > <. suitable scaling transformation PCA can used assess data quality. expected biological variance (samples) larger technical variance (QCs). workflow reproducing (link) following steps applied: log10 transform autoscaling (scaled unit variance) knn imputation (3 neighbours) transformed scaled matrix used input PCA. Using struct can chain steps single model sequence.","code":"# prepare model sequence M = rsd_filter(rsd_threshold=20,qc_label='QC',factor_name='Class') +     mv_feature_filter(threshold = 10,method='across',factor_name='Class')  # apply model M = model_apply(M,DE)  # get the model output filtered = predicted(M)  # summary of filtered data filtered ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # prepare the model sequence M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3) +     PCA(number_components = 10)  # apply model sequence to data M = model_apply(M,filtered)  # get the transformed, scaled and imputed matrix TSI = predicted(M[3])  # scores plot C = pca_scores_plot(factor_name = 'SampleType') g1 = chart_plot(C,M[4])  # loadings plot C = pca_loadings_plot() g2 = chart_plot(C,M[4])  plot_grid(g1,g2,align='hv',nrow=1,axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"univariate-statistics","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Univariate statistics","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"structToolbox provides number objects ttest, counting numbers features etc. brevity ttest calculated comparison workflow following (link). QC samples need excluded, data reduced GC groups.","code":"# prepare model TT = filter_smeta(mode='include',factor_name='Class',levels=c('GC','HE')) +        ttest(alpha=0.05,mtc='fdr',factor_names='Class')  # apply model TT = model_apply(TT,filtered)  # keep the data filtered by group for later filtered = predicted(TT[1])  # convert to data frame out=as_data_frame(TT[2])  # show first few features head(out) ##     t_statistic   t_p_value t_significant estimate.mean.GC estimate.mean.HE ## M4   -3.5392652 0.008421042          TRUE         26.47778         51.73947 ## M5    1.4296604 0.410396437         FALSE        265.11860        169.91500 ## M7    2.7456506 0.051494976         FALSE        118.52558         53.98718 ## M8   -2.1294198 0.178392032         FALSE         54.39535         79.26750 ## M11   0.5106536 0.776939682         FALSE        201.34390        171.27949 ## M14  -1.4786810 0.403091881         FALSE         61.53171         83.90250 ##         lower      upper ## M4  -39.56162 -10.961769 ## M5  -38.04747 228.454679 ## M7   17.60818 111.468619 ## M8  -48.20069  -1.543611 ## M11 -87.30604 147.434869 ## M14 -52.57754   7.835950"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"training-and-test-sets","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Training and Test sets","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Splitting data training test sets important aspect machine learning. structToolbox implemented using split_data object random subsampling across whole dataset, stratified_split splitting based group sizes, approach used Mendez et al.","code":"# prepare model M = stratified_split(p_train=0.75,factor_name='Class') # apply to filtered data M = model_apply(M,filtered) # get data from object train = M$training train ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Training set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a training set ## data:          62 rows x 53 columns ## sample_meta:   62 rows x 5 columns ## variable_meta: 53 rows x 1 columns cat('\\n') test = M$testing test ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Testing set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a test set ## data:          21 rows x 53 columns ## sample_meta:   21 rows x 5 columns ## variable_meta: 53 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"optimal-number-of-pls-components","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Optimal number of PLS components","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Mendez et al k-fold cross-validation used determine optimal number PLS components. 100 bootstrap iterations used generate confidence intervals. strucToolbox implemented using “iterator” objects, can combined model objects. R2 used metric optimisation, PLSR model structToolbox used. speed 10 bootstrap iterations used .  chart plotted shows Q2, comparable Figure 13 Mendez et al . Two components selected Mendez et al, use .","code":"# scale/transform training data M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3,by='samples')  # apply model M = model_apply(M,train)  # get scaled/transformed training data train_st = predicted(M)  # prepare model sequence MS = grid_search_1d(         param_to_optimise = 'number_components',         search_values = as.numeric(c(1:6)),         model_index = 2,         factor_name = 'Class_num',         max_min = 'max') *      permute_sample_order(         number_of_permutations = 10) *      kfold_xval(         folds = 5,         factor_name = 'Class_num') *       (mean_centre(mode='sample_meta')+       PLSR(factor_name='Class_num'))  # run the validation MS = struct::run(MS,train_st,r_squared())  # C = gs_line() chart_plot(C,MS)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-model-evalutation","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS model evalutation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"evaluate model discriminant analysis structToolbox PLSDA model appropriate.  Note default cutoff B figure PLS models structToolbox 0, groups encoded +/-1. impact overall performance model.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  # charts C = plsda_predicted_plot(factor_name='Class',style='boxplot') g1 = chart_plot(C,P)  C = plsda_predicted_plot(factor_name='Class',style='density') g2 = chart_plot(C,P)+xlim(c(-2,2))  C = plsda_roc_plot(factor_name='Class') g3 = chart_plot(C,P)  plot_grid(g1,g2,g3,align='vh',axis='tblr',nrow=1, labels=c('A','B','C')) # AUC for comparison with Mendez et al MET = calculate(AUC(),P$y$Class,P$yhat[,1]) MET ## A \"AUC\" object ## -------------- ## name:          Area under ROC curve ## description:   The area under the ROC curve of a classifier is estimated using the trapezoid method. ## value:         0.9739583"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"permutation-test","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Permutation test","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"permutation test can used assess likely observed result occurred chance. structToolbox permutation_test iterator object can combined iterators models.  plot comparable bottom half Figure 17 Mendez et. al.. unpermuted (true) Q2 values consistently better permuted (null) models. .e. model reliable.","code":"# model sequence MS = permutation_test(number_of_permutations = 20,factor_name = 'Class_num') *       kfold_xval(folds = 5,factor_name = 'Class_num') *      (mean_centre(mode='sample_meta') + PLSR(factor_name='Class_num', number_components = 2))  # run iterator MS = struct::run(MS,train_st,r_squared())  # chart C = permutation_test_plot(style = 'density')  chart_plot(C,MS) + xlim(c(-1,1)) + xlab('R Squared')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-projection-plots","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS projection plots","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PLS can also used visualise model interpret latent variables.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  C = pls_scores_plot(components=c(1,2),factor_name = 'Class') chart_plot(C,P)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-feature-importance","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS feature importance","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Regression coefficients VIP scores can used estimate importance individual features PLS model. Mendez et al bootstrapping used estimate confidence intervals, brevity skip .","code":"# prepare chart C = pls_vip_plot(ycol = 'HE') g1 = chart_plot(C,P)  C = pls_regcoeff_plot(ycol='HE') g2 = chart_plot(C,P)  plot_grid(g1,g2,align='hv',axis='tblr',nrow=2)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-5","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette illustrate apply SVM analysis Classifying Metabolomics data. Support vector Machines (SVM) commonly used method Machine Learning. classification tasks used generate boundary groups samples training set. well generating linear boundaries, SVM can extended exploit use kernels generate complex non-linear boundaries groups required. structToolbox package, SVM functionality provided e1071 package incorporated model object. chart object (svm_plot_2d) also available plot SVM boundaries data two variables.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-4","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). raw experimental data available Metabolomics Workbench (PR000699) processed version available Excel data file. simplicity use pre-processed version 1H-NMR “Gastric cancer” dataset using structToolbox package. Details regards pre-processing reported “NMR_clinical_metabolomics” vignette `r Biocpkg(“structToolbox”) package. purposes illustrating effect SVM parameters boundary groups, reduce data include GC groups apply PLS reduce data two components. treat PLS scores two group dataset two features.","code":"# summary of DatasetExperiment object DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # model sequence and pls model (NB data already centred) MS = filter_smeta(mode = 'include', levels = c('GC','HE'), factor_name = 'Class') +      PLSDA(factor_name = 'Class',number_components = 2)  # apply PLS model MS = model_apply(MS,DE)  # plot the data C = pls_scores_plot(factor_name = 'Class') chart_plot(C,MS[2]) # new DatasetExperiment object from the PLS scores DE2 = DatasetExperiment(   data = MS[2]$scores$data,    sample_meta = predicted(MS[1])$sample_meta,   variable_meta = data.frame('LV'=c(1,2),row.names = colnames(MS[2]$scores)),   name = 'Illustrativate SVM dataset',   description = 'Generated by applying PLS to the processed Gastric cancer (NMR) dataset' )  DE2 ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Illustrativate SVM dataset ## description:   Generated by applying PLS to the processed Gastric cancer (NMR) dataset ## data:          83 rows x 2 columns ## sample_meta:   83 rows x 5 columns ## variable_meta: 2 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"basic-svm-model","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Basic SVM model","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"simplest SVM model uses linear kernel. structToolbox SVM model can used train apply SVM models. svm_plot_2d chart object provided visualisation boundaries two dimensions.  SVM boundary plotted black, margins grey support vectors indicated grey circles.","code":"# SVM model M = SVM(   factor_name = 'Class',   kernel = 'linear' )  # apply model M = model_apply(M,DE2)  # plot boundary C = svm_plot_2d(factor_name = 'Class') chart_plot(C,M, DE2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"svm-cost-function","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"SVM cost function","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"SVM cost function applies penalty samples wrong side margins. high penalty results narrow margin tries force samples correct side boundary. low penalty makes wider margin less strict samples misclassified. optimal cost use data dependent.","code":"# low cost M$cost=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium cost M$cost=0.05 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high cost M$cost=100 M=model_apply(M,DE2)  C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Low cost\", \"Medium cost\", \"High cost\"),   hjust = -1,   nrow = 2 )  legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") )  plot_grid(prow, legend, ncol=1, rel_heights = c(1, .1))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"kernel-functions","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Kernel functions","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"number different kernels can used support vector machines. structToolbox wrapper ‘linear’, ‘polynomial’,‘radial’ ‘sigmoid’ kernels can specified. Using kernels allows boundary flexible, often require additional parameters specified. best kernel use vary depending dataset, common choice radial kernel allows high flexibility single parameter.  parameters kernel can used control complexity boundary. show radial kernel parameter “gamma” can used change complexity boundary. combination cost parameter (keep constant ) allows highly flexible boundary models.  Note best practice select optimal kernel parameter(s) combination cost parameter (e.g. 2d grid search) best combination identified.","code":"# set a fixed cost for this comparison M$cost=1  # linear kernel M$kernel='linear' M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # polynomial kernel M$kernel='polynomial' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # rbf kernel M$kernel='radial' M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # sigmoid kernel M$kernel='sigmoid' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g4=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   g4 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Linear\", \"Polynomial\", \"Radial\",\"Sigmoid\"),   hjust = -1,   nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +     theme(legend.position = \"bottom\") ) plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1)) # rbf kernel and cost M$kernel = 'radial' M$cost = 1  # low gamma M$gamma=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium gamma M$gamma=0.1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high gamma M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(  g1 + theme(legend.position=\"none\"),  g2 + theme(legend.position=\"none\"),  g3 + theme(legend.position=\"none\"),  align = 'vh',  labels = c(\"Low gamma\", \"Medium gamma\", \"High gamma\"),  hjust = -1,  nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") ) plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1))"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-6","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette conduct data preprocessing exploratory analysis data STATegra project (https://www.nature.com/articles/s41597-019-0202-7). demonstration purposes focus Proteomics Metabolomics datasets publicly available part STATegra multi-omics dataset. …STATegra multi-omics dataset combines measurements 10 different omics technologies applied biological system, namely well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data. Gomez-Cabrero et al STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-proteomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based proteomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based proteomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox. number Reporter genes included study. plot two illustrate trends data.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Proteomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(path, files = \"Proteomics_01_uniprot_canonical_normalized.txt\", exdir=temp) # read samples all_data <-  read.delim(file.path(temp,\"Proteomics_01_uniprot_canonical_normalized.txt\"), as.is = TRUE, header = TRUE, sep = \"\\t\") # extract data matrix data = all_data[1:2527,51:86] # shorten sample names colnames(data) = lapply(colnames(data), function (x) substr(x, 27, nchar(x))) # replace 0 with NA data[data == 0] <- NA # transpose data=as.data.frame(t(data))  # prepare sample meta SM = lapply(rownames(data),function(x) {   s=strsplit(x,'_')[[1]] # split at underscore   out=data.frame(     'treatment' = s[[1]],     'time' = substr(s[[2]],1,nchar(s[[2]])-1) ,         'batch' = substr(s[[3]],6,nchar(s[[3]])),     'condition' = substr(x,1,6) # interaction between treatment and time   )   return(out) }) SM = do.call(rbind,SM) rownames(SM)=rownames(data) # convert to factors SM$treatment=factor(SM$treatment) SM$time=ordered(SM$time,c(\"0\",\"2\",\"6\",\"12\",\"18\",\"24\")) SM$batch=ordered(SM$batch,c(1,3,4,5,6,7)) SM$condition=factor(SM$condition)  # variable meta data VM = all_data[1:2527,c(1,6,7)] rownames(VM)=colnames(data)  # prepare DatasetExperiment DS = DatasetExperiment(   data = data,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Proteomics',   description = 'downloaded from: https://github.com/STATegraData/STATegraData/' ) DS ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 2527 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 2527 rows x 3 columns # find id of reporters Ldha = which(DS$variable_meta$Gene.names=='Ldha') Hk2 = which(DS$variable_meta$Gene.names=='Hk2')  # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DS)+ggtitle('Ldha')+ylab('expression') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DS)+ggtitle('Hk2')+ylab('expression')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-transformation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Data transformation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"data log2 transformed, scaled mean medians equal conditions. steps available structToolbox using log_transform mean_of_medians objects. Reporter genes plotted comparison.","code":"# prepare model sequence M = log_transform(   base=2) +    mean_of_medians(     factor_name = 'condition') # apply model sequence M = model_apply(M,DS)   # get transformed data DST = predicted(M) # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DST)+ggtitle('Ldha')+ylab('log2(expression)') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DST)+ggtitle('Hk2')+ylab('log2(expression)')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-filtering","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Missing value filtering involves removing feature (gene) least 3 missing values per group least 11 samples. specific filter structToolbox time, can achieved combining filter_na_count filter_by_name objects. Specifically, default output filter_na_count changed return matrix NA counts per class. output connected ‘names’ input filter_by_names converted TRUE/FALSE using ‘seq_fcn’ input. ‘seq_fcn’ function processes NA counts used inputs filter_by_names. data passed along model sequence passes unchanged filter_na_count object becuase default output changed, filter_na_count filter_by_name objects working together single filter.","code":"# build model sequence M2 = filter_na_count(   threshold=2,   factor_name='condition',    predicted='na_count') +    # override the default output   filter_by_name(     mode='exclude',     dimension='variable',     names='place_holder',     seq_in='names',     seq_fcn=function(x) {      # convert NA count pre group to true/false       x=x>2                  # more the two missing per group       x=rowSums(x)>10        # in more than 10 groups       return(x)     }   ) # apply to transformed data M2 = model_apply(M2,DST)  # get the filtered data DSTF = predicted(M2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-imputation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value imputation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra uses two imputation methods available struct objects, create temporary STATegra_impute objects using functions struct package. first imputation method imputes missing values treatment values missing samples using “random value discovery”. create new struct object using set_struct_obj global environment, “method_apply” method implements imputation. second imputation method replacing missing values condition exactly 1 missing value mean values condition. create new struct object corresponding method new object implement filter. new STATegra imputation objects can now used model sequences like struct object. final filter added remove feature missing values imputation.","code":"# create new imputation object set_struct_obj(   class_name = 'STATegra_impute1',   struct_obj = 'model',   params=c(factor_sd='character',factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 1',     description = 'If missing values are present for all one group then they are replaced with min/2 + \"random value below discovery\".',     predicted = 'imputed'   )  )  # create method_apply for imputation method 1 set_obj_method(   class_name='STATegra_impute1',   method_name='model_apply',   definition=function(M,D) {          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })     # count number of samples in each group     count=summary(D$sample_meta[[M$factor_name]])     # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_sd]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == matrix(count,nrow=2,ncol=ncol(D)) # all missing in one class          # impute matrix     mi = D$data     for (j in 1:nrow(mi)) {       # index of group for this sample       g = which(levels(D$sample_meta[[M$factor_name]])==D$sample_meta[[M$factor_name]][j])       iv=rnorm(ncol(D),min(D$data[j,],na.rm=TRUE)/2,sd)       mi[j,is.na(mi[j,]) & check[g,]] = iv[is.na(mi[j,]) & check[g,]]     }     D$data = mi     M$imputed=D     return(M)   } ) # create new imputation object set_struct_obj(   class_name = 'STATegra_impute2',   struct_obj = 'model',   params=c(factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 2',     description = 'For those conditions with only 1 NA impute with the mean of the condition.',     predicted = 'imputed'   )  )  # create method_apply for imputation method 2 set_obj_method(   class_name='STATegra_impute2',   method_name='model_apply',   definition=function(M,D) {     # levels in condition     L = levels(D$sample_meta[[M$factor_name]])          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })          # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_name]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == 1 # only one missing for a condition          # index of samples for each condition     IDX = list()     for (k in L) {       IDX[[k]]=which(D$sample_meta[[M$factor_name]]==k)     }          ## impute     # for each feature     for (k in 1:ncol(D)) {       # for each condition       for (j in 1:length(L)) {         # if passes test         if (check[j,k]) {           # mean of samples in group           m = mean(D$data[IDX[[j]],k],na.rm=TRUE)           # imputed value           im = rnorm(1,m,sd)           # replace NA with imputed           D$data[is.na(D$data[,k]) & D$sample_meta[[M$factor_name]]==L[j],k]=im         }       }     }     M$imputed=D     return(M)   } ) # model sequence M3 = STATegra_impute1(factor_name='treatment',factor_sd='condition') +      STATegra_impute2(factor_name = 'condition') +       filter_na_count(threshold = 3, factor_name='condition') # apply model M3 = model_apply(M3,DSTF) # get imputed data DSTFI = predicted(M3) DSTFI ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 864 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 864 rows x 3 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"often useful visualise distribution values across samples verify transformations/normalisation/filtering etc effective.  values longer skewed show approximately normal distribution. boxplots comparable width outliers indicated, transformations etc overall positive effect. PCA used provide graphical representation data. comparison outputs STATegra filter included reduce data include treated samples (IKA)  appear strong batch effect. PC1 dominated time point “24” potentially outlying points time points “2” “0”.","code":"# model sequence P = filter_smeta(mode='include',factor_name='treatment',levels='IKA') +     mean_centre() +      PCA(number_components = 2) # apply model P = model_apply(P,DSTFI)  # scores plots coloured by factors g = list() for (k in c('batch','time')) {   C = pca_scores_plot(factor_name=k,ellipse='none')   g[[k]]=chart_plot(C,P[3]) }  plot_grid(plotlist = g,nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-metabolomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based metabolomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based metabolomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Metabolomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(zipfile=path, files = \"LC_MS_raw_data.xlsx\", exdir=temp)  # read samples data <- as.data.frame(read.xlsx(file.path(temp,\"LC_MS_raw_data.xlsx\"),sheet = 'Data')) # extract sample meta data SM = data[ ,1:8]  # add coloumn for sample type (QC, blank etc) blanks=c(1,2,33,34,65,66) QCs=c(3,4,11,18,25,32,35,36,43,50,57,64) SM$sample_type='Sample' SM$sample_type[blanks]='Blank' SM$sample_type[QCs]='QC'  # put qc/blank labels in all factors for plotting later SM$biol.batch[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$time.point[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$condition[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample']  # convert to factors SM$biol.batch=ordered(SM$biol.batch,c('9','10','11','12','QC','Blank')) SM$time.point=ordered(SM$time.point,c('0h','2h','6h','12h','18h','24h','QC','Blank')) SM$condition=factor(SM$condition) SM$sample_type=factor(SM$sample_type)  # variable meta data VM = data.frame('annotation'=colnames(data)[9:ncol(data)])  # raw data X = data[,9:ncol(data)] # convert 0 to NA X[X==0]=NA # force to numeric; any non-numerics will become NA X=data.frame(lapply(X,as.numeric),check.names = FALSE)  # make sure row/col names match rownames(X)=data$label rownames(SM)=data$label rownames(VM)=colnames(X)   # create DatasetExperiment object DE = DatasetExperiment(   data = X,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Metabolomics LCMS',   description = 'https://www.nature.com/articles/s41597-019-0202-7' )  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Metabolomics LCMS ## description:   https://www.nature.com/articles/s41597-019-0202-7 ## data:          66 rows x 152 columns ## sample_meta:   66 rows x 9 columns ## variable_meta: 152 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra project LCMS data combined GCMS data multiblock analysis conducted. LCMS explored, data processed differently comparison Gomez-Cabrero et al. basic processing steps applied order generate valid PCA plot biological QC samples.","code":"# prepare model sequence MS = filter_smeta(mode = 'include', levels='QC', factor_name = 'sample_type') +      knn_impute(neighbours=5) +      vec_norm() +       log_transform(base = 10)   # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 3 rows with more than 50 % entries missing; ##  mean imputation used for these rows"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-2","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"First use PCA look QC samples order make assessment data quality.  QC labelled “36” clearly different QCs. STATegra QC removed, exclude well. corresponds QC H1. STATegra also excluded QC samples measured immediately blank, also .  Now plot QC samples context samples. several possible approaches, apply approach applying PCA full dataset including QCs. exclude blanks likely dominate plot removed. samples batch 12 excluded STATegra replicate . QCs appear representative samples, strong clusters data, including QC samples biological variation. likely number ‘low quality’ features excluded, now, use sophisticated normalisation (PQN) scaling methods (glog).  can see now QCs tightly clustered. indicates biological variance remaining high quality features much greater technical variance represented QCs. appear trend measurement order (), important indicator instrument drift throughout run large source variation dataset. appear strong clustering related biological batch (B). appear strong trend time (C) likely subtle variation might masked sources variance stage. clustering related condition (D) overlap. explore trends time, split data condition factor explore Ikaros group. Removing condition factor variation potentially make easier spot subtle trends. extract glog transformed matrix previous model sequence continue .  Colouring groups () makes time point trend difficult see, adding ggplot continuous colour scale “viridis” (B) trend time along PC1 becomes much clearer.","code":"# pca model sequence M =  mean_centre() +      PCA(number_components = 3)  # apply model M = model_apply(M,predicted(MS))   # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,M[2]) # prepare model sequence MS = filter_smeta(       mode = 'include',        levels='QC',        factor_name = 'sample_type') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1','1358BZU_0001QC_A1','1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 4 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,MS[7]) # prepare model sequence MS = filter_smeta(       mode = 'exclude',        levels='Blank',        factor_name = 'sample_type') +         filter_smeta(       mode = 'exclude',        levels='12',        factor_name = 'biol.batch') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1',                 '1358BZU_0001QC_A1',                 '1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 2 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plots C = pca_scores_plot(factor_name = 'sample_type') # plot chart_plot(C,MS[8]) MS =  filter_smeta(        mode = 'exclude',         levels = '12',         factor_name = 'biol.batch') +          filter_by_name(        mode = 'exclude',         dimension='sample',        names = c('1358BZU_0001QC_H1',                  '1358BZU_0001QC_A1',                  '1358BZU_0001QC_G1')) +        blank_filter(        fold_change = 20,        qc_label = 'QC',        factor_name = 'sample_type') +        filter_smeta(        mode='exclude',        levels='Blank',        factor_name='sample_type') +          mv_feature_filter(        threshold = 80,         qc_label = 'QC',         factor_name = 'sample_type',         method = 'QC') +             mv_feature_filter(         threshold = 50,          factor_name = 'sample_type',          method='across') +         rsd_filter(        rsd_threshold=20,         qc_label='QC',        factor_name='sample_type') +         mv_sample_filter(        mv_threshold = 50) +            pqn_norm(        qc_label='QC',        factor_name='sample_type') +            knn_impute(        neighbours=5,         by='samples') +            glog_transform(        qc_label = 'QC',        factor_name = 'sample_type') +            mean_centre() +             PCA(        number_components = 10)  # apply model sequence MS = model_apply(MS, DE)   # PCA plots using different factors g=list() for (k in c('order','biol.batch','time.point','condition')) {   C = pca_scores_plot(factor_name = k,ellipse='none')   # plot   g[[k]]=chart_plot(C,MS[length(MS)]) }  plot_grid(plotlist = g,align='vh',axis='tblr',nrow=2,labels=c('A','B','C','D')) # get the glog scaled data GL = predicted(MS[11])  # extract the Ikaros group and apply PCA IK = filter_smeta(       mode='include',       factor_name='condition',       levels='Ikaros') +      mean_centre() +       PCA(number_components = 5)  # apply the model sequence to glog transformed data IK = model_apply(IK,GL)  # plot the PCA scores C = pca_scores_plot(factor_name='time.point',ellipse = 'sample') g1=chart_plot(C,IK[3]) g2=g1 + scale_color_viridis_d() # add continuous scale colouring  plot_grid(g1,g2,nrow=2,align='vh',axis = 'tblr',labels=c('A','B'))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"","code":"sessionInfo() ## R version 4.3.2 (2023-10-31) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 22.04.3 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        ##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    ##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           ## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    ##  ## time zone: UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats     graphics  grDevices utils     datasets  methods   base      ##  ## other attached packages: ##  [1] openxlsx_4.2.5.2     cowplot_1.1.3        gridExtra_2.3        ##  [4] ggplot2_3.4.4        BiocFileCache_2.10.1 dbplyr_2.4.0         ##  [7] ropls_1.34.0         pmp_1.14.0           structToolbox_1.15.1 ## [10] struct_1.14.0        BiocStyle_2.30.0     ##  ## loaded via a namespace (and not attached): ##   [1] DBI_1.2.1                   bitops_1.0-7                ##   [3] MultiDataSet_1.30.0         rlang_1.1.3                 ##   [5] magrittr_2.0.3              e1071_1.7-14                ##   [7] matrixStats_1.2.0           compiler_4.3.2              ##   [9] RSQLite_2.3.5               systemfonts_1.0.5           ##  [11] vctrs_0.6.5                 reshape2_1.4.4              ##  [13] stringr_1.5.1               pkgconfig_2.0.3             ##  [15] crayon_1.5.2                fastmap_1.1.1               ##  [17] XVector_0.42.0              labeling_0.4.3              ##  [19] utf8_1.2.4                  rmarkdown_2.25              ##  [21] itertools_0.1-3             ragg_1.2.7                  ##  [23] bit_4.0.5                   purrr_1.0.2                 ##  [25] xfun_0.41                   MultiAssayExperiment_1.28.0 ##  [27] randomForest_4.7-1.1        zlibbioc_1.48.0             ##  [29] cachem_1.0.8                GenomeInfoDb_1.38.5         ##  [31] jsonlite_1.8.8              progress_1.2.3              ##  [33] blob_1.2.4                  highr_0.10                  ##  [35] DelayedArray_0.28.0         prettyunits_1.2.0           ##  [37] parallel_4.3.2              rols_2.30.0                 ##  [39] R6_2.5.1                    bslib_0.6.1                 ##  [41] stringi_1.8.3               limma_3.58.1                ##  [43] GenomicRanges_1.54.1        jquerylib_0.1.4             ##  [45] Rcpp_1.0.12                 bookdown_0.37               ##  [47] SummarizedExperiment_1.32.0 iterators_1.0.14            ##  [49] knitr_1.45                  IRanges_2.36.0              ##  [51] Matrix_1.6-1.1              tidyselect_1.2.0            ##  [53] abind_1.4-5                 yaml_2.3.8                  ##  [55] codetools_0.2-19            curl_5.2.0                  ##  [57] doRNG_1.8.6                 lattice_0.21-9              ##  [59] tibble_3.2.1                plyr_1.8.9                  ##  [61] withr_3.0.0                 Biobase_2.62.0              ##  [63] evaluate_0.23               ontologyIndex_2.11          ##  [65] desc_1.4.3                  isoband_0.2.7               ##  [67] proxy_0.4-27                zip_2.3.1                   ##  [69] filelock_1.0.3              pillar_1.9.0                ##  [71] BiocManager_1.30.22         MatrixGenerics_1.14.0       ##  [73] rngtools_1.5.2              foreach_1.5.2               ##  [75] stats4_4.3.2                generics_0.1.3              ##  [77] sp_2.1-2                    RCurl_1.98-1.14             ##  [79] hms_1.1.3                   S4Vectors_0.40.2            ##  [81] munsell_0.5.0               scales_1.3.0                ##  [83] calibrate_1.7.7             class_7.3-22                ##  [85] glue_1.7.0                  tools_4.3.2                 ##  [87] fs_1.6.3                    grid_4.3.2                  ##  [89] impute_1.76.0               missForest_1.5              ##  [91] colorspace_2.1-0            GenomeInfoDbData_1.2.11     ##  [93] cli_3.6.2                   textshaping_0.3.7           ##  [95] fansi_1.0.6                 viridisLite_0.4.2           ##  [97] ggthemes_5.0.0              S4Arrays_1.2.0              ##  [99] dplyr_1.1.4                 pls_2.8-3                   ## [101] pcaMethods_1.94.0           gtable_0.3.4                ## [103] sass_0.4.8                  digest_0.6.34               ## [105] BiocGenerics_0.48.1         SparseArray_1.2.3           ## [107] farver_2.1.1                memoise_2.0.1               ## [109] htmltools_0.5.7             pkgdown_2.0.7               ## [111] lifecycle_1.0.4             httr_1.4.7                  ## [113] statmod_1.5.0               bit64_4.0.5                 ## [115] qqman_0.1.9                 MASS_7.3-60"},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gavin Rhys Lloyd. Author, maintainer. Ralf Johannes Maria Weber. Author.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lloyd, G., Jankevics, ., Weber, R. 2020. struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond. Bioinformatics, 36(22-23), p.5551-5552.","code":"@Article{,   title = {struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond},   author = {Gavin Rhys Lloyd and Andris Jankevics and Ralf Johannes Maria Weber},   journal = {Bioinformatics},   year = {2020},   volume = {36},   number = {22-23},   pages = {5551-5552},   url = {https://doi.org/10.1093/bioinformatics/btaa1031}, }"},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"structtoolbox","dir":"","previous_headings":"","what":"Data processing & analysis tools for Metabolomics and other omics","title":"Data processing & analysis tools for Metabolomics and other omics","text":"extensive set data (pre-)processing analysis methods tools metabolomics omics, strong emphasis statistics machine learning. toolbox allows user build extensive standardised workflows data analysis. methods tools implemented using class-based templates provided struct (Statistics R Using Class-based Templates) package. toolbox includes pre-processing methods (e.g. signal drift batch correction, normalisation, missing value imputation scaling), univariate (e.g. ttest, various forms ANOVA, Kruskal–Wallis test ) multivariate statistical methods (e.g. PCA PLS, including cross-validation permutation testing) well machine learning methods (e.g. Support Vector Machines). STATistics Ontology (STATO) integrated implemented provide standardised definitions different methods, inputs outputs.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Data processing & analysis tools for Metabolomics and other omics","text":"install package: install development version:","code":"if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"structToolbox\") if (!require(\"remotes\", quietly = TRUE))     install.packages(\"remotes\")  remotes::install_github(\"computational-metabolomics/structToolbox\")"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis of Variance — ANOVA","title":"Analysis of Variance — ANOVA","text":"Analysis Variance (ANOVA) univariate method used analyse difference among group means. Multiple test corrected p-values computed indicate significance feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis of Variance — ANOVA","text":"","code":"ANOVA(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"III\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis of Variance — ANOVA","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) ANOVA sum squares. Allowed values limited following: \"\": Type sum squares. \"II\": Type II sum squares. \"III\": Type III sum squares. default \"III\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis of Variance — ANOVA","text":" ANOVA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analysis of Variance — ANOVA","text":"object makes use functionality following packages: car","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Analysis of Variance — ANOVA","text":"ANOVA object inherits following struct classes: ANOVA() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Analysis of Variance — ANOVA","text":"Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analysis of Variance — ANOVA","text":"","code":"M = ANOVA(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       ss_type = \"III\")  D = iris_DatasetExperiment() M = ANOVA(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":null,"dir":"Reference","previous_headings":"","what":"Area under ROC curve — AUC","title":"Area under ROC curve — AUC","text":"area ROC curve classifier estimated using trapezoid method.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area under ROC curve — AUC","text":"","code":"AUC(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Area under ROC curve — AUC","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Area under ROC curve — AUC","text":"  AUC  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Area under ROC curve — AUC","text":"AUC object inherits following struct classes: AUC() ⭢ metric() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Area under ROC curve — AUC","text":"","code":"M = AUC()  D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = AUC() XCV = run(XCV,D,MET) #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":null,"dir":"Reference","previous_headings":"","what":"Discriminant Factor Analysis — DFA","title":"Discriminant Factor Analysis — DFA","text":"Discriminant Factor Analysis (DFA) supervised classification method. Using linear combination input variables, DFA finds new orthogonal axes (canonical values) minimize variance within given class maximize variance classes.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discriminant Factor Analysis — DFA","text":"","code":"DFA(factor_name, number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discriminant Factor Analysis — DFA","text":"factor_name (character) name sample-meta column use. number_components (numeric, integer) number DFA components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discriminant Factor Analysis — DFA","text":" DFA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Discriminant Factor Analysis — DFA","text":"DFA object inherits following struct classes: DFA() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Discriminant Factor Analysis — DFA","text":"Manly B (1986). Multivariate Statistical Methods: Primer. Chapman Hall, Boca Raton.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discriminant Factor Analysis — DFA","text":"","code":"M = DFA(       factor_name = \"V1\",       number_components = 2)  D = iris_DatasetExperiment() M = DFA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_boxplot","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"boxplot visualise distribution values within subset features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"DatasetExperiment_boxplot(   factor_name,   by_sample = TRUE,   per_class = TRUE,   number = 50,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"factor_name (character) name sample-meta column use. by_sample (logical) Plot sample. Allowed values limited following: \"TRUE\": data plotted across features subset samples. \"FALSE\": data plotted across samples subset features. default TRUE. per_class (logical) Plot per class. Allowed values limited following: \"TRUE\": data plotted class. \"FALSE\": data plotted samples. default TRUE. number (numeric, integer) number features/samples plotted. default 50. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"  DatasetExperiment_boxplot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"DatasetExperiment_boxplot object inherits following struct classes: DatasetExperiment_boxplot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"M = DatasetExperiment_boxplot(       factor_name = \"V1\",       by_sample = FALSE,       per_class = FALSE,       number = 50)  D = MTBLS79_DatasetExperiment() C = DatasetExperiment_boxplot(factor_name='Class',number=10,per_class=FALSE) chart_plot(C,D) #> Warning: Removed 585 rows containing non-finite values (`stat_boxplot()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_dist","title":"Feature distribution histogram — DatasetExperiment_dist","text":"histogram visualise distribution values within features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"DatasetExperiment_dist(factor_name, per_class = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_dist","text":"factor_name (character) name sample-meta column use. per_class (logical) Plot per class. Allowed values limited following: \"TRUE\": distributions plotted class. \"FALSE\": distribution plotted samples. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_dist","text":"  DatasetExperiment_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature distribution histogram — DatasetExperiment_dist","text":"DatasetExperiment_dist object inherits following struct classes: DatasetExperiment_dist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"M = DatasetExperiment_dist(       factor_name = \"V1\",       per_class = FALSE)  D = MTBLS79_DatasetExperiment() C = DatasetExperiment_dist(factor_name='Class') chart_plot(C,D) #> Warning: Removed 9222 rows containing non-finite values (`stat_bin()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Factor boxplot — DatasetExperiment_factor_boxplot","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"Boxplot feature visualise distribution values within group","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"DatasetExperiment_factor_boxplot(feature_to_plot, factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"feature_to_plot (character, numeric, integer) name plotted feature. factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"  DatasetExperiment_factor_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"DatasetExperiment_factor_boxplot object inherits following struct classes: DatasetExperiment_factor_boxplot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"M = DatasetExperiment_factor_boxplot(       factor_names = \"V1\",       feature_to_plot = \"V1\")  D = iris_DatasetExperiment() C = DatasetExperiment_factor_boxplot(factor_names='Species',feature_to_plot='Petal.Width') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"DatasetExperiment heatmap — DatasetExperiment_heatmap","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"heatmap visualise measured values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"DatasetExperiment_heatmap(na_colour = \"#FF00E4\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"na_colour (character) hex colour code used plot missing values. default \"#FF00E4\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"  DatasetExperiment_heatmap  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"object makes use functionality following packages: reshape2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"DatasetExperiment_heatmap object inherits following struct classes: DatasetExperiment_heatmap() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"M = DatasetExperiment_heatmap(       na_colour = \"#FF00E4\")  D = iris_DatasetExperiment() C = DatasetExperiment_heatmap() chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Cluster Analysis — HCA","title":"Hierarchical Cluster Analysis — HCA","text":"Hierarchical Cluster Analysis numerical technique uses agglomerative clustering identify clusters groupings samples.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"HCA(   dist_method = \"euclidean\",   cluster_method = \"complete\",   minkowski_power = 2,   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Cluster Analysis — HCA","text":"dist_method (character) Distance measure. Allowed values limited following: \"euclidean\": euclidean distance (2 norm). \"maximum\": maximum distance. \"manhattan\": absolute distance (1 norm). \"canberra\": weighted version mahattan distance. \"minkowski\": generalisation manhattan euclidean distance nth norm. default \"euclidean\". cluster_method (character) Agglomeration method. Allowed values limited following: \"ward.D\": Ward clustering. \"ward.D2\": Ward clustering using sqaured distances. \"single\": Single linkage. \"complete\": Complete linkage. \"average\": Average linkage (UPGMA). \"mcquitty\": McQuitty linkage (WPGMA). \"median\": Median linkage (WPGMC). \"centroid\": Centroid linkage (UPGMC). default \"complete\". minkowski_power (numeric)  default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Cluster Analysis — HCA","text":" HCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical Cluster Analysis — HCA","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Hierarchical Cluster Analysis — HCA","text":"HCA object inherits following struct classes: HCA() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hierarchical Cluster Analysis — HCA","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"M = HCA(       dist_method = \"euclidean\",       cluster_method = \"complete\",       minkowski_power = numeric(0),       factor_name = \"V1\")  D = iris_DatasetExperiment() M = HCA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference — HSD","title":"Tukey's Honest Significant Difference — HSD","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"HSD(alpha = 0.05, mtc = \"fdr\", formula, unbalanced = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference — HSD","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. unbalanced (logical) Unbalanced model. Allowed values limited following: \"TRUE\": correction applied unbalanced designs. \"FALSE\": correction applied unbalanced designs. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference — HSD","text":" HSD object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference — HSD","text":"object makes use functionality following packages: agricolae","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Tukey's Honest Significant Difference — HSD","text":"HSD object inherits following struct classes: HSD() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference — HSD","text":"de Mendiburu F (2023). agricolae: Statistical Procedures Agricultural Research. R package version 1.3-7, https://CRAN.R-project.org/package=agricolae.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"M = HSD(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       unbalanced = FALSE)  D = iris_DatasetExperiment() M = HSD(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature. mixed effects models estimated marginal means used.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"HSDEM(alpha = 0.05, mtc = \"fdr\", formula, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":" HSDEM object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"object makes use functionality following packages: emmeans nlme","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"HSDEM object inherits following struct classes: HSDEM() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Lenth R (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.9.0, https://CRAN.R-project.org/package=emmeans. Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-164, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"M = HSDEM(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x)  D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = HSDEM(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"Direct-infusion mass spectrometry (DIMS) metabolomics important approach characterising molecular responses organisms disease, drugs environment. Increasingly large-scale metabolomics studies conducted, necessitating improvements bioanalytical computational workflows maintain data quality. dataset represents systematic evaluation reproducibility multi-batch DIMS metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"MTBLS79_DatasetExperiment(filtered = FALSE)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"filtered TRUE load data quality control filters already applied, FALSE load unfiltered data. Default FALSE. raw data available (https://www.ebi.ac.uk/metabolights/MTBLS79) R dataset pmp package, available Bioconductor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"D = MTBLS79_DatasetExperiment() summary(D) #> [1] \"DatasetExperiment object of length 172 with 7 metadata columns\""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSDA","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"OPLSDA(number_components = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"number_components (numeric, integer) number orthgonal components. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":" OPLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"OPLSDA object inherits following struct classes: OPLSDA() ⭢ OPLSR() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"M = OPLSDA(       number_components = 2,       factor_name = \"V1\")  M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSR","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"OPLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"number_components (numeric, integer) number orthgonal components. default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSR","text":" OPLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"OPLSR object inherits following struct classes: OPLSR() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"M = OPLSR(       number_components = 2,       factor_name = \"V1\")  M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (PCA) — PCA","title":"Principal Component Analysis (PCA) — PCA","text":"PCA multivariate data reduction technique. summarises data smaller number Principal Components maximise variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"PCA(number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (PCA) — PCA","text":"number_components (numeric, integer) number Principal Components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (PCA) — PCA","text":" PCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Principal Component Analysis (PCA) — PCA","text":"PCA object inherits following struct classes: PCA() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"M = PCA(       number_components = 2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares discriminant analysis — PLSDA","title":"Partial least squares discriminant analysis — PLSDA","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. Discriminant Analysis variant uses group labels response variable. >2 groups 1-vs-approach used. Group membership can predicted test samples based probability estimate group membership, estimated y-value.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"PLSDA(number_components = 2, factor_name, pred_method = \"max_prob\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares discriminant analysis — PLSDA","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample-meta column use. pred_method (character) Prediction method. Allowed values limited following: \"max_yhat\": predicted group selected based largest value y_hat. \"max_prob\": predicted group selected based largest probability group membership. default \"max_prob\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares discriminant analysis — PLSDA","text":" PLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares discriminant analysis — PLSDA","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Partial least squares discriminant analysis — PLSDA","text":"PLSDA object inherits following struct classes: PLSDA() ⭢ PLSR() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares discriminant analysis — PLSDA","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Perez NF, Ferre J, Boque R (2009). \"Calculation reliability classification discriminant partial least-squares binary classification.\" Chemometrics Intelligent Laboratory Systems, 95(2), 122-128. Barker M, Rayens W (2003). \"Partial least squares discrimination.\" Journal Chemometrics, 17(3), 166-173.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"M = PLSDA(       number_components = 2,       factor_name = \"V1\",       pred_method = \"max_prob\")  M = PLSDA('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares regression — PLSR","title":"Partial least squares regression — PLSR","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. regression response continuous variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares regression — PLSR","text":"","code":"PLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares regression — PLSR","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares regression — PLSR","text":" PLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares regression — PLSR","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Partial least squares regression — PLSR","text":"PLSR object inherits following struct classes: PLSR() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares regression — PLSR","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares regression — PLSR","text":"","code":"M = PLSR(       number_components = 2,       factor_name = \"V1\")  M = PLSR(factor_name='run_order')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":null,"dir":"Reference","previous_headings":"","what":"Support Vector Machine Classifier — SVM","title":"Support Vector Machine Classifier — SVM","text":"Support Vector Machines (SVM) machine learning algorithm classification. can make use kernel functions generate highly non-linear boundaries groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Support Vector Machine Classifier — SVM","text":"","code":"SVM(   factor_name,   kernel = \"linear\",   degree = 3,   gamma = 1,   coef0 = 0,   cost = 1,   class_weights = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Support Vector Machine Classifier — SVM","text":"factor_name (character) name sample-meta column use. kernel (character) Kernel type. Allowed values limited following: \"linear\": . \"polynomial\": . \"radial\": . \"sigmoid\": . default \"linear\". degree (numeric) polynomial degree. default 3. gamma (numeric) gamma parameter. default 1. coef0 (numeric) offset coefficient. default 0. cost (numeric) cost violating constraints. default 1. class_weights (numeric, character, NULL) named vector weights different classes.  Specifying \"inverse\" choose weights inversely proportional class distribution. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Support Vector Machine Classifier — SVM","text":" SVM object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Support Vector Machine Classifier — SVM","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Support Vector Machine Classifier — SVM","text":"SVM object inherits following struct classes: SVM() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Support Vector Machine Classifier — SVM","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, https://CRAN.R-project.org/package=e1071. Brereton RG, Lloyd GR (2010). \"Support Vector Machines classification regression.\" Analyst, 135(2), 230-267.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Support Vector Machine Classifier — SVM","text":"","code":"M = SVM(       factor_name = \"V1\",       kernel = \"linear\",       degree = 3,       gamma = 1,       coef0 = 0,       cost = 1,       class_weights = 1)  M = SVM(factor_name='Species',gamma=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to data.frame — as_data_frame","title":"Convert to data.frame — as_data_frame","text":"Convert outputs input model data.frame.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to data.frame — as_data_frame","text":"","code":"# S4 method for filter_na_count as_data_frame(M)  # S4 method for ttest as_data_frame(M)  # S4 method for wilcox_test as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to data.frame — as_data_frame","text":"M model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to data.frame — as_data_frame","text":"data.frame model outputs","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to data.frame — as_data_frame","text":"","code":"D = iris_DatasetExperiment() M = filter_na_count(threshold=50,factor_name='Species') M= model_apply(M,D) df = as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Autoscaling — autoscale","title":"Autoscaling — autoscale","text":"variable/feature mean centred scaled standard deviation. transformed variables zero-mean unit-variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autoscaling — autoscale","text":"","code":"autoscale(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autoscaling — autoscale","text":"mode (character) Mode action. Allowed values limited following: \"data\": Autoscaling applied data matrix . \"sample_meta\": Autoscaling applied sample_meta data . \"\": Autoscaling applied data matrix meta data. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autoscaling — autoscale","text":" autoscale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Autoscaling — autoscale","text":"autoscale object inherits following struct classes: autoscale() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autoscaling — autoscale","text":"","code":"M = autoscale(       mode = \"data\")  D = iris_DatasetExperiment() M = autoscale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Balanced Accuracy — balanced_accuracy","title":"Balanced Accuracy — balanced_accuracy","text":"Balanced Accuracy average proportion correctly classified samples across groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"balanced_accuracy(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Balanced Accuracy — balanced_accuracy","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Balanced Accuracy — balanced_accuracy","text":"  balanced_accuracy  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Balanced Accuracy — balanced_accuracy","text":"balanced_accuracy object inherits following struct classes: balanced_accuracy() ⭢ metric() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"M = balanced_accuracy()  D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = balanced_accuracy() XCV = run(XCV,D,MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Blank filter — blank_filter","title":"Blank filter — blank_filter","text":"blank filter filters features comparing median intensity blank samples median intensity samples. Features relative intensity (fold change) large compared blank removed. number times feature detected across blank samples may also considered. feature detected high enough proportion blanks removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blank filter — blank_filter","text":"","code":"blank_filter(   fold_change = 20,   blank_label = \"blank\",   qc_label = \"QC\",   factor_name,   fraction_in_blank = 0,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blank filter — blank_filter","text":"fold_change (numeric) Features fold change less value removed. default 20. blank_label (character) label used identify blank samples. default \"blank\". qc_label (character, NULL) label used identify QC samples. set NULL median samples used. default \"QC\". factor_name (character) name sample-meta column use. fraction_in_blank (numeric) Features present less proportion blanks considered removal. default 0. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Blank filter — blank_filter","text":" blank_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Blank filter — blank_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Blank filter — blank_filter","text":"blank_filter object inherits following struct classes: blank_filter() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Blank filter — blank_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Blank filter — blank_filter","text":"","code":"M = blank_filter(       fold_change = 20,       blank_label = \"Blank\",       qc_label = \"QC\",       factor_name = \"V1\",       fraction_in_blank = 0)  D = iris_DatasetExperiment() M = blank_filter(fold_change=2,                  factor_name='Species',                  blank_label='setosa',                  qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of blank filter fold changes — blank_filter_hist","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"histogram calculated fold changes blank filter (median samples divided median blanks)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"blank_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"  blank_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"blank_filter_hist object inherits following struct classes: blank_filter_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"M = blank_filter_hist()  C = blank_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap resampling — bootstrap","title":"Bootstrap resampling — bootstrap","text":"bootstrap resampling subset samples selected random replacement form training set. sample selected training included test set. process repeated many times, performance metrics computed repetition.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap resampling — bootstrap","text":"","code":"bootstrap(number_of_repetitions = 100, collect, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap resampling — bootstrap","text":"number_of_repetitions (numeric, integer) number bootstrap repetitions. default 100. collect (character) name model output collect bootstrap repetitions, addition input metric. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap resampling — bootstrap","text":" bootstrap object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Bootstrap resampling — bootstrap","text":"bootstrap object inherits following struct classes: bootstrap() ⭢ resampler() ⭢ iterator() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap resampling — bootstrap","text":"","code":"M = bootstrap(       number_of_repetitions = 10,       collect = \"vip\")  I = bootstrap(number_of_repetitions = 10, collect = 'vip')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate metric — calculate,AUC-method","title":"Calculate metric — calculate,AUC-method","text":"Calculate metric","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate metric — calculate,AUC-method","text":"","code":"# S4 method for AUC calculate(obj, Y, Yhat)  # S4 method for balanced_accuracy calculate(obj, Y, Yhat)  # S4 method for r_squared calculate(obj, Y, Yhat)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate metric — calculate,AUC-method","text":"obj metric object Y true values/group labels Yhat predicted values/group labels","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate metric — calculate,AUC-method","text":"modified metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate metric — calculate,AUC-method","text":"","code":"MET = metric() calculate(MET) #> Warning: no calculation provided for this metric #> A \"metric\" object #> ----------------- #> name:           #> description:    #> value:          #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"Plots chart object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"# S4 method for dfa_scores_plot,DFA chart_plot(obj, dobj)  # S4 method for scatter_chart,DatasetExperiment chart_plot(obj, dobj)  # S4 method for pca_correlation_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_scores_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_biplot,PCA chart_plot(obj, dobj)  # S4 method for pca_loadings_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_scree_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_dstat_plot,PCA chart_plot(obj, dobj)  # S4 method for plsr_prediction_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsr_residual_hist,PLSR chart_plot(obj, dobj)  # S4 method for plsr_qq_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsr_cook_dist,PLSR chart_plot(obj, dobj)  # S4 method for pls_scores_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsda_predicted_plot,PLSDA chart_plot(obj, dobj)  # S4 method for plsda_roc_plot,PLSDA chart_plot(obj, dobj)  # S4 method for pls_vip_plot,PLSR chart_plot(obj, dobj)  # S4 method for pls_regcoeff_plot,PLSR chart_plot(obj, dobj)  # S4 method for blank_filter_hist,blank_filter chart_plot(obj, dobj)  # S4 method for confounders_lsq_barchart,confounders_clsq chart_plot(obj, dobj)  # S4 method for confounders_lsq_boxplot,confounders_clsq chart_plot(obj, dobj)  # S4 method for feature_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for mv_histogram,DatasetExperiment chart_plot(obj, dobj)  # S4 method for mv_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_dist,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for compare_dist,DatasetExperiment chart_plot(obj, dobj, eobj)  # S4 method for DatasetExperiment_heatmap,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_factor_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for feature_profile_array,DatasetExperiment chart_plot(obj, dobj)  # S4 method for feature_profile,DatasetExperiment chart_plot(obj, dobj)  # S4 method for fold_change_plot,fold_change chart_plot(obj, dobj)  # S4 method for fs_line,forward_selection_by_rank chart_plot(obj, dobj)  # S4 method for glog_opt_plot,glog_transform chart_plot(obj, dobj, gobj)  # S4 method for gs_line,grid_search_1d chart_plot(obj, dobj)  # S4 method for hca_dendrogram,HCA chart_plot(obj, dobj)  # S4 method for kfoldxcv_grid,kfold_xval chart_plot(obj, dobj)  # S4 method for kfoldxcv_metric,kfold_xval chart_plot(obj, dobj)  # S4 method for kw_p_hist,kw_rank_sum chart_plot(obj, dobj)  # S4 method for mv_feature_filter_hist,mv_feature_filter chart_plot(obj, dobj)  # S4 method for mv_sample_filter_hist,mv_sample_filter chart_plot(obj, dobj)  # S4 method for permutation_test_plot,permutation_test chart_plot(obj, dobj)  # S4 method for plsda_feature_importance_plot,PLSDA chart_plot(obj, dobj)  # S4 method for pqn_norm_hist,pqn_norm chart_plot(obj, dobj)  # S4 method for resample_chart,resample chart_plot(obj, dobj)  # S4 method for rsd_filter_hist,rsd_filter chart_plot(obj, dobj)  # S4 method for feature_profile,sb_corr chart_plot(obj, dobj, gobj)  # S4 method for svm_plot_2d,SVM chart_plot(obj, dobj, gobj)  # S4 method for tSNE_scatter,tSNE chart_plot(obj, dobj)  # S4 method for tic_chart,DatasetExperiment chart_plot(obj, dobj)  # S4 method for wilcox_p_hist,wilcox_test chart_plot(obj, dobj)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"obj chart object dobj struct object eobj second DatasetExperiment object compare first gobj DatasetExperiment object signal correction applied.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"plot object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"C = example_chart() chart_plot(C,iris_DatasetExperiment()) #> Warning: no chart defined for \"DatasetExperiment\" #> A \"example_chart\" object #> ------------------------ #> name:           #> description:"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Univariate Classical Least Squares Regression — classical_lsq","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"univariate classical least squares regression line fitted feature/variable response variable. fitted line minimises sum squared differences true response predicted response. coefficients (offset, gradient) fit can tested significance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"classical_lsq(alpha = 0.05, mtc = \"fdr\", factor_names, intercept = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character, list) column names regress . character vector list used ofr features. list character vectors provided assumed different set columns feature. intercept (logical) Model intercept. Allowed values limited following: \"TRUE\": intercept term included model. \"FALSE\": intercept term included model. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Univariate Classical Least Squares Regression — classical_lsq","text":" classical_lsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"classical_lsq object inherits following struct classes: classical_lsq() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"M = classical_lsq(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       intercept = FALSE)  D = iris_DatasetExperiment() M = classical_lsq(factor_names = 'Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare distributions — compare_dist","title":"Compare distributions — compare_dist","text":"Histograms boxplots computed across samples features used visually compare two datasets e.g. filtering /normalisation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare distributions — compare_dist","text":"","code":"compare_dist(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare distributions — compare_dist","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare distributions — compare_dist","text":"  compare_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Compare distributions — compare_dist","text":"compare_dist object inherits following struct classes: compare_dist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare distributions — compare_dist","text":"","code":"M = compare_dist(       factor_name = \"V1\")  D1=MTBLS79_DatasetExperiment(filtered=FALSE) D2=MTBLS79_DatasetExperiment(filtered=TRUE) C = compare_dist(factor_name='Class') chart_plot(C,D1,D2) #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 8012 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 8012 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 10623 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 6 rows containing missing values (`geom_path()`). #> Warning: Removed 10623 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 6 rows containing missing values (`geom_path()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`).  #> TableGrob (4 x 2) \"arrange\": 4 grobs #>   z     cells    name           grob #> 1 1 (1-1,1-1) arrange gtable[layout] #> 2 2 (1-1,2-2) arrange gtable[layout] #> 3 3 (2-4,1-1) arrange gtable[layout] #> 4 4 (2-4,2-2) arrange gtable[layout]"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for confounding factors — confounders_clsq","title":"Check for confounding factors — confounders_clsq","text":"Univariate least squares regression models used compare models without potential confounding factors included. change coefficients (delta) computed potential confounding factor. Factors large delta said large impact model therefore confounding. p-values computed models confounders included reduce potential false positives. suitable main factors 2 levels.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for confounding factors — confounders_clsq","text":"","code":"confounders_clsq(   alpha = 0.05,   mtc = \"fdr\",   factor_name,   confounding_factors,   threshold = 0.15,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for confounding factors — confounders_clsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name main factor factors may confounding. confounding_factors (character) name(s) factor(s) potential confounding factors. threshold (numeric) Factors delta greater threshold considered confounding. default 0.15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for confounding factors — confounders_clsq","text":" confounders_clsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Check for confounding factors — confounders_clsq","text":"confounders_clsq object inherits following struct classes: confounders_clsq() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for confounding factors — confounders_clsq","text":"","code":"M = confounders_clsq(       alpha = 0.05,       mtc = \"fdr\",       factor_name = character(0),       confounding_factors = character(0),       threshold = 0.15)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change barchart — confounders_lsq_barchart","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"barchart relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"confounders_lsq_barchart(feature_to_plot, threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"feature_to_plot (numeric, character, integer) column name feature plotted. threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"  confounders_lsq_barchart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"confounders_lsq_barchart object inherits following struct classes: confounders_lsq_barchart() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"M = confounders_lsq_barchart(       feature_to_plot = 1,       threshold = 10)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_barchart(feature_to_plot=1,threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change boxplot — confounders_lsq_boxplot","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"boxplot relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"confounders_lsq_boxplot(threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"  confounders_lsq_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"confounders_lsq_boxplot object inherits following struct classes: confounders_lsq_boxplot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"M = confounders_lsq_boxplot(       threshold = 10)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_boxplot(threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalisation to constant sum — constant_sum_norm","title":"Normalisation to constant sum — constant_sum_norm","text":"sample normalised total signal equal one (scaling factor specified).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"constant_sum_norm(scaling_factor = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalisation to constant sum — constant_sum_norm","text":"scaling_factor (numeric) scaling factor applied normalisation. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalisation to constant sum — constant_sum_norm","text":" constant_sum_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Normalisation to constant sum — constant_sum_norm","text":"constant_sum_norm object inherits following struct classes: constant_sum_norm() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"M = constant_sum_norm(       scaling_factor = 1)  M = constant_sum_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlation coefficient — corr_coef","title":"Correlation coefficient — corr_coef","text":"correlation features set continuous factor calculated. Multiple-test corrected p-values used indicate whether computed coefficients may occurred chance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlation coefficient — corr_coef","text":"","code":"corr_coef(alpha = 0.05, mtc = \"fdr\", factor_names, method = \"spearman\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlation coefficient — corr_coef","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. method (character) Type correlation. Allowed values limited following: \"kendall\": Kendall's tau computed. \"pearson\": Pearson product moment correlation computed. \"spearman\": Spearman's rho statistic computed. default \"spearman\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlation coefficient — corr_coef","text":" corr_coef object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correlation coefficient — corr_coef","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Correlation coefficient — corr_coef","text":"corr_coef object inherits following struct classes: corr_coef() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correlation coefficient — corr_coef","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correlation coefficient — corr_coef","text":"","code":"M = corr_coef(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       method = \"spearman\")  D = MTBLS79_DatasetExperiment(filtered=TRUE)  # subset for this example D = D[,1:10]  # convert to numeric for this example D$sample_meta$sample_order=as.numeric(D$sample_meta$run_order) D$sample_meta$sample_rep=as.numeric(D$sample_meta$Sample_Rep)  M = corr_coef(factor_names=c('sample_order','sample_rep')) M = model_apply(M,D) #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"DFA scores plot — dfa_scores_plot","title":"DFA scores plot — dfa_scores_plot","text":"scatter plot selected DFA components.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DFA scores plot — dfa_scores_plot","text":"","code":"dfa_scores_plot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DFA scores plot — dfa_scores_plot","text":"components (numeric) components selected plotting. default c(1, 2). points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Hotelling T2 ellipses (p=0.95) plotted groups samples. \"group\": Hotelling T2 ellipses (p=0.95) plotted groups. \"none\": Ellipses included plot. \"sample\": Hotelling T2 ellipse (p=0.95) plotted samples (ignoring group). default \"\". label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DFA scores plot — dfa_scores_plot","text":"  dfa_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DFA scores plot — dfa_scores_plot","text":"object makes use functionality following packages: scales ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"DFA scores plot — dfa_scores_plot","text":"dfa_scores_plot object inherits following struct classes: dfa_scores_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DFA scores plot — dfa_scores_plot","text":"Wickham H, Pedersen T, Seidel D (2023). scales: Scale Functions Visualization. R package version 1.3.0, https://CRAN.R-project.org/package=scales. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DFA scores plot — dfa_scores_plot","text":"","code":"M = dfa_scores_plot(       components = c(1, 2),       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88)  D = iris_DatasetExperiment() M = mean_centre() + DFA(factor_name='Species') M = model_apply(M,D) C = dfa_scores_plot(factor_name = 'Species') chart_plot(C,M[2]) #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor?"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Dispersion ratio filter — dratio_filter","title":"Dispersion ratio filter — dratio_filter","text":"dispersion ratio (d-ratio) compares standard deviation (non-parametric equivalent) Quality Control (QC) samples relative standard deviation (non-parametric equivalent) samples feature. d-ratio greater predefined threshold observed sample variance due technical variance feature removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dispersion ratio filter — dratio_filter","text":"","code":"dratio_filter(   threshold = 20,   qc_label = \"QC\",   factor_name,   method = \"ratio\",   dispersion = \"sd\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dispersion ratio filter — dratio_filter","text":"threshold (numeric) threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. method (character) dratio method. Allowed values limited following: \"ratio\": Dispersion QCs divided dispersion samples. Corresponds Eq 4  Broadhurst et al (2018). \"euclidean\": Dispersion QCs divided euclidean length total dispersion. Total dispersion estimated QC Sample dispersion assuming orthogonal. Corresponds Eq 5 Broadhurst et al (2018). default \"ratio\". dispersion (character) Dispersion method. Allowed values limited following: \"sd\": Dispersion estimated using standard deviation. \"mad\": Dispersion estimated using median absolute deviation. default \"sd\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dispersion ratio filter — dratio_filter","text":" dratio_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Dispersion ratio filter — dratio_filter","text":"dratio_filter object inherits following struct classes: dratio_filter() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dispersion ratio filter — dratio_filter","text":"Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB (2018). \"Guidelines considerations use system suitability quality control samples mass spectrometry assays applied untargeted clinical metabolomic studies.\" Metabolomics, 14(6).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dispersion ratio filter — dratio_filter","text":"","code":"M = dratio_filter(       threshold = 20,       qc_label = \"QC\",       factor_name = \"V1\",       method = \"ratio\",       dispersion = \"sd\")  D = MTBLS79_DatasetExperiment() M = dratio_filter(threshold=20,qc_label='QC',factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Equal group sized sampling — equal_split","title":"Equal group sized sampling — equal_split","text":"Samples randomly chosen level training set equal numbers samples levels. number samples based input proportion smallest group size.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Equal group sized sampling — equal_split","text":"","code":"equal_split(p_train = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Equal group sized sampling — equal_split","text":"p_train (numeric) proportion samples selected training set. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Equal group sized sampling — equal_split","text":" equal_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Equal group sized sampling — equal_split","text":"equal_split object inherits following struct classes: equal_split() ⭢ split_data() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Equal group sized sampling — equal_split","text":"","code":"M = equal_split(       factor_name = \"V1\",       p_train = 0.75)  D = iris_DatasetExperiment() M = equal_split(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — feature_boxplot","title":"Feature boxplot — feature_boxplot","text":"boxplot visualise distribution values within feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — feature_boxplot","text":"","code":"feature_boxplot(   label_outliers = TRUE,   feature_to_plot,   factor_name,   show_counts = TRUE,   style = \"boxplot\",   jitter = FALSE,   fill = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — feature_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": index outlier samples included plot. \"FALSE\": labels displayed. default TRUE. feature_to_plot (character, numeric, integer) column name plotted feature. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"TRUE\": number samples box displayed. \"FALSE\": number samples box displayed. default TRUE. style (character) Plot style. Allowed values limited following: \"boxplot\": Boxplot style. \"violin\": Violon plot style. default \"boxplot\". jitter (logical) Include points plotted added jitter. default FALSE. fill (logical) Block fill boxes violins group colour. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — feature_boxplot","text":"  feature_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature boxplot — feature_boxplot","text":"feature_boxplot object inherits following struct classes: feature_boxplot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — feature_boxplot","text":"","code":"M = feature_boxplot(       label_outliers = FALSE,       feature_to_plot = \"V1\",       factor_name = \"V1\",       show_counts = FALSE,       style = \"boxplot\",       jitter = FALSE,       fill = FALSE)  D = MTBLS79_DatasetExperiment C = feature_boxplot(factor_name='Species',feature_to_plot='Petal.Width') chart_plot(C,D) #> Warning: no chart defined for \"function\" #> A \"feature_boxplot\" object #> -------------------------- #> name:          Feature boxplot #> description:   A boxplot to visualise the distribution of values within a feature. #> input params:  label_outliers, feature_to_plot, factor_name, show_counts, style, jitter, fill"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile","title":"Feature profile — feature_profile","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile","text":"","code":"feature_profile(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   plot_sd = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. plot_sd (logical) Plot standard deviation. Allowed values limited following: \"TRUE\": Standard deviation samples QCs included plot. \"FALSE\": Standard deviation plotted. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile","text":"  feature_profile  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature profile — feature_profile","text":"feature_profile object inherits following struct classes: feature_profile() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile","text":"","code":"M = feature_profile(       run_order = character(0),       qc_label = character(0),       qc_column = character(0),       colour_by = character(0),       feature_to_plot = numeric(0),       plot_sd = FALSE)  D = MTBLS79_DatasetExperiment() C = feature_profile(run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1) chart_plot(C,D) #> Warning: Removed 18 rows containing missing values (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile_array","title":"Feature profile — feature_profile_array","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile_array","text":"","code":"feature_profile_array(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   nrow = 5,   log = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile_array","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. nrow (numeric, integer) number rows plot. default 5. log (logical) Log transform. Allowed values limited following: \"TRUE\": data log tranformed plotting. \"FALSE\": data transformed plotting. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile_array","text":"  feature_profile_array  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature profile — feature_profile_array","text":"feature_profile_array object inherits following struct classes: feature_profile_array() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile_array","text":"","code":"M = feature_profile_array(       run_order = character(0),       qc_label = character(0),       qc_column = character(0),       colour_by = character(0),       feature_to_plot = numeric(0),       nrow = 1,       log = FALSE)  D = MTBLS79_DatasetExperiment() C = feature_profile_array(     run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1:3,     nrow=1,     log=TRUE) chart_plot(C,D) #> Warning: Removed 23 rows containing missing values (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by name — filter_by_name","title":"Filter by name — filter_by_name","text":"Filter samples/variables row/column name, index logicals.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by name — filter_by_name","text":"","code":"filter_by_name(mode = \"exclude\", dimension = \"sample\", names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by name — filter_by_name","text":"mode (character) filtering mode controls whether samples/features mode=\"included\" mode=\"excluded\" based name. default \"exclude\". dimension (character) filtering dimensions controls whether dimension=\"sample\" dimension=\"variable\" filtered based name. default \"sample\". names (character, numeric, logical) name features/samples filtered. Must exact match. Can also provide indexes (numeric) logical. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by name — filter_by_name","text":" filter_by_name object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter by name — filter_by_name","text":"filter_by_name object inherits following struct classes: filter_by_name() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by name — filter_by_name","text":"","code":"M = filter_by_name(       mode = \"exclude\",       dimension = \"sample\",       names = character(0))  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='exclude',dimension='variable',names=c(1,2,3)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum number of measured values filter — filter_na_count","title":"Minimum number of measured values filter — filter_na_count","text":"number measured values counted feature, feature less predefined minimum number values group removed. several factors, threshold applied minimum number samples present combinations (interactions) groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"filter_na_count(threshold, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum number of measured values filter — filter_na_count","text":"threshold (numeric) minimum number samples group/interaction. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Minimum number of measured values filter — filter_na_count","text":" filter_na_count object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Minimum number of measured values filter — filter_na_count","text":"filter_na_count object inherits following struct classes: filter_na_count() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"M = filter_na_count(       threshold = 2,       factor_name = \"V1\")  D = MTBLS79_DatasetExperiment() M = filter_na_count(threshold=3,factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by sample meta data — filter_smeta","title":"Filter by sample meta data — filter_smeta","text":"data filtered named levels factor included/excluded dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by sample meta data — filter_smeta","text":"","code":"filter_smeta(mode = \"include\", levels, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by sample meta data — filter_smeta","text":"mode (character) Mode action. Allowed values limited following: \"include\": Samples specified levels retained. \"exclude\": Samples specified levels excluded. default \"include\". levels (character) level name(s) filtering. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by sample meta data — filter_smeta","text":" filter_smeta object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter by sample meta data — filter_smeta","text":"filter_smeta object inherits following struct classes: filter_smeta() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by sample meta data — filter_smeta","text":"","code":"M = filter_smeta(       mode = \"include\",       levels = character(0),       factor_name = \"V1\")  D = MTBLS79_DatasetExperiment() M = filter_smeta(mode='exclude',levels='QC',factor_name='QC') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher Exact Test — fisher_exact","title":"Fisher Exact Test — fisher_exact","text":"fisher exact test used analyse contingency tables comparing number correctly/incorrectly predicted group labels. multiple test corrected p-value indicates whether number measured values significantly different groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher Exact Test — fisher_exact","text":"","code":"fisher_exact(alpha = 0.05, mtc = \"fdr\", factor_name, factor_pred, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher Exact Test — fisher_exact","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. factor_pred (data.frame) data.frame, column factor predicted group labels compare true groups labels. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher Exact Test — fisher_exact","text":" fisher_exact object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fisher Exact Test — fisher_exact","text":"fisher_exact object inherits following struct classes: fisher_exact() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher Exact Test — fisher_exact","text":"","code":"M = fisher_exact(       alpha = 0.05,       mtc = \"fdr\",       factor_name = \"V1\",       factor_pred = data.frame(id=NA))  # load some data D=MTBLS79_DatasetExperiment()  # prepare predictions based on NA pred=as.data.frame(is.na(D$data)) pred=lapply(pred,factor,levels=c(TRUE,FALSE)) pred=as.data.frame(pred)  # apply method M = fisher_exact(alpha=0.05,mtc='fdr',factor_name='Class',factor_pred=pred) M=model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change — fold_change","title":"Fold change — fold_change","text":"Fold change relative change mean (non-parametric equivalent) intensities feature pairs levels factor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change — fold_change","text":"","code":"fold_change(   factor_name,   paired = FALSE,   sample_name = character(0),   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change — fold_change","text":"factor_name (character) name sample-meta column use. paired (logical) Paired fold change. Allowed values limited following: \"TRUE\": Fold change calculated taking account paired sampling. \"FALSE\": Fold change calculated assuming paired sampling. default FALSE. sample_name (character) name sample_meta column containing sample identifiers paired sampling. default character(0). threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level name group used denominator (possible) computing fold change. default character(0). method (character) Fold change method. Allowed values limited following: \"geometric\": log transform applied using group means calculate fold change. non-tranformedspace equivalent using geometric group means. Confidence intervals independant paired sampling estimated using standard error mean log transformed space transformed back original space. \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change — fold_change","text":" fold_change object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change — fold_change","text":"fold_change object inherits following struct classes: fold_change() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change — fold_change","text":"Price Jr RM, Bonett DG (2020). \"Confidence Intervals Ratios Means Medians.\" Journal Educational Behavioral Statistics, 45(6), 750-770.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change — fold_change","text":"","code":"M = fold_change(       factor_name = \"V1\",       sample_name = character(0),       paired = FALSE,       threshold = 2,       control_group = character(0),       method = \"geometric\",       conf_level = 0.95)  D = MTBLS79_DatasetExperiment() M = fold_change(factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change for interactions between factors — fold_change_int","title":"Fold change for interactions between factors — fold_change_int","text":"one factor fold change calculation extended include combinations levels (interactions) factors. Paired fold changes possible computation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"fold_change_int(   factor_name,   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change for interactions between factors — fold_change_int","text":"factor_name (character) name sample-meta column use. threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level names groups used denominator (possible) computing fold change. One level factor, assumed order factor_name. default character(0). method (character) Fold change method. Allowed values limited following: \"geometric\": log transform applied using group means calculate fold change. non-tranformedspace equivalent using geometric group means. Confidence intervals independant paired sampling estimated using standard error mean log transformed space transformed back original space. \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change for interactions between factors — fold_change_int","text":" fold_change_int object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change for interactions between factors — fold_change_int","text":"fold_change_int object inherits following struct classes: fold_change_int() ⭢ fold_change() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change for interactions between factors — fold_change_int","text":"Lloyd GR, Jankevics , Weber RJM (2020). \"struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond.\" Bioinformatics, 36(22-23), 5551-5552. https://doi.org/10.1093/bioinformatics/btaa1031.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"M = fold_change_int(       factor_name = \"V1\",       sample_name = character(0),       threshold = 2,       control_group = character(0),       method = \"geometric\",       paired = FALSE,       conf_level = 0.95)  D = MTBLS79_DatasetExperiment() D=D[,1:10,drop=FALSE] M = filter_smeta(mode='exclude',levels='QC',factor_name='Class') +     fold_change_int(factor_name=c('Class','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change plot — fold_change_plot","title":"Fold change plot — fold_change_plot","text":"plot fold changes calculated chosen subset features. predefined fold change threshold indicated shaded regions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change plot — fold_change_plot","text":"","code":"fold_change_plot(number_features = 20, orientation = \"portrait\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change plot — fold_change_plot","text":"number_features (numeric) number randomly selected features plot, list column numbers. default 20. orientation (character) Plot orientation. Allowed values limited following: \"landscape\": Features plotted y-axis. \"portrait\": Features plotted x-axis. default \"portrait\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change plot — fold_change_plot","text":"  fold_change_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change plot — fold_change_plot","text":"fold_change_plot object inherits following struct classes: fold_change_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change plot — fold_change_plot","text":"","code":"M = fold_change_plot(       number_features = 10,       orientation = \"portrait\")  C = fold_change_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection by rank — forward_selection_by_rank","title":"Forward selection by rank — forward_selection_by_rank","text":"model trained performance metric computed including increasing numbers features model. features included step defined rank, computed another variable e.g. VIP score. \"optimal\"subset features suggested minimising input performance metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"forward_selection_by_rank(   min_no_vars = 1,   max_no_vars = 100,   step_size = 1,   factor_name,   variable_rank,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection by rank — forward_selection_by_rank","text":"min_no_vars (numeric) minimum number variables include model. default 1. max_no_vars (numeric) maximum number variables include model. default 100. step_size (numeric) incremental change number features model. default 1. factor_name (character) name sample-meta column use. variable_rank (numeric, integer) values used rank features. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection by rank — forward_selection_by_rank","text":" forward_selection_by_rank object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Forward selection by rank — forward_selection_by_rank","text":"forward_selection_by_rank object inherits following struct classes: forward_selection_by_rank() ⭢ resampler() ⭢ iterator() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"M = forward_selection_by_rank(       min_no_vars = 1,       max_no_vars = 100,       step_size = 1,       factor_name = \"V1\",       variable_rank = 1)  # some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection line plot — fs_line","title":"Forward selection line plot — fs_line","text":"line plot forward selection. computed model performance metric plotted number features included model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection line plot — fs_line","text":"","code":"fs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection line plot — fs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection line plot — fs_line","text":"  fs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Forward selection line plot — fs_line","text":"fs_line object inherits following struct classes: fs_line() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection line plot — fs_line","text":"","code":"M = fs_line()  # some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())  # chart C = fs_line() chart_plot(C,M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Glog optimisation — glog_opt_plot","title":"Glog optimisation — glog_opt_plot","text":"plot sum squares error (SSE) vs different values lambda glog transform. indicated optimum value lambda minimises SSE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glog optimisation — glog_opt_plot","text":"","code":"glog_opt_plot(plot_grid = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glog optimisation — glog_opt_plot","text":"plot_grid (numeric)  default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glog optimisation — glog_opt_plot","text":"  glog_opt_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glog optimisation — glog_opt_plot","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Glog optimisation — glog_opt_plot","text":"glog_opt_plot object inherits following struct classes: glog_opt_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Glog optimisation — glog_opt_plot","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glog optimisation — glog_opt_plot","text":"","code":"M = glog_opt_plot(       plot_grid = numeric(0))  D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard C = glog_opt_plot() chart_plot(C,M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Generalised logarithmic transform — glog_transform","title":"Generalised logarithmic transform — glog_transform","text":"generalised logarithm (glog) transformation applies log transformation applying offset account technical variation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generalised logarithmic transform — glog_transform","text":"","code":"glog_transform(qc_label = \"QC\", factor_name, lambda = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generalised logarithmic transform — glog_transform","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. lambda (numeric, NULL) value lambda use. NULL pmp package used determine \"optimal\" value lambda. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generalised logarithmic transform — glog_transform","text":" glog_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generalised logarithmic transform — glog_transform","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Generalised logarithmic transform — glog_transform","text":"glog_transform object inherits following struct classes: glog_transform() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generalised logarithmic transform — glog_transform","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp. Durbin B, Hardin J, Hawkins D, Rocke D (2002). \"variance-stabilizing transformation gene-expression microarray data.\" Bioinformatics, 18(Suppl 1), S105-S110. Parsons HM, Ludwig C, Gunther UL, Viant MR (2007). \"Improved classification accuracy 1- ', '2-dimensional NMR metabolomics data using variance ', 'stabilising generalised logarithm transformation.\" Bioinformatics, 8(1), 234.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generalised logarithmic transform — glog_transform","text":"","code":"M = glog_transform(       qc_label = \"QC\",       factor_name = \"V1\",       lambda = NULL)  D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"One dimensional grid search — grid_search_1d","title":"One dimensional grid search — grid_search_1d","text":"one dimensional grid search calculates performance metric model evenly spaced values model input parameter. \"optimum\" value parameter suggested one maximises performance, minimises error (whichever appropriate chosen metric)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One dimensional grid search — grid_search_1d","text":"","code":"grid_search_1d(   param_to_optimise,   search_values,   model_index,   factor_name,   max_min = \"min\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One dimensional grid search — grid_search_1d","text":"param_to_optimise (character) name model input parameter focus search. search_values () values input parameter optimised. model_index (numeric, integer) index model sequence uses parameter optimised. factor_name (character) name sample-meta column use. max_min (character) Maximise minimise. Allowed values limited following: \"max\": optimium parameter value suggested based maximising performance metric. \"min\": optimium parameter value suggested based minimising performance metric. default \"min\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One dimensional grid search — grid_search_1d","text":" grid_search_1d object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"One dimensional grid search — grid_search_1d","text":"grid_search_1d object inherits following struct classes: grid_search_1d() ⭢ resampler() ⭢ iterator() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One dimensional grid search — grid_search_1d","text":"","code":"M = grid_search_1d(       param_to_optimise = character(0),       search_values = numeric(0),       model_index = numeric(0),       factor_name = \"V1\",       max_min = \"min\")  D = MTBLS79_DatasetExperiment() # some preprocessing M = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute() +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(factor_name='Class',levels='QC',mode='exclude') M=model_apply(M,D) D=predicted(M)  # reduce number of features for this example D=D[,1:10]  # optmise number of components for PLS model I = grid_search_1d(param_to_optimise='number_components',search_values=1:5,         model_index=2,factor_name='Class') *         (mean_centre()+PLSDA(factor_name='Class')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Grid search line plot — gs_line","title":"Grid search line plot — gs_line","text":"plot calculated performance metric model input parameter values used train model. optimum parameter value indicated based minimising (maximising) chosen metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grid search line plot — gs_line","text":"","code":"gs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grid search line plot — gs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grid search line plot — gs_line","text":"  gs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Grid search line plot — gs_line","text":"gs_line object inherits following struct classes: gs_line() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Grid search line plot — gs_line","text":"","code":"M = gs_line()  C = gs_line()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"HCA dendrogram — hca_dendrogram","title":"HCA dendrogram — hca_dendrogram","text":"dendrogram visualising clustering HCA.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HCA dendrogram — hca_dendrogram","text":"","code":"hca_dendrogram(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HCA dendrogram — hca_dendrogram","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HCA dendrogram — hca_dendrogram","text":"  hca_dendrogram  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"HCA dendrogram — hca_dendrogram","text":"object makes use functionality following packages: ggdendro","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"HCA dendrogram — hca_dendrogram","text":"hca_dendrogram object inherits following struct classes: hca_dendrogram() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"HCA dendrogram — hca_dendrogram","text":"de Vries , Ripley BD (2022). ggdendro: Create Dendrograms Tree Diagrams Using 'ggplot2'. R package version 0.1.23, https://CRAN.R-project.org/package=ggdendro.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HCA dendrogram — hca_dendrogram","text":"","code":"M = hca_dendrogram()  C = hca_dendrogram()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross-validation — kfold_xval","title":"k-fold cross-validation — kfold_xval","text":"k-fold cross-validation iterative approach applied validate models. samples divided k \"folds\", subsets. subset excluded model training used model validation , resulting single left-prediction sample. Model performance metrics computed training test sets across folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross-validation — kfold_xval","text":"","code":"kfold_xval(folds = 10, method = \"venetian\", factor_name, collect = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross-validation — kfold_xval","text":"folds (numeric, integer) number cross-validation folds. default 10. method (character) Fold selection method. Allowed values limited following: \"venetian\": Every nth sample assigned fold, n number folds. \"blocks\": Blocks adjacent samples assigned fold. \"random\": Samples randomly assigned fold. default \"venetian\". factor_name (character) name sample-meta column use. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross-validation — kfold_xval","text":" kfold_xval object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"k-fold cross-validation — kfold_xval","text":"kfold_xval object inherits following struct classes: kfold_xval() ⭢ resampler() ⭢ iterator() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross-validation — kfold_xval","text":"","code":"M = kfold_xval(       folds = 5,       method = \"random\",       factor_name = \"V1\",       collect = NULL)  D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross validation plot — kfoldxcv_grid","title":"k-fold cross validation plot — kfoldxcv_grid","text":"graphic visualising true class predicted class samples groups cross-validation folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"kfoldxcv_grid(factor_name, level, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross validation plot — kfoldxcv_grid","text":"factor_name (character) name sample-meta column use. level (character) level/group plot. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross validation plot — kfoldxcv_grid","text":"  kfoldxcv_grid  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"k-fold cross validation plot — kfoldxcv_grid","text":"kfoldxcv_grid object inherits following struct classes: kfoldxcv_grid() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"M = kfoldxcv_grid(       factor_name = \"V1\",       level = \"level_1\")  D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())  C = kfoldxcv_grid(factor_name='Species',level='setosa') chart_plot(C,I) #> Warning: Removed 900 rows containing missing values (`geom_tile()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"kfoldxcv metric plot — kfoldxcv_metric","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"boxplot performance metric computed fold k-fold cross-validation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"kfoldxcv_metric(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"  kfoldxcv_metric  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"kfoldxcv_metric object inherits following struct classes: kfoldxcv_metric() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"M = kfoldxcv_metric()  C = kfoldxcv_metric()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":null,"dir":"Reference","previous_headings":"","what":"kNN missing value imputation — knn_impute","title":"kNN missing value imputation — knn_impute","text":"k-nearest neighbour missing value imputation replaces missing values data average predefined number similar neighbours value present","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kNN missing value imputation — knn_impute","text":"","code":"knn_impute(   neighbours = 5,   sample_max = 50,   feature_max = 50,   by = \"features\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kNN missing value imputation — knn_impute","text":"neighbours (numeric) number neighbours (k) use imputation. default 5. sample_max (numeric) maximum percent missing values per sample. default 50. feature_max (numeric) maximum percent missing values per feature. default 50. (character) Impute using similar \"samples\" \"features\". Default features. default \"features\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kNN missing value imputation — knn_impute","text":" knn_impute object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"kNN missing value imputation — knn_impute","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"kNN missing value imputation — knn_impute","text":"knn_impute object inherits following struct classes: knn_impute() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"kNN missing value imputation — knn_impute","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kNN missing value imputation — knn_impute","text":"","code":"M = knn_impute(       neighbours = 5,       feature_max = 50,       sample_max = 50,       by = \"features\")  M = knn_impute()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — kw_p_hist","title":"Histogram of p values — kw_p_hist","text":"histogram p-values computed kruskal-wallis method","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — kw_p_hist","text":"","code":"kw_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — kw_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — kw_p_hist","text":"  kw_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of p values — kw_p_hist","text":"kw_p_hist object inherits following struct classes: kw_p_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — kw_p_hist","text":"","code":"M = kw_p_hist()  C = kw_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Kruskal-Wallis rank sum test — kw_rank_sum","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"Kruskal-Wallis test univariate hypothesis testing method allows multiple (n>=2) groups compared without making assumption values normally distributed. non-parametric equivalent 1-way ANOVA. test applied variables/features individually, multiple test corrected p-values computed indicate significance variables/features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"kw_rank_sum(alpha = 0.05, mtc = \"fdr\", factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":" kw_rank_sum object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"kw_rank_sum object inherits following struct classes: kw_rank_sum() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"M = kw_rank_sum(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\")  D = iris_DatasetExperiment() M = kw_rank_sum(factor_names='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear model — linear_model","title":"Linear model — linear_model","text":"Linear models can used carry regression, single stratum analysis variance analysis covariance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear model — linear_model","text":"","code":"linear_model(formula, na_action = \"na.omit\", contrasts = list(), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear model — linear_model","text":"formula (formula) symbolic description model fitted. na_action (character) NA action. Allowed values limited following: \"na.omit\": Incomplete cases removed. \"na.fail\": error thrown NA present. \"na.exclude\": Incomplete cases removed, output result padded correct size using NA. \"na.pass\": apply linear model NA present. default \"na.omit\". contrasts (list) contrasts associated factor. default list(). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear model — linear_model","text":" linear_model object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Linear model — linear_model","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Linear model — linear_model","text":"linear_model object inherits following struct classes: linear_model() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Linear model — linear_model","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear model — linear_model","text":"","code":"M = linear_model(       formula = y ~ x,       na_action = \"na.omit\",       contrasts = list())  D = iris_DatasetExperiment() M = linear_model(formula = y~Species)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"logarithm transform — log_transform","title":"logarithm transform — log_transform","text":"logarithmic transform applied values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"logarithm transform — log_transform","text":"","code":"log_transform(base = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"logarithm transform — log_transform","text":"base (numeric) base logarithm used transform. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"logarithm transform — log_transform","text":" log_transform object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"logarithm transform — log_transform","text":"log_transform object inherits following struct classes: log_transform() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"logarithm transform — log_transform","text":"","code":"M = log_transform(       base = 10)  M = log_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean centre — mean_centre","title":"Mean centre — mean_centre","text":"mean sample subtracted samples data matrix. features centred matrix zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean centre — mean_centre","text":"","code":"mean_centre(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean centre — mean_centre","text":"mode (character) Mode action. Allowed values limited following: \"data\": Centring applied data block. \"sample_meta\": Centring applied sample_meta block. \"\": Centring applied data sample_meta blocks. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean centre — mean_centre","text":" mean_centre object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mean centre — mean_centre","text":"mean_centre object inherits following struct classes: mean_centre() ⭢ preprocess() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean centre — mean_centre","text":"","code":"M = mean_centre(       mode = \"data\")  M = mean_centre()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean of medians — mean_of_medians","title":"Mean of medians — mean_of_medians","text":"data matrix normalised mean median factor level.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean of medians — mean_of_medians","text":"","code":"mean_of_medians(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean of medians — mean_of_medians","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean of medians — mean_of_medians","text":" mean_of_medians object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mean of medians — mean_of_medians","text":"mean_of_medians object inherits following struct classes: mean_of_medians() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean of medians — mean_of_medians","text":"","code":"M = mean_of_medians(       factor_name = \"V1\")  D = iris_DatasetExperiment() M = mean_of_medians(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixed effects model — mixed_effect","title":"Mixed effects model — mixed_effect","text":"mixed effects model extension ANOVA fixed random effects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixed effects model — mixed_effect","text":"","code":"mixed_effect(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"marginal\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixed effects model — mixed_effect","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) Sum squares type. Allowed values limited following: \"marginal\": Type III sum squares. \"sequential\": Type II sum squares. default \"marginal\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mixed effects model — mixed_effect","text":" mixed_effect object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mixed effects model — mixed_effect","text":"object makes use functionality following packages: nlme emmeans","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mixed effects model — mixed_effect","text":"mixed_effect object inherits following struct classes: mixed_effect() ⭢ ANOVA() ⭢ model() ⭢ stato() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mixed effects model — mixed_effect","text":"Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-164, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882. Lenth R (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.9.0, https://CRAN.R-project.org/package=emmeans. Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixed effects model — mixed_effect","text":"","code":"M = mixed_effect(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       ss_type = \"marginal\")  D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = mixed_effect(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply method — model_apply,ANOVA,DatasetExperiment-method","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Applies method input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"# S4 method for ANOVA,DatasetExperiment model_apply(M, D)  # S4 method for HSD,DatasetExperiment model_apply(M, D)  # S4 method for mixed_effect,DatasetExperiment model_apply(M, D)  # S4 method for HSDEM,DatasetExperiment model_apply(M, D)  # S4 method for classical_lsq,DatasetExperiment model_apply(M, D)  # S4 method for confounders_clsq,DatasetExperiment model_apply(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_apply(M, D)  # S4 method for corr_coef,DatasetExperiment model_apply(M, D)  # S4 method for split_data,DatasetExperiment model_apply(M, D)  # S4 method for equal_split,DatasetExperiment model_apply(M, D)  # S4 method for filter_smeta,DatasetExperiment model_apply(M, D)  # S4 method for fisher_exact,DatasetExperiment model_apply(M, D)  # S4 method for fold_change,DatasetExperiment model_apply(M, D)  # S4 method for fold_change_int,DatasetExperiment model_apply(M, D)  # S4 method for HCA,DatasetExperiment model_apply(M, D)  # S4 method for knn_impute,DatasetExperiment model_apply(M, D)  # S4 method for kw_rank_sum,DatasetExperiment model_apply(M, D)  # S4 method for log_transform,DatasetExperiment model_apply(M, D)  # S4 method for mean_of_medians,DatasetExperiment model_apply(M, D)  # S4 method for nroot_transform,DatasetExperiment model_apply(M, D)  # S4 method for pairs_filter,DatasetExperiment model_apply(M, D)  # S4 method for prop_na,DatasetExperiment model_apply(M, D)  # S4 method for rsd_filter,DatasetExperiment model_apply(M, D)  # S4 method for sb_corr,DatasetExperiment model_apply(M, D)  # S4 method for stratified_split,DatasetExperiment model_apply(M, D)  # S4 method for tSNE,DatasetExperiment model_apply(M, D)  # S4 method for ttest,DatasetExperiment model_apply(M, D)  # S4 method for vec_norm,DatasetExperiment model_apply(M, D)  # S4 method for wilcox_test,DatasetExperiment model_apply(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"M method object D another object used first","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Returns modified method object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"M=model() model_apply(M,DatasetExperiment()) #> Warning: no training implemented for this model #> A \"model\" object #> ---------------- #> name:           #> description:    #> predicted:      #> seq_in:        data #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Model prediction — model_predict,DFA,DatasetExperiment-method","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Apply model using input DatasetExperiment. Assumes model trained first.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"# S4 method for DFA,DatasetExperiment model_predict(M, D)  # S4 method for PCA,DatasetExperiment model_predict(M, D)  # S4 method for PLSR,DatasetExperiment model_predict(M, D)  # S4 method for PLSDA,DatasetExperiment model_predict(M, D)  # S4 method for autoscale,DatasetExperiment model_predict(M, D)  # S4 method for blank_filter,DatasetExperiment model_predict(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_predict(M, D)  # S4 method for dratio_filter,DatasetExperiment model_predict(M, D)  # S4 method for filter_by_name,DatasetExperiment model_predict(M, D)  # S4 method for filter_na_count,DatasetExperiment model_predict(M, D)  # S4 method for filter_smeta,DatasetExperiment model_predict(M, D)  # S4 method for glog_transform,DatasetExperiment model_predict(M, D)  # S4 method for linear_model,DatasetExperiment model_predict(M, D)  # S4 method for mean_centre,DatasetExperiment model_predict(M, D)  # S4 method for mv_feature_filter,DatasetExperiment model_predict(M, D)  # S4 method for mv_sample_filter,DatasetExperiment model_predict(M, D)  # S4 method for OPLSR,DatasetExperiment model_predict(M, D)  # S4 method for OPLSDA,DatasetExperiment model_predict(M, D)  # S4 method for pareto_scale,DatasetExperiment model_predict(M, D)  # S4 method for pqn_norm,DatasetExperiment model_predict(M, D)  # S4 method for SVM,DatasetExperiment model_predict(M, D)  # S4 method for vec_norm,DatasetExperiment model_predict(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_predict(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Reverse effect preprocessing step DatasetExperiment.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"# S4 method for autoscale,DatasetExperiment model_reverse(M, D)  # S4 method for mean_centre,DatasetExperiment model_reverse(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Returns modified DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"M = example_model() D = model_reverse(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model — model_train,DFA,DatasetExperiment-method","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Trains model using input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"# S4 method for DFA,DatasetExperiment model_train(M, D)  # S4 method for PCA,DatasetExperiment model_train(M, D)  # S4 method for PLSR,DatasetExperiment model_train(M, D)  # S4 method for PLSDA,DatasetExperiment model_train(M, D)  # S4 method for autoscale,DatasetExperiment model_train(M, D)  # S4 method for blank_filter,DatasetExperiment model_train(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_train(M, D)  # S4 method for dratio_filter,DatasetExperiment model_train(M, D)  # S4 method for filter_by_name,DatasetExperiment model_train(M, D)  # S4 method for filter_na_count,DatasetExperiment model_train(M, D)  # S4 method for filter_smeta,DatasetExperiment model_train(M, D)  # S4 method for glog_transform,DatasetExperiment model_train(M, D)  # S4 method for linear_model,DatasetExperiment model_train(M, D)  # S4 method for mean_centre,DatasetExperiment model_train(M, D)  # S4 method for mv_feature_filter,DatasetExperiment model_train(M, D)  # S4 method for mv_sample_filter,DatasetExperiment model_train(M, D)  # S4 method for OPLSR,DatasetExperiment model_train(M, D)  # S4 method for OPLSDA,DatasetExperiment model_train(M, D)  # S4 method for pareto_scale,DatasetExperiment model_train(M, D)  # S4 method for pqn_norm,DatasetExperiment model_train(M, D)  # S4 method for SVM,DatasetExperiment model_train(M, D)  # S4 method for vec_norm,DatasetExperiment model_train(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_train(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value boxplots — mv_boxplot","title":"Missing value boxplots — mv_boxplot","text":"Boxplots number missing values per sample/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value boxplots — mv_boxplot","text":"","code":"mv_boxplot(   label_outliers = TRUE,   by_sample = TRUE,   factor_name,   show_counts = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value boxplots — mv_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": Sample labels potential outliers displayed plot. \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"TRUE\": Missing values plotted per sample. \"FALSE\": Missing values plotted per feature. default TRUE. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"TRUE\": number samples box displayed. \"FALSE\": number samples box displayed. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value boxplots — mv_boxplot","text":"  mv_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value boxplots — mv_boxplot","text":"mv_boxplot object inherits following struct classes: mv_boxplot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value boxplots — mv_boxplot","text":"","code":"M = mv_boxplot(       label_outliers = FALSE,       by_sample = FALSE,       factor_name = \"V1\",       show_counts = FALSE)  D = MTBLS79_DatasetExperiment() C = mv_boxplot(factor_name='Class') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter features by missing values — mv_feature_filter","title":"Filter features by missing values — mv_feature_filter","text":"Removes features percentage non-missing values falls threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter features by missing values — mv_feature_filter","text":"","code":"mv_feature_filter(   threshold = 20,   qc_label = \"QC\",   method = \"QC\",   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter features by missing values — mv_feature_filter","text":"threshold (numeric) minimum percentage non-missing values. default 20. qc_label (character) label used identify QC/group samples using \"QC\" (within named group) filtering method. default \"QC\". method (character) Filtering method. Allowed values limited following: \"within_all\": Features removed threshold non-missing values met groups. \"within_one\": Features removed threshold non-missing values met group. \"QC\": Features removed threshold non-missing values met named group. \"across\": filter applied ignoring sample group. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter features by missing values — mv_feature_filter","text":" mv_feature_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter features by missing values — mv_feature_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter features by missing values — mv_feature_filter","text":"mv_feature_filter object inherits following struct classes: mv_feature_filter() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Filter features by missing values — mv_feature_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter features by missing values — mv_feature_filter","text":"","code":"M = mv_feature_filter(       threshold = 20,       qc_label = \"QC\",       method = \"QC\",       factor_name = \"V1\")  D = iris_DatasetExperiment() M = mv_feature_filter(factor_name='Species',qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per feature — mv_feature_filter_hist","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"histogram proportion missing values per feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"mv_feature_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"  mv_feature_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"mv_feature_filter_hist object inherits following struct classes: mv_feature_filter_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"M = mv_feature_filter_hist()  C = mv_feature_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value histogram — mv_histogram","title":"Missing value histogram — mv_histogram","text":"histogram numbers missing values per sample/feature","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value histogram — mv_histogram","text":"","code":"mv_histogram(label_outliers = TRUE, by_sample = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value histogram — mv_histogram","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": Sample labels potential outliers displayed plot. \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"TRUE\": Missing values plotted per sample. \"FALSE\": Missing values plotted per feature. default TRUE. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value histogram — mv_histogram","text":"  mv_histogram  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value histogram — mv_histogram","text":"mv_histogram object inherits following struct classes: mv_histogram() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value histogram — mv_histogram","text":"","code":"M = mv_histogram(       label_outliers = FALSE,       by_sample = FALSE)  D = MTBLS79_DatasetExperiment() C = mv_histogram(label_outliers=FALSE,by_sample=FALSE) chart_plot(C,D) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. #> Warning: Removed 2 rows containing missing values (`geom_bar()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value sample filter — mv_sample_filter","title":"Missing value sample filter — mv_sample_filter","text":"Removes samples percent number missing values exceeds threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value sample filter — mv_sample_filter","text":"","code":"mv_sample_filter(mv_threshold = 20, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value sample filter — mv_sample_filter","text":"mv_threshold (numeric) maximum percentage features missing values sample. default 20. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value sample filter — mv_sample_filter","text":" mv_sample_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing value sample filter — mv_sample_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value sample filter — mv_sample_filter","text":"mv_sample_filter object inherits following struct classes: mv_sample_filter() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing value sample filter — mv_sample_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value sample filter — mv_sample_filter","text":"","code":"M = mv_sample_filter(       mv_threshold = 20)  C = mv_sample_filter()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per sample — mv_sample_filter_hist","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"histogram proportion missing values per sample","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"mv_sample_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"  mv_sample_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"mv_sample_filter_hist object inherits following struct classes: mv_sample_filter_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"M = mv_sample_filter_hist()  C = mv_sample_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"nth root transform — nroot_transform","title":"nth root transform — nroot_transform","text":"values data matrix transformed raising power 1/n.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nth root transform — nroot_transform","text":"","code":"nroot_transform(root = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nth root transform — nroot_transform","text":"root (numeric) nth root used transform. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nth root transform — nroot_transform","text":" nroot_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"nth root transform — nroot_transform","text":"nroot_transform object inherits following struct classes: nroot_transform() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nth root transform — nroot_transform","text":"","code":"M = nroot_transform(       root = 2)  M = nroot_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"ontology cache — ontology_cache","title":"ontology cache — ontology_cache","text":"cached list ontology terms obtained ontology lookup service (OLS) ontology terms specified objects structToolbox.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ontology cache — ontology_cache","text":"","code":"ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ontology cache — ontology_cache","text":"list cached ontology terms","code":""},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ontology cache — ontology_cache","text":"","code":"cache = ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairs filter — pairs_filter","title":"Pairs filter — pairs_filter","text":"filter used study designs paired sampling ensure  measurements source (e.g. patient) represented factor levels interactions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairs filter — pairs_filter","text":"","code":"pairs_filter(factor_name, sample_id, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairs filter — pairs_filter","text":"factor_name (character) name sample-meta column use. sample_id (character) Name sample meta column containing sample identifiers. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairs filter — pairs_filter","text":" pairs_filter object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Pairs filter — pairs_filter","text":"pairs_filter object inherits following struct classes: pairs_filter() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairs filter — pairs_filter","text":"","code":"M = pairs_filter(       factor_name = \"V1\",       sample_id = \"V1\")  M=pairs_filter(factor_name='Class',sample_id='ids')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Pareto scaling — pareto_scale","title":"Pareto scaling — pareto_scale","text":"mean sample subtracted samples scaled square root standard deviation. transformed data zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pareto scaling — pareto_scale","text":"","code":"pareto_scale(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pareto scaling — pareto_scale","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pareto scaling — pareto_scale","text":" pareto_scale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Pareto scaling — pareto_scale","text":"pareto_scale object inherits following struct classes: pareto_scale() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pareto scaling — pareto_scale","text":"","code":"M = pareto_scale()  D = iris_DatasetExperiment() M = pareto_scale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA biplot — pca_biplot","title":"PCA biplot — pca_biplot","text":"scatter plot selected principal component scores overlaid corresponding principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA biplot — pca_biplot","text":"","code":"pca_biplot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   scale_factor = 0.95,   style = \"points\",   label_features = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA biplot — pca_biplot","text":"components (numeric) principal components used generate plot. default c(1, 2). points_to_label (character) points_to_label. Allowed values limited following: \"none\": samples labelled plot. \"\": samples labelled plot. \"outliers\": Potential outliers labelled plot. default \"none\". factor_name (character) name sample-meta column use. scale_factor (numeric) scaling factor applied loadings. default 0.95. style (character) Plot style. Allowed values limited following: \"points\": Loadings scores plotted scatter plot. \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (logical) Add feature labels. Allowed values limited following: \"TRUE\": Features labelled. \"FALSE\": Features labelled. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA biplot — pca_biplot","text":"  pca_biplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA biplot — pca_biplot","text":"pca_biplot object inherits following struct classes: pca_biplot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA biplot — pca_biplot","text":"","code":"M = pca_biplot(       components = c(1, 2),       points_to_label = \"none\",       factor_name = \"V1\",       scale_factor = 0.95,       style = \"points\",       label_features = FALSE)  C = pca_biplot(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA correlation plot — pca_correlation_plot","title":"PCA correlation plot — pca_correlation_plot","text":"plot correlation variables/features selected principal component scores. Features high correlation well represented selected component(s)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"pca_correlation_plot(components = c(1, 2), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA correlation plot — pca_correlation_plot","text":"components (numeric) Principal Components used generate plot. default c(1, 2). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA correlation plot — pca_correlation_plot","text":"  pca_correlation_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA correlation plot — pca_correlation_plot","text":"pca_correlation_plot object inherits following struct classes: pca_correlation_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"M = pca_correlation_plot(       components = c(1, 2))  C = pca_correlation_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"d-statistic plot — pca_dstat_plot","title":"d-statistic plot — pca_dstat_plot","text":"bar chart d-statistics samples input PCA model. Samples indicated threshold considered outlying.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"d-statistic plot — pca_dstat_plot","text":"","code":"pca_dstat_plot(number_components = 2, alpha = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"d-statistic plot — pca_dstat_plot","text":"number_components (numeric) number principal components use. default 2. alpha (numeric) confidence threshold rejecting samples based d-statistic. default 0.05. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"d-statistic plot — pca_dstat_plot","text":"  pca_dstat_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"d-statistic plot — pca_dstat_plot","text":"pca_dstat_plot object inherits following struct classes: pca_dstat_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"d-statistic plot — pca_dstat_plot","text":"","code":"M = pca_dstat_plot(       number_components = 2,       alpha = 0.95)  C = pca_dstat_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA loadings plot — pca_loadings_plot","title":"PCA loadings plot — pca_loadings_plot","text":"barchart (one component) scatter plot (two components) selected principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"pca_loadings_plot(   components = c(1, 2),   style = \"points\",   label_features = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA loadings plot — pca_loadings_plot","text":"components (numeric) principal components used generate plot. default c(1, 2). style (character) Plot style. Allowed values limited following: \"points\": Loadings scores plotted scatter plot. \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (character, NULL) Feature labels. Allowed values limited following: \"character()\": vector labels features. \"NULL\": labels. \"row.names\": Labels extracted column names data matrix. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA loadings plot — pca_loadings_plot","text":"  pca_loadings_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA loadings plot — pca_loadings_plot","text":"pca_loadings_plot object inherits following struct classes: pca_loadings_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"M = pca_loadings_plot(       components = c(1, 2),       style = \"points\",       label_features = NULL)  C = pca_loadings_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA scores plot — pca_scores_plot","title":"PCA scores plot — pca_scores_plot","text":"Plots 2d scatter plot selected components","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA scores plot — pca_scores_plot","text":"","code":"pca_scores_plot(   xcol = \"PC1\",   ycol = \"PC2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA scores plot — pca_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"PC1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"PC2\". points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA scores plot — pca_scores_plot","text":"  pca_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA scores plot — pca_scores_plot","text":"pca_scores_plot object inherits following struct classes: pca_scores_plot() ⭢ scatter_chart() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA scores plot — pca_scores_plot","text":"","code":"M = pca_scores_plot(       components = NULL,       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() M = mean_centre() + PCA() M = model_apply(M,D) C = pca_scores_plot(factor_name = 'Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree plot — pca_scree_plot","title":"Scree plot — pca_scree_plot","text":"plot percent variance cumulative percent variance components PCA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree plot — pca_scree_plot","text":"","code":"pca_scree_plot(max_pc = 15, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree plot — pca_scree_plot","text":"max_pc (numeric, integer) maximum number components include plot. default 15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree plot — pca_scree_plot","text":"  pca_scree_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Scree plot — pca_scree_plot","text":"pca_scree_plot object inherits following struct classes: pca_scree_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree plot — pca_scree_plot","text":"","code":"M = pca_scree_plot(       max_pc = 15)  C = pca_scree_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation test — permutation_test","title":"Permutation test — permutation_test","text":"permutation test generates \"null\" model randomising response (regression models) group labels (classification models). repeated many times generate distribution performance metrics null model. distribution can compared performance true model. overlap true null model performances model overfitted.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutation test — permutation_test","text":"","code":"permutation_test(number_of_permutations = 50, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutation test — permutation_test","text":"number_of_permutations (numeric, integer) number permutations. default 50. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutation test — permutation_test","text":" permutation_test object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Permutation test — permutation_test","text":"permutation_test object inherits following struct classes: permutation_test() ⭢ resampler() ⭢ iterator() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permutation test — permutation_test","text":"","code":"M = permutation_test(       number_of_permutations = 100,       factor_name = \"V1\")  I=permutation_test(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"permutation_test_plot class — permutation_test_plot","title":"permutation_test_plot class — permutation_test_plot","text":"Plots results permutation test.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"permutation_test_plot(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"permutation_test_plot class — permutation_test_plot","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"permutation_test_plot class — permutation_test_plot","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"C = permutation_test_plot(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":null,"dir":"Reference","previous_headings":"","what":"Permute Sample Order — permute_sample_order","title":"Permute Sample Order — permute_sample_order","text":"order samples data matrix randomly permuted. relationship samples sample meta data maintained.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permute Sample Order — permute_sample_order","text":"","code":"permute_sample_order(number_of_permutations = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permute Sample Order — permute_sample_order","text":"number_of_permutations (numeric, integer) number times sample order permuted. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permute Sample Order — permute_sample_order","text":" permute_sample_order object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Permute Sample Order — permute_sample_order","text":"permute_sample_order object inherits following struct classes: permute_sample_order() ⭢ resampler() ⭢ iterator() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permute Sample Order — permute_sample_order","text":"","code":"M = permute_sample_order(       number_of_permutations = 100)  C = permute_sample_order()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"pls_regcoeff_plot class — pls_regcoeff_plot","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Plots regression coefficients PLSDA model. Plots regression coefficient scores PLSDA model","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"pls_regcoeff_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"ycol (character, numeric, integer) Y column plot. default 1. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"  pls_regcoeff_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"pls_regcoeff_plot object inherits following struct classes: pls_regcoeff_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"M = pls_regcoeff_plot(       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_regcoeff_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA scores plot — pls_scores_plot","title":"PLSDA scores plot — pls_scores_plot","text":"scatter plot selected PLSDA scores.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"pls_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )  plsda_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA scores plot — pls_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"LV1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"LV2\". points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA scores plot — pls_scores_plot","text":"  pls_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA scores plot — pls_scores_plot","text":"pls_scores_plot object inherits following struct classes: pls_scores_plot() ⭢ scatter_chart() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"M = pls_scores_plot(       components = NULL,       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_scores_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA VIP plot — pls_vip_plot","title":"PLSDA VIP plot — pls_vip_plot","text":"plot Variable Importance Projection (VIP) scores PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"pls_vip_plot(threshold = 1, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA VIP plot — pls_vip_plot","text":"threshold (numeric, integer) threshold indicating significant features. default 1. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA VIP plot — pls_vip_plot","text":"  pls_vip_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA VIP plot — pls_vip_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA VIP plot — pls_vip_plot","text":"pls_vip_plot object inherits following struct classes: pls_vip_plot() ⭢ chart() ⭢ stato() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA VIP plot — pls_vip_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"M = pls_vip_plot(       threshold = 1,       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_vip_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA feature importance summary plot — plsda_feature_importance_plot","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"plot selected feature significance metric PLSDA model top selected features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"plsda_feature_importance_plot(n_features = 30, metric = \"vip\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"n_features (numeric, integer) number features include summary. default 30. metric (character) Metric plot. Allowed values limited following: \"sr\": Plot Selectivity Ratio. \"sr_pvalue\": Plot SR p-values. \"vip\": Plot Variable Importance Projection scores. default \"vip\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"  plsda_feature_importance_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"object makes use functionality following packages: pls ggplot2 reshape2 cowplot","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"plsda_feature_importance_plot object inherits following struct classes: plsda_feature_importance_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org. Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/. Wilke C (2023). cowplot: Streamlined Plot Theme Plot Annotations 'ggplot2'. R package version 1.1.2, https://CRAN.R-project.org/package=cowplot.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"M = plsda_feature_importance_plot(       n_features = 50,       metric = \"vip\")  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_feature_importance_plot(n_features=30,metric='vip') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA predicted plot — plsda_predicted_plot","title":"PLSDA predicted plot — plsda_predicted_plot","text":"plot regression coefficients PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"plsda_predicted_plot(factor_name, style = \"boxplot\", ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA predicted plot — plsda_predicted_plot","text":"factor_name (character) name sample-meta column use. style (character) Plot style. Allowed values limited following: \"boxplot\": boxplot. \"violin\": violin plot. \"density\": density plot. default \"boxplot\". ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA predicted plot — plsda_predicted_plot","text":"  plsda_predicted_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA predicted plot — plsda_predicted_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA predicted plot — plsda_predicted_plot","text":"plsda_predicted_plot object inherits following struct classes: plsda_predicted_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA predicted plot — plsda_predicted_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"M = plsda_predicted_plot(       factor_name = \"V1\",       style = \"boxplot\",       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_predicted_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA ROC plot — plsda_roc_plot","title":"PLSDA ROC plot — plsda_roc_plot","text":"Receiver Operator Characteristic (ROC) plot PLSDA models computed adjusting threshold assigning group labels PLS predictions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"plsda_roc_plot(factor_name, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA ROC plot — plsda_roc_plot","text":"factor_name (character) name sample-meta column use. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA ROC plot — plsda_roc_plot","text":"  plsda_roc_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA ROC plot — plsda_roc_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA ROC plot — plsda_roc_plot","text":"plsda_roc_plot object inherits following struct classes: plsda_roc_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA ROC plot — plsda_roc_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"M = plsda_roc_plot(       factor_name = \"V1\",       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_roc_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Cook's distance barchart — plsr_cook_dist","title":"Cook's distance barchart — plsr_cook_dist","text":"barchart Cook's distance sample used train PLSR model. Cook's distance used estimate influence sample model can used identify potential outliers.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"plsr_cook_dist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cook's distance barchart — plsr_cook_dist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cook's distance barchart — plsr_cook_dist","text":"  plsr_cook_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Cook's distance barchart — plsr_cook_dist","text":"plsr_cook_dist object inherits following struct classes: plsr_cook_dist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"M = plsr_cook_dist(       ycol = 1)  C = plsr_cook_dist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR prediction plot — plsr_prediction_plot","title":"PLSR prediction plot — plsr_prediction_plot","text":"scatter plot true response values predicted values PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"plsr_prediction_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR prediction plot — plsr_prediction_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR prediction plot — plsr_prediction_plot","text":"  plsr_prediction_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR prediction plot — plsr_prediction_plot","text":"plsr_prediction_plot object inherits following struct classes: plsr_prediction_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"M = plsr_prediction_plot(       ycol = 1)  C = plsr_prediction_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR QQ plot — plsr_qq_plot","title":"PLSR QQ plot — plsr_qq_plot","text":"plot quantiles residuals PLSR model quantiles normal distribution.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"plsr_qq_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR QQ plot — plsr_qq_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR QQ plot — plsr_qq_plot","text":"  plsr_qq_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR QQ plot — plsr_qq_plot","text":"plsr_qq_plot object inherits following struct classes: plsr_qq_plot() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"M = plsr_qq_plot(       ycol = 1)  C = plsr_qq_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR residuals histogram — plsr_residual_hist","title":"PLSR residuals histogram — plsr_residual_hist","text":"histogram residuals PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"plsr_residual_hist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR residuals histogram — plsr_residual_hist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR residuals histogram — plsr_residual_hist","text":"  plsr_residual_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR residuals histogram — plsr_residual_hist","text":"plsr_residual_hist object inherits following struct classes: plsr_residual_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"M = plsr_residual_hist(       ycol = 1)  C = plsr_residual_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"PQN used normalise differences concentration samples. makes use Quality Control (QC) samples reference. PQN scales median change relative reference order robust changes caused response perturbation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"pqn_norm(   qc_label = \"QC\",   factor_name,   qc_frac = 0,   sample_frac = 0,   ref_method = \"mean\",   ref_mean = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. qc_frac (numeric) value 0 1 indicate minimum proportion QC samples feature must present included computing reference. Default qc_frac = 0. . default 0. sample_frac (numeric) value 0 1 indicate minimum proportion samples feature must present considered computing normalisation coefficients. . default 0. ref_method (character) Reference computation method. Allowed values limited following: \"mean\": reference computed mean samples matching qc_label input. \"median\": reference computed median samples matching qc_label_input. default \"mean\". ref_mean (numeric, NULL) single sample use reference normalisation. set NULL reference computed based input parameters (ref_mean, qc_label etc). . default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":" pqn_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"pqn_norm object inherits following struct classes: pqn_norm() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"M = pqn_norm(       qc_label = \"QC\",       factor_name = \"V1\",       qc_frac = 0,       sample_frac = 0,       ref_mean = NULL,       ref_method = \"mean\")  D = iris_DatasetExperiment() M = pqn_norm(factor_name='Species',qc_label='all') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PQN coefficient histogram — pqn_norm_hist","title":"PQN coefficient histogram — pqn_norm_hist","text":"histogram PQN coefficients features","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"pqn_norm_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PQN coefficient histogram — pqn_norm_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PQN coefficient histogram — pqn_norm_hist","text":"  pqn_norm_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PQN coefficient histogram — pqn_norm_hist","text":"pqn_norm_hist object inherits following struct classes: pqn_norm_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"M = pqn_norm_hist()  C = pqn_norm_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher's exact test for missing values — prop_na","title":"Fisher's exact test for missing values — prop_na","text":"Fisher's exact test used compare number missing values group. Multiple test corrected p-values computed indicate whether significant difference number missing values across groups feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher's exact test for missing values — prop_na","text":"","code":"prop_na(alpha = 0.05, mtc = \"fdr\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher's exact test for missing values — prop_na","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher's exact test for missing values — prop_na","text":" prop_na object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fisher's exact test for missing values — prop_na","text":"prop_na object inherits following struct classes: prop_na() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher's exact test for missing values — prop_na","text":"","code":"M = prop_na(       alpha = 0.05,       mtc = \"fdr\",       factor_name = \"V1\")  M = prop_na(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of determination (R-squared) — r_squared","title":"Coefficient of determination (R-squared) — r_squared","text":"R-squared metric used assess goodness fit regression models. measures much variance one variable can explained another variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"r_squared(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of determination (R-squared) — r_squared","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of determination (R-squared) — r_squared","text":"  r_squared  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Coefficient of determination (R-squared) — r_squared","text":"r_squared object inherits following struct classes: r_squared() ⭢ metric() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"M = r_squared()  MET = r_squared()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Data resampling — resample","title":"Data resampling — resample","text":"New training sets generated original data selecting samples random. can based levels factor whole dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data resampling — resample","text":"","code":"resample(   number_of_iterations = 10,   method = \"split_data\",   factor_name,   p_train = 0.8,   collect = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data resampling — resample","text":"number_of_iterations (numeric, integer) number training sets generate. default 10. method (character) Resampling method. Allowed values limited following: \"split_data\": Samples training set selected random full dataset. \"stratified_split\": Samples training set randomly selected level chosen factor. \"equal_split\": Samples training set selected random level main factor group sizes equal. default \"split_data\". factor_name (character) name sample-meta column use. p_train (numeric) proportion samples selected training set. default 0.8. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Data resampling — resample","text":" resample object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Data resampling — resample","text":"resample object inherits following struct classes: resample() ⭢ resampler() ⭢ iterator() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data resampling — resample","text":"","code":"M = resample(       number_of_iterations = 100,       method = \"split_data\",       factor_name = \"V1\",       p_train = 0.75,       collect = NULL)  I = resample(     number_of_iterations = 10,      factor_name = 'Species',      method = 'split_data',     p_train = 0.8)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"resample_chart class — resample_chart","title":"resample_chart class — resample_chart","text":"Plots results resampling.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"resample_chart class — resample_chart","text":"","code":"resample_chart(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"resample_chart class — resample_chart","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"resample_chart class — resample_chart","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"resample_chart class — resample_chart","text":"","code":"C = resample_chart(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD filter — rsd_filter","title":"RSD filter — rsd_filter","text":"RSD filter calculates relative standard deviation (ratio standard deviation mean) features. feature RSD greater predefined threshold excluded.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD filter — rsd_filter","text":"","code":"rsd_filter(rsd_threshold = 20, qc_label = \"QC\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD filter — rsd_filter","text":"rsd_threshold (numeric) RSD threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD filter — rsd_filter","text":" rsd_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RSD filter — rsd_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"RSD filter — rsd_filter","text":"rsd_filter object inherits following struct classes: rsd_filter() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RSD filter — rsd_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD filter — rsd_filter","text":"","code":"M = rsd_filter(       rsd_threshold = 20,       qc_label = \"QC\",       factor_name = \"V1\")  M = rsd_filter(factor_name='Class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD histogram — rsd_filter_hist","title":"RSD histogram — rsd_filter_hist","text":"histogram calculated RSD values.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD histogram — rsd_filter_hist","text":"","code":"rsd_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD histogram — rsd_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD histogram — rsd_filter_hist","text":"  rsd_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"RSD histogram — rsd_filter_hist","text":"rsd_filter_hist object inherits following struct classes: rsd_filter_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD histogram — rsd_filter_hist","text":"","code":"M = rsd_filter_hist()  C = rsd_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Running iterator apply iterator number times DatasetExperiment. example, cross-validation model applied multiple times data, splitting training test sets. input metric object can calculated collected iteration output.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"# S4 method for bootstrap,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for forward_selection_by_rank,DatasetExperiment,metric run(I, D, MET)  # S4 method for grid_search_1d,DatasetExperiment,metric run(I, D, MET)  # S4 method for kfold_xval,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for permutation_test,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for permute_sample_order,DatasetExperiment,metric run(I, D, MET)  # S4 method for resample,DatasetExperiment,metric run(I, D, MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"iterator object D DatasetExperiment object MET metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Modified iterator object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"D = iris_DatasetExperiment() # get some data MET = metric()  # use a metric I = example_iterator() # initialise iterator models(I) = example_model() # set the model I = run(I,D,MET) # run #> Warning: no calculation provided for this metric"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Signal/batch correction for mass spectrometry data — sb_corr","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Applies Quality Control Robust Spline (QC-RSC) method correct signal drift batch differences mass spectrometry data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"sb_corr(   order_col,   batch_col,   qc_col,   smooth = 0,   use_log = TRUE,   min_qc = 4,   qc_label = \"QC\",   spar_lim = c(-1.5, 1.5),   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"order_col (character) column name sample_meta indicating run order samples. batch_col (character) column name sample_meta indicating batch sample measured . qc_col (character) column name sample_meta indicating group sample member . smooth (numeric) amount smoothing applied (0 1). set 0 smoothing parameter estimated using leave-one-cross-validation. default 0. use_log (logical) Log tranformation. Allowed values limited following: \"TRUE\": data log transformed prior performing signal correction. \"FALSE\": Signal correction applied input data. default TRUE. min_qc (numeric) minimum number QC samples required signal correction. default 4. qc_label (character) label used identify QC samples. default \"QC\". spar_lim (numeric) two element vector specifying upper lower limits spar = 0. Allows value spar constrained within limits prevent overfitting. default c(-1.5, 1.5). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":" sb_corr object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"sb_corr object inherits following struct classes: sb_corr() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp. Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013). \"Characterising correcting batch variation automated direct infusion mass spectrometry (DIMS) metabolomics workflow.\" Analytical Bioanalytical Chemistry, 405(15), 5147-5157.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"M = sb_corr(       order_col = character(0),       batch_col = character(0),       qc_col = character(0),       smooth = 0,       use_log = FALSE,       min_qc = 4,       qc_label = \"QC\",       spar_lim = c(-1.5, 1.5))  M = sb_corr(order_col='run_order',batch_col='batch_no',qc_col='class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Group scatter chart — scatter_chart","title":"Group scatter chart — scatter_chart","text":"Plots 2d scatter plot input data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group scatter chart — scatter_chart","text":"","code":"scatter_chart(   xcol = 1,   ycol = 2,   points_to_label = \"none\",   factor_name = \"none\",   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group scatter chart — scatter_chart","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default 1. ycol (numeric, integer, character) column name, index, data plot y-axis. default 2. points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. default \"none\". ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group scatter chart — scatter_chart","text":"  scatter_chart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Group scatter chart — scatter_chart","text":"scatter_chart object inherits following struct classes: scatter_chart() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group scatter chart — scatter_chart","text":"","code":"M = scatter_chart(       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() C = scatter_chart(         xcol = 'Petal.Width',         ycol = 'Sepal.Width',         factor_name = 'Species'     ) chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split data — split_data","title":"Split data — split_data","text":"data matrix divided two subsets.predefined proportion samples randomly selected training set, remaining samples used test set.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split data — split_data","text":"","code":"split_data(p_train, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split data — split_data","text":"p_train (numeric) proportion samples selected training set. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split data — split_data","text":" split_data object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Split data — split_data","text":"split_data object inherits following struct classes: split_data() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split data — split_data","text":"","code":"M = split_data(       p_train = 0.75)  M = split_data(p_train=0.75)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Stratified sampling — stratified_split","title":"Stratified sampling — stratified_split","text":"dataset divided two subsets. predefined proportion samples level factor selected training set, remaining samples used test set. stratification factor level means relative number samples per level approximately equal original dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stratified sampling — stratified_split","text":"","code":"stratified_split(p_train, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stratified sampling — stratified_split","text":"p_train (numeric) proportion samples selected training set. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stratified sampling — stratified_split","text":" stratified_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Stratified sampling — stratified_split","text":"stratified_split object inherits following struct classes: stratified_split() ⭢ split_data() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stratified sampling — stratified_split","text":"","code":"M = stratified_split(       factor_name = \"V1\",       p_train = 0.75)  D = iris_DatasetExperiment() M = stratified_split(p_train=0.75,factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/structToolbox.html","id":null,"dir":"Reference","previous_headings":"","what":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox","title":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox","text":"package extends classes defined struct package","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"SVM scatter plot — svm_plot_2d","title":"SVM scatter plot — svm_plot_2d","text":"scatter plot input data group calculated boundary SVM model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SVM scatter plot — svm_plot_2d","text":"","code":"svm_plot_2d(factor_name, npoints = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SVM scatter plot — svm_plot_2d","text":"factor_name (character) name sample-meta column use. npoints (numeric) number grid points used plot boundary. default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SVM scatter plot — svm_plot_2d","text":"  svm_plot_2d  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SVM scatter plot — svm_plot_2d","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"SVM scatter plot — svm_plot_2d","text":"svm_plot_2d object inherits following struct classes: svm_plot_2d() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SVM scatter plot — svm_plot_2d","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, https://CRAN.R-project.org/package=e1071.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SVM scatter plot — svm_plot_2d","text":"","code":"M = svm_plot_2d(       factor_name = \"V1\",       npoints = 100)  D = iris_DatasetExperiment() M = filter_smeta(mode='exclude',levels='setosa',factor_name='Species') +     mean_centre()+PCA(number_components=2)+     SVM(factor_name='Species',kernel='linear') M = model_apply(M,D)  C = svm_plot_2d(factor_name='Species') chart_plot(C,M[4],predicted(M[3]))"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":null,"dir":"Reference","previous_headings":"","what":"tSNE — tSNE","title":"tSNE — tSNE","text":"t-Distributed Stochastic Neighbor Embedding.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"tSNE — tSNE","text":"","code":"tSNE(   dims = 2,   perplexity = 30,   max_iter = 100,   theta = 0.5,   check_duplicates = FALSE,   init = NULL,   eta = 200,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"tSNE — tSNE","text":"dims (numeric) number tSNE dimensions computed. default 2. perplexity (numeric) Perplexity parameter. default 30. max_iter (numeric) maximum number tSNE iterations. default 100. theta (numeric) Speed/accuracy trade-. value 0 gives exact tSNE. default 0.5. check_duplicates (logical) Check duplicates. Allowed values limited following: \"TRUE\": Checks presence exact duplicate samples. \"FALSE\": check exact duplicate samples. default FALSE. init (NULL, data.frame, DatasetExperiment) set coordinates initialising tSNE algorithm. NULL uses random initialisation. default NULL. eta (numeric) learning rate parameter. default 200. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"tSNE — tSNE","text":" tSNE object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"tSNE — tSNE","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"tSNE — tSNE","text":"tSNE object inherits following struct classes: tSNE() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"tSNE — tSNE","text":"Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.17, https://github.com/jkrijthe/Rtsne. van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"tSNE — tSNE","text":"","code":"M = tSNE(       dims = 2,       perplexity = 30,       max_iter = 1000,       theta = 0.5,       check_duplicates = FALSE,       init = NULL,       eta = 200)  M = tSNE()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — tSNE_scatter","title":"Feature boxplot — tSNE_scatter","text":"plots new representation data applying tSNE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — tSNE_scatter","text":"","code":"tSNE_scatter(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — tSNE_scatter","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — tSNE_scatter","text":"  tSNE_scatter  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Feature boxplot — tSNE_scatter","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature boxplot — tSNE_scatter","text":"tSNE_scatter object inherits following struct classes: tSNE_scatter() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Feature boxplot — tSNE_scatter","text":"Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.17, https://github.com/jkrijthe/Rtsne. van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — tSNE_scatter","text":"","code":"M = tSNE_scatter(       factor_name = \"V1\")  M = tSNE_scatter(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Total Ion Count chart. — tic_chart","title":"Total Ion Count chart. — tic_chart","text":"scatter plot Total Ion Count (sum sample) versus run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total Ion Count chart. — tic_chart","text":"","code":"tic_chart(run_order, factor_name, connected = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total Ion Count chart. — tic_chart","text":"run_order (character) column name sample_meta indicating run order samples. factor_name (character) name sample-meta column use. connected (logical) Plot samples connected grey line. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total Ion Count chart. — tic_chart","text":"  tic_chart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Total Ion Count chart. — tic_chart","text":"tic_chart object inherits following struct classes: tic_chart() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Total Ion Count chart. — tic_chart","text":"","code":"M = tic_chart(       factor_name = \"V1\",       run_order = character(0),       connected = FALSE)  D = iris_DatasetExperiment() D$sample_meta$run_order=1:nrow(D) C = tic_chart(factor_name='Species',run_order='run_order') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":null,"dir":"Reference","previous_headings":"","what":"t-test — ttest","title":"t-test — ttest","text":"t-test compares means two factor levels. Multiple-test corrected p-values used indicate significance computed difference features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"t-test — ttest","text":"","code":"ttest(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   equal_variance = FALSE,   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"t-test — ttest","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. paired (logical) Apply paired t-test. default FALSE. paired_factor (character) factor name encodes sample id pairing. default character(0). equal_variance (logical) Equal variance. Allowed values limited following: \"TRUE\": variance group treated equal using pooled variance estimate variance. \"FALSE\": variance group assumed equal Welch (Satterthwaite) approximation used. default FALSE. conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"t-test — ttest","text":" ttest object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"t-test — ttest","text":"ttest object inherits following struct classes: ttest() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"t-test — ttest","text":"","code":"M = ttest(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       paired = FALSE,       paired_factor = \"NA\",       equal_variance = FALSE,       conf_level = 0.95)  M = ttest(factor_name='Class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Vector normalisation — vec_norm","title":"Vector normalisation — vec_norm","text":"samples data matrix normalised account differences concentration scaling sample sum squares equal 1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vector normalisation — vec_norm","text":"","code":"vec_norm(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vector normalisation — vec_norm","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vector normalisation — vec_norm","text":" vec_norm object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Vector normalisation — vec_norm","text":"vec_norm object inherits following struct classes: vec_norm() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vector normalisation — vec_norm","text":"","code":"M = vec_norm()  M = vec_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — wilcox_p_hist","title":"Histogram of p values — wilcox_p_hist","text":"histogram p values wilcoxon signed rank test","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — wilcox_p_hist","text":"","code":"wilcox_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — wilcox_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — wilcox_p_hist","text":"  wilcox_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of p values — wilcox_p_hist","text":"wilcox_p_hist object inherits following struct classes: wilcox_p_hist() ⭢ chart() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — wilcox_p_hist","text":"","code":"M = wilcox_p_hist()  M = wilcox_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":null,"dir":"Reference","previous_headings":"","what":"wilcoxon signed rank test — wilcox_test","title":"wilcoxon signed rank test — wilcox_test","text":"Mann-Whitney-Wilcoxon signed rank test compares ,ranks values two groups. non-parametric equivalent t-test. Multiple test corrected p-values computed indicators significance variable/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"wilcox_test(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wilcoxon signed rank test — wilcox_test","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample-meta column use. paired (logical) Apply paired test. default FALSE. paired_factor (character) factor name containing sample ids paired data. default character(0). conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wilcoxon signed rank test — wilcox_test","text":" wilcox_test object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"wilcoxon signed rank test — wilcox_test","text":"wilcox_test object inherits following struct classes: wilcox_test() ⭢ model() ⭢ struct_class()","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"M = wilcox_test(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       paired = FALSE,       paired_factor = character(0),       conf_level = 0.95)  M = wilcox_test(factor_name='Class')"}]