diff --git a/404.html b/404.html
index 2921b10..ddd2720 100644
--- a/404.html
+++ b/404.html
@@ -6,71 +6,49 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Page not found (404) • arf</title>
-<!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="https://bips-hb.github.io/arf/favicon-16x16.png">
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 <link href="https://bips-hb.github.io/arf/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="https://bips-hb.github.io/arf/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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="https://bips-hb.github.io/arf/pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
+<script src="https://bips-hb.github.io/arf/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://bips-hb.github.io/arf/deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
+<link href="https://bips-hb.github.io/arf/deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="https://bips-hb.github.io/arf/deps/headroom-0.11.0/headroom.min.js"></script><script src="https://bips-hb.github.io/arf/deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="https://bips-hb.github.io/arf/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="https://bips-hb.github.io/arf/deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="https://bips-hb.github.io/arf/deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="https://bips-hb.github.io/arf/deps/search-1.0.0/fuse.min.js"></script><script src="https://bips-hb.github.io/arf/deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="https://bips-hb.github.io/arf/pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
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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]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
 </head>
 <body>
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-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light" data-bs-theme="light"><div class="container">
-    
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="https://bips-hb.github.io/arf/index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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     <div id="navbar" class="collapse navbar-collapse ms-3">
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-  <a class="nav-link" href="https://bips-hb.github.io/arf/articles/arf.html">Get started</a>
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-<li class="nav-item">
-  <a class="nav-link" href="https://bips-hb.github.io/arf/reference/index.html">Reference</a>
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-<li class="nav-item">
-  <a class="nav-link" href="https://bips-hb.github.io/arf/news/index.html">Changelog</a>
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+<li class="nav-item"><a class="nav-link" href="https://bips-hb.github.io/arf/articles/arf.html">Get started</a></li>
+<li class="nav-item"><a class="nav-link" href="https://bips-hb.github.io/arf/reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="https://bips-hb.github.io/arf/news/index.html">Changelog</a></li>
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-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
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-  <a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="github">
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-     
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+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </div>
 
-    
+
   </div>
 </nav><div class="container template-title-body">
 <div class="row">
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-      
+
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 Content not found. Please use links in the navbar.
@@ -84,15 +62,15 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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 </div>
 
-  
 
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diff --git a/LICENSE-text.html b/LICENSE-text.html
index dd082b4..d53d4cb 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -1,50 +1,35 @@
 <!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 • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
+<!-- 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 • arf</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><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light" data-bs-theme="light"><div class="container">
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+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="nav-item">
-  <a class="nav-link" href="articles/arf.html">Get started</a>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="reference/index.html">Reference</a>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="articles/arf.html">Get started</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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+
   </div>
 </nav><div class="container template-title-body">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="logo.png" class="logo" alt=""><h1>License</h1>
-      
+
     </div>
 
 <pre>                    GNU GENERAL PUBLIC LICENSE
@@ -731,14 +716,14 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/articles/arf.html b/articles/arf.html
index 7edfadd..221cf6d 100644
--- a/articles/arf.html
+++ b/articles/arf.html
@@ -5,68 +5,43 @@
 <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">
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 <title>Adversarial Random Forests • arf</title>
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 <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">
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@@ -78,7 +53,7 @@
       <img src="../logo.png" class="logo" alt=""><h1>Adversarial Random Forests</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/vignettes/arf.Rmd" class="external-link"><code>vignettes/arf.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/vignettes/arf.Rmd" class="external-link"><code>vignettes/arf.Rmd</code></a></small>
       <div class="d-none name"><code>arf.Rmd</code></div>
     </div>
 
@@ -93,13 +68,14 @@ <h2 id="adversarial-training">Adversarial Training<a class="anchor" aria-label="
 <p>The ARF algorithm is an iterative procedure. In the first instance,
 we generate synthetic data by independently sampling from the marginals
 of each feature and training a random forest (RF) to distinguish
-original from synthetic samples. If accuracy is greater than <span class="math inline">\(0.5 + \delta\)</span> (where <code>delta</code> is
-a user-controlled tolerance parameter, generally set to 0), we create a
-new dataset by sampling from the marginals within each leaf and training
-another RF classifier. The procedure repeats until original and
-synthetic samples cannot be reliably distinguished. With the default
-<code>verbose = TRUE</code>, the algorithm will print accuracy at each
-iteration.</p>
+original from synthetic samples. If accuracy is greater than
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0.5</mn><mo>+</mo><mi>δ</mi></mrow><annotation encoding="application/x-tex">0.5 + \delta</annotation></semantics></math>
+(where <code>delta</code> is a user-controlled tolerance parameter,
+generally set to 0), we create a new dataset by sampling from the
+marginals within each leaf and training another RF classifier. The
+procedure repeats until original and synthetic samples cannot be
+reliably distinguished. With the default <code>verbose = TRUE</code>,
+the algorithm will print accuracy at each iteration.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Load libraries</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/bips-hb/arf" class="external-link">arf</a></span><span class="op">)</span></span>
@@ -111,8 +87,8 @@ <h2 id="adversarial-training">Adversarial Training<a class="anchor" aria-label="
 <span></span>
 <span><span class="co"># Train ARF</span></span>
 <span><span class="va">arf_iris</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Iteration: 0, Accuracy: 85.52%</span></span>
-<span><span class="co">#&gt; Iteration: 1, Accuracy: 42.33%</span></span>
+<span><span class="co">#&gt; Iteration: 0, Accuracy: 76.09%</span></span>
+<span><span class="co">#&gt; Iteration: 1, Accuracy: 39.67%</span></span>
 <span><span class="co">#&gt; Warning: executing %dopar% sequentially: no parallel backend registered</span></span></code></pre></div>
 <p>The printouts can be turned off by setting
 <code>verbose = FALSE</code>. Accuracy is still stored within the
@@ -136,9 +112,11 @@ <h2 id="adversarial-training">Adversarial Training<a class="anchor" aria-label="
 as desired. If the ARF has converged, then resulting splits should form
 fully factorized leaves, i.e. subregions of the feature space where
 variables are locally independent.</p>
-<p>ARF convergence is asymptotically guaranteed as <span class="math inline">\(n \rightarrow \infty\)</span> (see <a href="https://proceedings.mlr.press/v206/watson23a.html" class="external-link">Watson et al.,
-2023</a>, Thm. 1). However, this has no implications for finite sample
-performance. In practice, we often find that adversarial training
+<p>ARF convergence is asymptotically guaranteed as
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>→</mo><mi>∞</mi></mrow><annotation encoding="application/x-tex">n \rightarrow \infty</annotation></semantics></math>
+(see <a href="https://proceedings.mlr.press/v206/watson23a.html" class="external-link">Watson
+et al., 2023</a>, Thm. 1). However, this has no implications for finite
+sample performance. In practice, we often find that adversarial training
 completes in just one or two rounds, but this may not hold for some
 datasets. To avoid infinite loops, users can increase the slack
 parameter <code>delta</code> or set the <code>max_iters</code> argument
@@ -173,8 +151,8 @@ <h2 id="adversarial-training">Adversarial Training<a class="anchor" aria-label="
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Rerun ARF, now in parallel and with more trees</span></span>
 <span><span class="va">arf_iris</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">iris</span>, num_trees <span class="op">=</span> <span class="fl">100</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Iteration: 0, Accuracy: 91.33%</span></span>
-<span><span class="co">#&gt; Iteration: 1, Accuracy: 33%</span></span></code></pre></div>
+<span><span class="co">#&gt; Iteration: 0, Accuracy: 85.67%</span></span>
+<span><span class="co">#&gt; Iteration: 1, Accuracy: 39.67%</span></span></code></pre></div>
 <p>The result is an object of class <code>ranger</code>, which we can
 input to downstream functions.</p>
 </div>
@@ -185,9 +163,16 @@ <h2 id="parameter-learning">Parameter Learning<a class="anchor" aria-label="anch
 forests for density estimation (FORDE). This function calculates the
 coverage, bounds, and pdf/pmf parameters for every variable in every
 leaf. This can be an expensive computation for large datasets, as it
-requires <span class="math inline">\(\mathcal{O}\big(B \cdot d \cdot n
-\cdot \log(n)\big)\)</span> operations, where <span class="math inline">\(B\)</span> is the number of trees, <span class="math inline">\(d\)</span> is the data dimensionality, and <span class="math inline">\(n\)</span> is the sample size. Once again, the
-process is parallelized by default.</p>
+requires
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝒪</mi><mo minsize="1.2" maxsize="1.2" stretchy="false" form="prefix">(</mo><mi>B</mi><mo>⋅</mo><mi>d</mi><mo>⋅</mo><mi>n</mi><mo>⋅</mo><mo>log</mo><mrow><mo stretchy="true" form="prefix">(</mo><mi>n</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo minsize="1.2" maxsize="1.2" stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{O}\big(B \cdot d \cdot n \cdot \log(n)\big)</annotation></semantics></math>
+operations, where
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>B</mi><annotation encoding="application/x-tex">B</annotation></semantics></math>
+is the number of trees,
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>d</mi><annotation encoding="application/x-tex">d</annotation></semantics></math>
+is the data dimensionality, and
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>n</mi><annotation encoding="application/x-tex">n</annotation></semantics></math>
+is the sample size. Once again, the process is parallelized by
+default.</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Compute leaf and distribution parameters</span></span>
 <span><span class="va">params_iris</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/forde.html">forde</a></span><span class="op">(</span><span class="va">arf_iris</span>, <span class="va">iris</span><span class="op">)</span></span></code></pre></div>
@@ -219,11 +204,12 @@ <h2 id="parameter-learning">Parameter Learning<a class="anchor" aria-label="anch
 other words, we impose a flat Dirichlet prior and report posterior
 probabilities rather than maximum likelihood estimates. The latter is a
 slack parameter on empirical bounds that expands the estimated extrema
-for continuous features by a factor of <span class="math inline">\(1 +
-\epsilon\)</span>.</p>
+for continuous features by a factor of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>1</mn><mo>+</mo><mi>ϵ</mi></mrow><annotation encoding="application/x-tex">1 + \epsilon</annotation></semantics></math>.</p>
 <p>Compare the results of our original probability estimates for the
 <code>Species</code> variable with those obtained by adding a
-pseudocount of <span class="math inline">\(\alpha = 0.1\)</span>.</p>
+pseudocount of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mn>0.1</mn></mrow><annotation encoding="application/x-tex">\alpha = 0.1</annotation></semantics></math>.</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Recompute with additive smoothing</span></span>
 <span><span class="va">params_alpha</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/forde.html">forde</a></span><span class="op">(</span><span class="va">arf_iris</span>, <span class="va">iris</span>, alpha <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
@@ -234,90 +220,88 @@ <h2 id="parameter-learning">Parameter Learning<a class="anchor" aria-label="anch
 <span><span class="co">#&gt;    f_idx variable       val  prob NA_share</span></span>
 <span><span class="co">#&gt;    &lt;int&gt;   &lt;char&gt;    &lt;char&gt; &lt;num&gt;    &lt;num&gt;</span></span>
 <span><span class="co">#&gt; 1:     1  Species virginica     1        0</span></span>
-<span><span class="co">#&gt; 2:     2  Species    setosa     1        0</span></span>
-<span><span class="co">#&gt; 3:     3  Species    setosa     1        0</span></span>
+<span><span class="co">#&gt; 2:     2  Species virginica     1        0</span></span>
+<span><span class="co">#&gt; 3:     3  Species virginica     1        0</span></span>
 <span><span class="co">#&gt; 4:     4  Species virginica     1        0</span></span>
 <span><span class="co">#&gt; 5:     5  Species    setosa     1        0</span></span>
-<span><span class="co">#&gt; 6:     6  Species    setosa     1        0</span></span></code></pre></div>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">params_alpha</span><span class="op">$</span><span class="va">cat</span><span class="op">)</span></span>
+<span><span class="co">#&gt; 6:     6  Species    setosa     1        0</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">params_alpha</span><span class="op">$</span><span class="va">cat</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Key: &lt;f_idx, variable&gt;</span></span>
 <span><span class="co">#&gt;    f_idx variable        val       prob NA_share</span></span>
 <span><span class="co">#&gt;    &lt;int&gt;   &lt;char&gt;     &lt;char&gt;      &lt;num&gt;    &lt;num&gt;</span></span>
 <span><span class="co">#&gt; 1:     1  Species  virginica 0.93939394        0</span></span>
 <span><span class="co">#&gt; 2:     1  Species     setosa 0.03030303        0</span></span>
 <span><span class="co">#&gt; 3:     1  Species versicolor 0.03030303        0</span></span>
-<span><span class="co">#&gt; 4:     2  Species  virginica 0.03030303        0</span></span>
-<span><span class="co">#&gt; 5:     2  Species     setosa 0.93939394        0</span></span>
-<span><span class="co">#&gt; 6:     2  Species versicolor 0.03030303        0</span></span></code></pre></div>
+<span><span class="co">#&gt; 4:     2  Species  virginica 0.91304348        0</span></span>
+<span><span class="co">#&gt; 5:     2  Species     setosa 0.04347826        0</span></span>
+<span><span class="co">#&gt; 6:     2  Species versicolor 0.04347826        0</span></span></code></pre></div>
 <p>Under Laplace smoothing, extreme probabilities only occur when the
 splits explicitly demand it. Otherwise, all values shrink toward a
 uniform prior. Note that these two data tables may not have exactly the
 same rows, as we omit zero probability events to conserve memory.
 However, we can verify that probabilities sum to unity for each
 leaf-variable combination.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Sum probabilities</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">params_iris</span><span class="op">$</span><span class="va">cat</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">prob</span><span class="op">)</span>, by <span class="op">=</span> <span class="fu">.</span><span class="op">(</span><span class="va">f_idx</span>, <span class="va">variable</span><span class="op">)</span><span class="op">]</span><span class="op">$</span><span class="va">V1</span><span class="op">)</span></span>
 <span><span class="co">#&gt;    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. </span></span>
-<span><span class="co">#&gt;       1       1       1       1       1       1</span></span></code></pre></div>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">params_alpha</span><span class="op">$</span><span class="va">cat</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">prob</span><span class="op">)</span>, by <span class="op">=</span> <span class="fu">.</span><span class="op">(</span><span class="va">f_idx</span>, <span class="va">variable</span><span class="op">)</span><span class="op">]</span><span class="op">$</span><span class="va">V1</span><span class="op">)</span></span>
+<span><span class="co">#&gt;       1       1       1       1       1       1</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">params_alpha</span><span class="op">$</span><span class="va">cat</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">prob</span><span class="op">)</span>, by <span class="op">=</span> <span class="fu">.</span><span class="op">(</span><span class="va">f_idx</span>, <span class="va">variable</span><span class="op">)</span><span class="op">]</span><span class="op">$</span><span class="va">V1</span><span class="op">)</span></span>
 <span><span class="co">#&gt;    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. </span></span>
 <span><span class="co">#&gt;       1       1       1       1       1       1</span></span></code></pre></div>
 <p>The <code>forde</code> function outputs a list of length 6, with
 entries for (1) continuous features; (2) categorical features; (3) leaf
 parameters; (4) variable metadata; (5) factor levels; and (6) data input
 class.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">params_iris</span></span>
 <span><span class="co">#&gt; $cnt</span></span>
 <span><span class="co">#&gt; Key: &lt;f_idx, variable&gt;</span></span>
-<span><span class="co">#&gt;       f_idx     variable   min   max       mu      sigma NA_share</span></span>
-<span><span class="co">#&gt;       &lt;int&gt;       &lt;char&gt; &lt;num&gt; &lt;num&gt;    &lt;num&gt;      &lt;num&gt;    &lt;num&gt;</span></span>
-<span><span class="co">#&gt;    1:     1 Petal.Length  -Inf   Inf 5.933333 0.20816660        0</span></span>
-<span><span class="co">#&gt;    2:     1  Petal.Width  2.45   Inf 2.500000 0.01041469        0</span></span>
-<span><span class="co">#&gt;    3:     1 Sepal.Length  -Inf   Inf 6.733333 0.45092498        0</span></span>
-<span><span class="co">#&gt;    4:     1  Sepal.Width  -Inf   Inf 3.400000 0.17320508        0</span></span>
-<span><span class="co">#&gt;    5:     2 Petal.Length  -Inf   Inf 1.466667 0.05773503        0</span></span>
-<span><span class="co">#&gt;   ---                                                            </span></span>
-<span><span class="co">#&gt; 8468:  2117  Sepal.Width  3.30  3.45 3.400000 0.02209289        0</span></span>
-<span><span class="co">#&gt; 8469:  2118 Petal.Length  1.35  1.45 1.400000 0.01803877        0</span></span>
-<span><span class="co">#&gt; 8470:  2118  Petal.Width  -Inf  0.50 0.200000 0.08164966        0</span></span>
-<span><span class="co">#&gt; 8471:  2118 Sepal.Length  4.65  5.30 4.875000 0.09574271        0</span></span>
-<span><span class="co">#&gt; 8472:  2118  Sepal.Width  2.45  3.30 3.075000 0.15000000        0</span></span>
+<span><span class="co">#&gt;        f_idx     variable   min   max       mu      sigma NA_share</span></span>
+<span><span class="co">#&gt;        &lt;int&gt;       &lt;char&gt; &lt;num&gt; &lt;num&gt;    &lt;num&gt;      &lt;num&gt;    &lt;num&gt;</span></span>
+<span><span class="co">#&gt;     1:     1 Petal.Length  -Inf   Inf 5.933333 0.20816660        0</span></span>
+<span><span class="co">#&gt;     2:     1  Petal.Width  2.45   Inf 2.500000 0.01041469        0</span></span>
+<span><span class="co">#&gt;     3:     1 Sepal.Length  -Inf   Inf 6.733333 0.45092498        0</span></span>
+<span><span class="co">#&gt;     4:     1  Sepal.Width  -Inf   Inf 3.400000 0.17320508        0</span></span>
+<span><span class="co">#&gt;     5:     2 Petal.Length  -Inf   Inf 5.250000 0.21213203        0</span></span>
+<span><span class="co">#&gt;    ---                                                            </span></span>
+<span><span class="co">#&gt; 10484:  2621  Sepal.Width  3.30  3.45 3.400000 0.02209289        0</span></span>
+<span><span class="co">#&gt; 10485:  2622 Petal.Length  -Inf  4.20 1.400000 0.10000000        0</span></span>
+<span><span class="co">#&gt; 10486:  2622  Petal.Width  -Inf  0.25 0.200000 0.03124407        0</span></span>
+<span><span class="co">#&gt; 10487:  2622 Sepal.Length  4.65  5.55 5.266667 0.20816660        0</span></span>
+<span><span class="co">#&gt; 10488:  2622  Sepal.Width  3.45  3.55 3.500000 0.02082938        0</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $cat</span></span>
 <span><span class="co">#&gt; Key: &lt;f_idx, variable&gt;</span></span>
-<span><span class="co">#&gt;       f_idx variable        val  prob NA_share</span></span>
-<span><span class="co">#&gt;       &lt;int&gt;   &lt;char&gt;     &lt;char&gt; &lt;num&gt;    &lt;num&gt;</span></span>
-<span><span class="co">#&gt;    1:     1  Species  virginica     1        0</span></span>
-<span><span class="co">#&gt;    2:     2  Species     setosa     1        0</span></span>
-<span><span class="co">#&gt;    3:     3  Species     setosa     1        0</span></span>
-<span><span class="co">#&gt;    4:     4  Species  virginica     1        0</span></span>
-<span><span class="co">#&gt;    5:     5  Species     setosa     1        0</span></span>
-<span><span class="co">#&gt;   ---                                         </span></span>
-<span><span class="co">#&gt; 2408:  2114  Species versicolor     1        0</span></span>
-<span><span class="co">#&gt; 2409:  2115  Species     setosa     1        0</span></span>
-<span><span class="co">#&gt; 2410:  2116  Species     setosa     1        0</span></span>
-<span><span class="co">#&gt; 2411:  2117  Species     setosa     1        0</span></span>
-<span><span class="co">#&gt; 2412:  2118  Species     setosa     1        0</span></span>
+<span><span class="co">#&gt;       f_idx variable        val      prob NA_share</span></span>
+<span><span class="co">#&gt;       &lt;int&gt;   &lt;char&gt;     &lt;char&gt;     &lt;num&gt;    &lt;num&gt;</span></span>
+<span><span class="co">#&gt;    1:     1  Species  virginica 1.0000000        0</span></span>
+<span><span class="co">#&gt;    2:     2  Species  virginica 1.0000000        0</span></span>
+<span><span class="co">#&gt;    3:     3  Species  virginica 1.0000000        0</span></span>
+<span><span class="co">#&gt;    4:     4  Species  virginica 1.0000000        0</span></span>
+<span><span class="co">#&gt;    5:     5  Species     setosa 1.0000000        0</span></span>
+<span><span class="co">#&gt;   ---                                             </span></span>
+<span><span class="co">#&gt; 2919:  2618  Species versicolor 0.4615385        0</span></span>
+<span><span class="co">#&gt; 2920:  2619  Species     setosa 1.0000000        0</span></span>
+<span><span class="co">#&gt; 2921:  2620  Species     setosa 1.0000000        0</span></span>
+<span><span class="co">#&gt; 2922:  2621  Species     setosa 1.0000000        0</span></span>
+<span><span class="co">#&gt; 2923:  2622  Species     setosa 1.0000000        0</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $forest</span></span>
 <span><span class="co">#&gt; Key: &lt;tree, leaf&gt;</span></span>
 <span><span class="co">#&gt;       f_idx  tree  leaf        cvg</span></span>
 <span><span class="co">#&gt;       &lt;int&gt; &lt;int&gt; &lt;int&gt;      &lt;num&gt;</span></span>
 <span><span class="co">#&gt;    1:     1     1     6 0.02000000</span></span>
-<span><span class="co">#&gt;    2:     2     1    11 0.02000000</span></span>
-<span><span class="co">#&gt;    3:     3     1    15 0.02000000</span></span>
-<span><span class="co">#&gt;    4:     4     1    25 0.05333333</span></span>
-<span><span class="co">#&gt;    5:     5     1    31 0.01333333</span></span>
+<span><span class="co">#&gt;    2:     2     1    10 0.01333333</span></span>
+<span><span class="co">#&gt;    3:     3     1    13 0.04000000</span></span>
+<span><span class="co">#&gt;    4:     4     1    16 0.03333333</span></span>
+<span><span class="co">#&gt;    5:     5     1    21 0.02000000</span></span>
 <span><span class="co">#&gt;   ---                             </span></span>
-<span><span class="co">#&gt; 2114:  2114   100    95 0.03333333</span></span>
-<span><span class="co">#&gt; 2115:  2115   100    97 0.01333333</span></span>
-<span><span class="co">#&gt; 2116:  2116   100   101 0.04000000</span></span>
-<span><span class="co">#&gt; 2117:  2117   100   103 0.04000000</span></span>
-<span><span class="co">#&gt; 2118:  2118   100   105 0.02666667</span></span>
+<span><span class="co">#&gt; 2618:  2618   100    49 0.08666667</span></span>
+<span><span class="co">#&gt; 2619:  2619   100    57 0.02000000</span></span>
+<span><span class="co">#&gt; 2620:  2620   100    58 0.04666667</span></span>
+<span><span class="co">#&gt; 2621:  2621   100    60 0.04000000</span></span>
+<span><span class="co">#&gt; 2622:  2622   100    61 0.02000000</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $meta</span></span>
 <span><span class="co">#&gt;        variable   class    family decimals</span></span>
@@ -348,24 +332,23 @@ <h2 id="likelihood-estimation">Likelihood Estimation<a class="anchor" aria-label
 to evaluate. For <em>total evidence</em> queries (i.e., those spanning
 all variables and no conditioning events), it is faster to also include
 <code>arf</code> in the function call.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Compute likelihood under truncated normal and uniform distributions</span></span>
 <span><span class="va">ll</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/lik.html">lik</a></span><span class="op">(</span><span class="va">params_iris</span>, <span class="va">iris</span>, arf <span class="op">=</span> <span class="va">arf_iris</span><span class="op">)</span></span>
 <span><span class="va">ll_unif</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/lik.html">lik</a></span><span class="op">(</span><span class="va">params_unif</span>, <span class="va">iris</span>, arf <span class="op">=</span> <span class="va">arf_iris</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Compare average negative log-likelihood (lower is better)</span></span>
 <span><span class="op">-</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll</span><span class="op">)</span></span>
-<span><span class="co">#&gt; [1] 0.5255625</span></span></code></pre></div>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="op">-</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll_unif</span><span class="op">)</span></span>
-<span><span class="co">#&gt; [1] -4.363783</span></span></code></pre></div>
+<span><span class="co">#&gt; [1] 0.3196718</span></span>
+<span><span class="op">-</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll_unif</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] -7.291452</span></span></code></pre></div>
 <p>Note that the piecewise constant estimator does considerably worse in
 this experiment.</p>
 <p>The <code>lik</code> function can also be used to compute the
 likelihood of some <em>partial state</em>, i.e. a setting in which some
 but not all variable values are specified. Let’s take a look at that
 iris dataset:</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span>
 <span><span class="co">#&gt;   Sepal.Length Sepal.Width Petal.Length Petal.Width Species</span></span>
 <span><span class="co">#&gt; 1          5.1         3.5          1.4         0.2  setosa</span></span>
@@ -378,7 +361,7 @@ <h2 id="likelihood-estimation">Likelihood Estimation<a class="anchor" aria-label
 data. That is, we provide values for the first four variables but
 exclude the fifth. In this case, the model will have to marginalize over
 <code>Species</code>:</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Compute likelihoods after marginalizing over Species</span></span>
 <span><span class="va">iris_without_species</span> <span class="op">&lt;-</span> <span class="va">iris</span><span class="op">[</span>, <span class="op">-</span><span class="fl">5</span><span class="op">]</span></span>
 <span><span class="va">ll_cnt</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/lik.html">lik</a></span><span class="op">(</span><span class="va">params_iris</span>, <span class="va">iris_without_species</span><span class="op">)</span></span>
@@ -403,20 +386,22 @@ <h2 id="likelihood-estimation">Likelihood Estimation<a class="anchor" aria-label
 classifier. Note that this only works when the dataset <code>x</code>
 passed to <code>forde</code> or <code>lik</code> is the same one used to
 train the <code>arf</code>. Recall that a sample’s probability of being
-excluded from a single tree is <span class="math inline">\(\exp(-1)
-\approx 0.368\)</span>. When using <code>oob = TRUE</code>, be sure to
-include enough trees so that every observation is likely to be OOB at
-least a few times.</p>
+excluded from a single tree is
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>exp</mo><mrow><mo stretchy="true" form="prefix">(</mo><mo>−</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>≈</mo><mn>0.368</mn></mrow><annotation encoding="application/x-tex">\exp(-1) \approx 0.368</annotation></semantics></math>.
+When using <code>oob = TRUE</code>, be sure to include enough trees so
+that every observation is likely to be OOB at least a few times.</p>
 </div>
 <div class="section level2">
 <h2 id="data-synthesis">Data Synthesis<a class="anchor" aria-label="anchor" href="#data-synthesis"></a>
 </h2>
 <p>For this experiment, we use the <code>smiley</code> simulation from
 the <code>mlbench</code> package, which allows for easy visual
-assessment. We draw a training set of <span class="math inline">\(n =
-1000\)</span> and simulate <span class="math inline">\(1000\)</span>
+assessment. We draw a training set of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>=</mo><mn>1000</mn></mrow><annotation encoding="application/x-tex">n = 1000</annotation></semantics></math>
+and simulate
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mn>1000</mn><annotation encoding="application/x-tex">1000</annotation></semantics></math>
 synthetic datapoints. Resulting data are plotted side by side.</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Simulate training data</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">mlbench</span><span class="op">)</span></span>
 <span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mlbench/man/mlbench.smiley.html" class="external-link">mlbench.smiley</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span></span>
@@ -425,10 +410,9 @@ <h2 id="data-synthesis">Data Synthesis<a class="anchor" aria-label="anchor" href
 <span></span>
 <span><span class="co"># Fit ARF</span></span>
 <span><span class="va">arf_smiley</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">x</span>, mtry <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Iteration: 0, Accuracy: 90.68%</span></span>
-<span><span class="co">#&gt; Iteration: 1, Accuracy: 38.64%</span></span></code></pre></div>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span></span>
+<span><span class="co">#&gt; Iteration: 0, Accuracy: 84.35%</span></span>
+<span><span class="co">#&gt; Iteration: 1, Accuracy: 42.29%</span></span>
+<span></span>
 <span><span class="co"># Estimate parameters</span></span>
 <span><span class="va">params_smiley</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/forde.html">forde</a></span><span class="op">(</span><span class="va">arf_smiley</span>, <span class="va">x</span><span class="op">)</span></span>
 <span></span>
@@ -440,15 +424,13 @@ <h2 id="data-synthesis">Data Synthesis<a class="anchor" aria-label="anchor" href
 <span><span class="co">#&gt; 'data.frame':    1000 obs. of  3 variables:</span></span>
 <span><span class="co">#&gt;  $ X    : num  -0.841 -0.911 -0.91 -0.743 -0.863 ...</span></span>
 <span><span class="co">#&gt;  $ Y    : num  0.874 0.926 1.051 0.918 1.157 ...</span></span>
-<span><span class="co">#&gt;  $ Class: Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...</span></span></code></pre></div>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">synth</span><span class="op">)</span></span>
+<span><span class="co">#&gt;  $ Class: Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">synth</span><span class="op">)</span></span>
 <span><span class="co">#&gt; 'data.frame':    1000 obs. of  3 variables:</span></span>
-<span><span class="co">#&gt;  $ X    : num  0.779 -0.721 0.645 0.643 0.97 ...</span></span>
-<span><span class="co">#&gt;  $ Y    : num  -0.446 0.937 -0.529 -0.569 0.983 ...</span></span>
-<span><span class="co">#&gt;  $ Class: Factor w/ 4 levels "1","2","3","4": 4 1 4 4 2 4 1 3 3 4 ...</span></span></code></pre></div>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span></span>
+<span><span class="co">#&gt;  $ X    : num  -0.3742 -0.0897 0.0322 0.0271 -0.8529 ...</span></span>
+<span><span class="co">#&gt;  $ Y    : num  -0.786 0.371 0.548 0.546 -0.247 ...</span></span>
+<span><span class="co">#&gt;  $ Class: Factor w/ 4 levels "1","2","3","4": 4 3 3 3 4 4 3 2 3 3 ...</span></span>
+<span></span>
 <span><span class="co"># Put it all together</span></span>
 <span><span class="va">x</span><span class="op">$</span><span class="va">Data</span> <span class="op">&lt;-</span> <span class="st">'Original'</span></span>
 <span><span class="va">synth</span><span class="op">$</span><span class="va">Data</span> <span class="op">&lt;-</span> <span class="st">'Synthetic'</span></span>
@@ -472,7 +454,7 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 samples from the <code>iris</code> dataset under the condition that
 <code>Species = 'setosa'</code>. There are several ways to encode
 evidence, but the simplest is to pass a partial observation.</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Compute conditional likelihoods</span></span>
 <span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">'setosa'</span><span class="op">)</span></span>
 <span><span class="va">ll_conditional</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/lik.html">lik</a></span><span class="op">(</span><span class="va">params_iris</span>, query <span class="op">=</span> <span class="va">iris_without_species</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span>
@@ -485,7 +467,7 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_continuous.html" class="external-link">scale_y_log10</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">ylab</a></span><span class="op">(</span><span class="st">'Negative Log-Likelihood'</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html" class="external-link">theme</a></span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">'none'</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: Removed 6 rows containing non-finite outside the scale range</span></span>
+<span><span class="co">#&gt; Warning: Removed 11 rows containing non-finite outside the scale range</span></span>
 <span><span class="co">#&gt; (`stat_boxplot()`).</span></span></code></pre></div>
 <p><img src="arf_files/figure-html/conditional-1.png" width="480"></p>
 <p>As expected, measurements for non-setosa samples appear much less
@@ -497,7 +479,7 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 calculate the likelihood of samples given that
 <code>Species = 'setosa'</code> and
 <code>Petal.Width &gt; 0.3</code>.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Data frame of conditioning events</span></span>
 <span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span>, </span>
 <span>                  Petal.Width <span class="op">=</span> <span class="st">"&gt;0.3"</span><span class="op">)</span></span>
@@ -505,7 +487,7 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 <span><span class="co">#&gt;   Species Petal.Width</span></span>
 <span><span class="co">#&gt; 1  setosa        &gt;0.3</span></span></code></pre></div>
 <p>We can also define intervals:</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span>, </span>
 <span>                  Petal.Width <span class="op">=</span> <span class="st">"(0.3,0.5)"</span><span class="op">)</span></span>
 <span><span class="va">evi</span></span>
@@ -513,7 +495,7 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 <span><span class="co">#&gt; 1  setosa   (0.3,0.5)</span></span></code></pre></div>
 <p>Resulting likelihoods will be computed on the condition that all
 queries are drawn from setosa flowers with petal width on the interval
-<span class="math inline">\((0.3, 0.5)\)</span>.</p>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="true" form="prefix">(</mo><mn>0.3</mn><mo>,</mo><mn>0.5</mn><mo stretchy="true" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">(0.3, 0.5)</annotation></semantics></math>.</p>
 <p>A final method for passing evidence is to directly compute a
 posterior distribution on leaves. This could be useful for particularly
 complex conditioning events for which there is currently no inbuilt
@@ -522,22 +504,22 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 columns for <code>f_idx</code> and <code>wt</code>. If the latter does
 not sum to unity, the distribution will be normalized with a
 warning.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Drawing random weights</span></span>
 <span><span class="va">evi</span> <span class="op">&lt;-</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>f_idx <span class="op">=</span> <span class="va">params_iris</span><span class="op">$</span><span class="va">forest</span><span class="op">$</span><span class="va">f_idx</span>,</span>
 <span>                  wt <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Exponential.html" class="external-link">rexp</a></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">params_iris</span><span class="op">$</span><span class="va">forest</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">evi</span><span class="op">$</span><span class="va">wt</span> <span class="op">&lt;-</span> <span class="va">evi</span><span class="op">$</span><span class="va">wt</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">evi</span><span class="op">$</span><span class="va">wt</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">evi</span><span class="op">)</span></span>
 <span><span class="co">#&gt;   f_idx           wt</span></span>
-<span><span class="co">#&gt; 1     1 6.787756e-05</span></span>
-<span><span class="co">#&gt; 2     2 2.290688e-04</span></span>
-<span><span class="co">#&gt; 3     3 7.573631e-05</span></span>
-<span><span class="co">#&gt; 4     4 2.252819e-05</span></span>
-<span><span class="co">#&gt; 5     5 5.651368e-04</span></span>
-<span><span class="co">#&gt; 6     6 5.034869e-04</span></span></code></pre></div>
+<span><span class="co">#&gt; 1     1 4.483113e-04</span></span>
+<span><span class="co">#&gt; 2     2 3.997981e-05</span></span>
+<span><span class="co">#&gt; 3     3 2.888576e-05</span></span>
+<span><span class="co">#&gt; 4     4 8.726013e-04</span></span>
+<span><span class="co">#&gt; 5     5 4.433602e-04</span></span>
+<span><span class="co">#&gt; 6     6 3.536210e-05</span></span></code></pre></div>
 <p>Each of these methods can be used for conditional sampling as
 well.</p>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Simulate class-conditional data for smiley example</span></span>
 <span><span class="va">evi</span> <span class="op">&lt;-</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>Class <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span>
 <span><span class="va">synth2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/forge.html">forge</a></span><span class="op">(</span><span class="va">params_smiley</span>, n_synth <span class="op">=</span> <span class="fl">250</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span>
@@ -554,14 +536,15 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 <p>By conditioning on <code>Class = 4</code>, we restrict our sampling
 to the smile itself, rather than eyes or nose.</p>
 <p>Computing conditional expectations is similarly straightforward.</p>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/expct.html">expct</a></span><span class="op">(</span><span class="va">params_smiley</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span>
 <span><span class="co">#&gt;             X          Y</span></span>
-<span><span class="co">#&gt; 1 -0.01207312 -0.6730464</span></span></code></pre></div>
-<p>These are the average <span class="math inline">\(X, Y\)</span>
+<span><span class="co">#&gt; 1 -0.01177414 -0.6728099</span></span></code></pre></div>
+<p>These are the average
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>X</mi><mo>,</mo><mi>Y</mi></mrow><annotation encoding="application/x-tex">X, Y</annotation></semantics></math>
 coordinates for the smile above.</p>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -572,15 +555,15 @@ <h2 id="conditioning">Conditioning<a class="anchor" aria-label="anchor" href="#c
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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 </div>
 
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diff --git a/articles/arf_files/figure-html/arf2-1.png b/articles/arf_files/figure-html/arf2-1.png
index 34ff00d..bba3a2d 100644
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diff --git a/articles/arf_files/figure-html/smiley-1.png b/articles/arf_files/figure-html/smiley-1.png
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diff --git a/articles/arf_files/figure-html/smiley2-1.png b/articles/arf_files/figure-html/smiley2-1.png
index 8fc63b7..961e022 100644
Binary files a/articles/arf_files/figure-html/smiley2-1.png and b/articles/arf_files/figure-html/smiley2-1.png differ
diff --git a/articles/index.html b/articles/index.html
index 3254f23..59ba8a6 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -1,44 +1,29 @@
 <!DOCTYPE html>
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@@ -46,9 +31,9 @@
       <img src="logo.png" class="logo" alt=""><h1>Authors and Citation</h1>
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-    <div class="section level2 citation">
+    <div class="section level2">
       <h2>Authors</h2>
-      
+
       <ul class="list-unstyled"><li>
           <p><strong>Marvin N. Wright</strong>. Author, maintainer. <a href="https://orcid.org/0000-0002-8542-6291" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
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@@ -67,25 +52,26 @@ <h2>Authors</h2>
         </li>
       </ul></div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2 id="citation">Citation</h2>
-      <p><small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
+      <p><small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
       <p>Wright M, Watson D, Blesch K, Kapar J (2024).
 <em>arf: Adversarial Random Forests</em>.
-R package version 0.2.2, 
-https://bips-hb.github.io/arf/, <a href="https://github.com/bips-hb/arf" class="external-link">https://github.com/bips-hb/arf</a>. 
+R package version 0.2.3,
+https://bips-hb.github.io/arf/, <a href="https://github.com/bips-hb/arf" class="external-link">https://github.com/bips-hb/arf</a>.
 </p>
       <pre>@Manual{,
   title = {arf: Adversarial Random Forests},
   author = {Marvin N. Wright and David S. Watson and Kristin Blesch and Jan Kapar},
   year = {2024},
-  note = {R package version 0.2.2, 
-https://bips-hb.github.io/arf/},
+  note = {R package version 0.2.3,
+    https://bips-hb.github.io/arf/},
   url = {https://github.com/bips-hb/arf},
 }</pre>
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index d944a2a..5d96aa0 100644
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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) * 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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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.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 !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .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 .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.55);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.75);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.25);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 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;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--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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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-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,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='%23052c65'%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: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--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 (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%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='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--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: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--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(13,110,253,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);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(var(--bs-border-width) * -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: #0d6efd;--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: var(--bs-tertiary-bg);--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: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;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: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%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: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--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 no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: 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: 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#198754;--bs-btn-border-color: #198754;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #198754;--bs-btn-hover-border-color: #198754;--bs-btn-focus-shadow-rgb: 25,135,84;--bs-btn-active-color: #fff;--bs-btn-active-bg: #198754;--bs-btn-active-border-color: #198754;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #198754;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #198754;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #0dcaf0;--bs-btn-border-color: #0dcaf0;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #0dcaf0;--bs-btn-hover-border-color: #0dcaf0;--bs-btn-focus-shadow-rgb: 13,202,240;--bs-btn-active-color: #000;--bs-btn-active-bg: #0dcaf0;--bs-btn-active-border-color: #0dcaf0;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #0dcaf0;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #0dcaf0;--bs-btn-bg: transparent;--bs-gradient: 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transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248,249,250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #212529;--bs-btn-border-color: #212529;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #212529;--bs-btn-hover-border-color: #212529;--bs-btn-focus-shadow-rgb: 33,37,41;--bs-btn-active-color: #fff;--bs-btn-active-bg: #212529;--bs-btn-active-border-color: #212529;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #212529;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: 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.5rem;--bs-btn-font-size:.875rem;--bs-btn-border-radius: var(--bs-border-radius-sm)}.fade{transition:opacity 0.15s linear}@media (prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing.collapse-horizontal{transition:none}}.dropup,.dropend,.dropdown,.dropstart,.dropup-center,.dropdown-center{position:relative}.dropdown-toggle{white-space:nowrap}.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid transparent;border-bottom:0;border-left:.3em solid transparent}.dropdown-toggle:empty::after{margin-left:0}.dropdown-menu{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: .5rem;--bs-dropdown-spacer: .125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: var(--bs-body-color);--bs-dropdown-bg: var(--bs-body-bg);--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-border-radius: var(--bs-border-radius);--bs-dropdown-border-width: var(--bs-border-width);--bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-divider-margin-y: .5rem;--bs-dropdown-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-dropdown-link-color: var(--bs-body-color);--bs-dropdown-link-hover-color: var(--bs-body-color);--bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #0d6efd;--bs-dropdown-link-disabled-color: var(--bs-tertiary-color);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: .25rem;--bs-dropdown-header-color: #6c757d;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: .5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);border-radius:var(--bs-dropdown-border-radius)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media (min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 768px){.dropdown-menu-md-start{--bs-position: start}.dropdown-menu-md-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-md-end{--bs-position: end}.dropdown-menu-md-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 992px){.dropdown-menu-lg-start{--bs-position: start}.dropdown-menu-lg-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-lg-end{--bs-position: end}.dropdown-menu-lg-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1200px){.dropdown-menu-xl-start{--bs-position: start}.dropdown-menu-xl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xl-end{--bs-position: end}.dropdown-menu-xl-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1400px){.dropdown-menu-xxl-start{--bs-position: start}.dropdown-menu-xxl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xxl-end{--bs-position: end}.dropdown-menu-xxl-end[data-bs-popper]{right:0;left:auto}}.dropup .dropdown-menu[data-bs-popper]{top:auto;bottom:100%;margin-top:0;margin-bottom:var(--bs-dropdown-spacer)}.dropup .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:0;border-right:.3em solid transparent;border-bottom:.3em solid;border-left:.3em solid transparent}.dropup .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-menu[data-bs-popper]{top:0;right:auto;left:100%;margin-top:0;margin-left:var(--bs-dropdown-spacer)}.dropend .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:0;border-bottom:.3em solid transparent;border-left:.3em solid}.dropend .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-toggle::after{vertical-align:0}.dropstart .dropdown-menu[data-bs-popper]{top:0;right:100%;left:auto;margin-top:0;margin-right:var(--bs-dropdown-spacer)}.dropstart .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:""}.dropstart .dropdown-toggle::after{display:none}.dropstart .dropdown-toggle::before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:.3em solid;border-bottom:.3em solid transparent}.dropstart .dropdown-toggle:empty::after{margin-left:0}.dropstart .dropdown-toggle::before{vertical-align:0}.dropdown-divider{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) 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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: 1rem;--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: var(--bs-secondary-color);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) 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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;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .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 .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.55);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.75);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.25);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.1);--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")}[data-bs-theme="dark"] .navbar-toggler-icon{--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")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--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 var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-.5 * var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-.5 * var(--bs-card-cap-padding-x));margin-bottom:calc(-1 * 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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-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,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='%23052c65'%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: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--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 (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%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='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--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: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--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(13,110,253,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);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(var(--bs-border-width) * -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: #0d6efd;--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: var(--bs-tertiary-bg);--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: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;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, ".") 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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: 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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: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%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: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--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 no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: 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: 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new file mode 100644
index 0000000..c628326
--- /dev/null
+++ b/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap Table of Contents v1.0.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
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diff --git a/deps/clipboard.js-2.0.11/clipboard.min.js b/deps/clipboard.js-2.0.11/clipboard.min.js
new file mode 100644
index 0000000..1103f81
--- /dev/null
+++ b/deps/clipboard.js-2.0.11/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
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n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/deps/data-deps.txt b/deps/data-deps.txt
index 693b82d..8aa49e2 100644
--- a/deps/data-deps.txt
+++ b/deps/data-deps.txt
@@ -2,3 +2,12 @@
 <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>
+<link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet" />
+<script src="deps/headroom-0.11.0/headroom.min.js"></script>
+<script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
+<script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
+<script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script>
+<script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script>
+<script src="deps/search-1.0.0/fuse.min.js"></script>
+<script src="deps/search-1.0.0/mark.min.js"></script>
diff --git a/deps/font-awesome-6.4.2/css/all.css b/deps/font-awesome-6.4.2/css/all.css
new file mode 100644
index 0000000..bdb6e3a
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/all.css
@@ -0,0 +1,7968 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, none));
+          transform: rotate(var(--fa-rotate-angle, none)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
+.fa-paste::before {
+  content: "\f0ea"; }
+
+.fa-file-clipboard::before {
+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
+
+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
+.fa-blog::before {
+  content: "\f781"; }
+
+.fa-user-ninja::before {
+  content: "\f504"; }
+
+.fa-person-arrow-up-from-line::before {
+  content: "\e539"; }
+
+.fa-scroll-torah::before {
+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
+
+.fa-broom-ball::before {
+  content: "\f458"; }
+
+.fa-quidditch::before {
+  content: "\f458"; }
+
+.fa-quidditch-broom-ball::before {
+  content: "\f458"; }
+
+.fa-toggle-off::before {
+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
+.fa-arrow-down-9-1::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-desc::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-down-alt::before {
+  content: "\f886"; }
+
+.fa-face-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
+
+.fa-trowel-bricks::before {
+  content: "\e58a"; }
+
+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
+
+.fa-hospital-user::before {
+  content: "\f80d"; }
+
+.fa-tent-arrow-left-right::before {
+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
+.fa-square-caret-down::before {
+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
+
+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
+.fa-chevron-circle-down::before {
+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
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+  content: "\55"; }
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+  content: "\f048"; }
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+  content: "\f048"; }
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+  content: "\f482"; }
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+  content: "\e005"; }
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+  content: "\f432"; }
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+  content: "\53"; }
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+  content: "\f11c"; }
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+  content: "\f0d7"; }
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+  content: "\f7f2"; }
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+  content: "\f7f2"; }
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+  content: "\f2c8"; }
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+  content: "\f2c8"; }
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+  content: "\f2c8"; }
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+  content: "\f2c8"; }
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+  content: "\f3cf"; }
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+  content: "\f3cf"; }
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+  content: "\e22d"; }
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+  content: "\f4d3"; }
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+  content: "\f242"; }
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+  content: "\f242"; }
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+  content: "\e52e"; }
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+  content: "\f51e"; }
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+  content: "\f66d"; }
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+  content: "\f1de"; }
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+  content: "\f1de"; }
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+  content: "\f802"; }
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+  content: "\f6ff"; }
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+  content: "\f276"; }
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+  content: "\f665"; }
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+  content: "\e3f5"; }
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+  content: "\f0c3"; }
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+  content: "\e31e"; }
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+  content: "\f72b"; }
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+  content: "\f142"; }
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+  content: "\f142"; }
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+  content: "\f145"; }
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+  content: "\f011"; }
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+  content: "\f30b"; }
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+  content: "\f30b"; }
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+  content: "\f74d"; }
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+  content: "\e51d"; }
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+  content: "\f1e4"; }
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+  content: "\f1e4"; }
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+  content: "\e476"; }
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+  content: "\e54e"; }
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+  content: "\e512"; }
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+  content: "\f20a"; }
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+  content: "\f6ec"; }
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+  content: "\f6ec"; }
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+  content: "\f226"; }
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+  content: "\f302"; }
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+  content: "\f1ec"; }
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+  content: "\e535"; }
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+  content: "\4e"; }
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+  content: "\f7da"; }
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+  content: "\f7da"; }
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+  content: "\f73d"; }
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+  content: "\e4d4"; }
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+  content: "\f21a"; }
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+  content: "\f019"; }
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+  content: "\f580"; }
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+  content: "\f580"; }
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+  content: "\f55a"; }
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+  content: "\f55a"; }
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+  content: "\f1fb"; }
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+  content: "\f1fb"; }
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+  content: "\f1fb"; }
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+  content: "\e5a0"; }
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+  content: "\f04e"; }
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+  content: "\f3ce"; }
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+  content: "\f3ce"; }
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+  content: "\f3ce"; }
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+  content: "\f11a"; }
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+  content: "\f11a"; }
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+  content: "\f037"; }
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+  content: "\f6b7"; }
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+  content: "\f6b7"; }
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+  content: "\f2c2"; }
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+  content: "\f2c2"; }
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+  content: "\f03b"; }
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+  content: "\f03b"; }
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+  content: "\e4fe"; }
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+  content: "\f015"; }
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+  content: "\f015"; }
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+  content: "\f015"; }
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+  content: "\f015"; }
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+  content: "\f784"; }
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+  content: "\f812"; }
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+  content: "\42"; }
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+  content: "\f477"; }
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+  content: "\f525"; }
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+  content: "\f535"; }
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+  content: "\f0ec"; }
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+  content: "\f0ec"; }
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+  content: "\f2f9"; }
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+  content: "\f2f9"; }
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+  content: "\f2f9"; }
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+  content: "\f2e7"; }
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+  content: "\f2e7"; }
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+  content: "\f161"; }
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+  content: "\f161"; }
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+  content: "\e1ed"; }
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+  content: "\e2eb"; }
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+  content: "\f54c"; }
+
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+  content: "\f519"; }
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+  content: "\f519"; }
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+  content: "\f63c"; }
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+  content: "\f30c"; }
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+  content: "\f30c"; }
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+  content: "\f04d"; }
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+  content: "\f387"; }
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+  content: "\f093"; }
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+  content: "\f751"; }
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+  content: "\e52d"; }
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+  content: "\e583"; }
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+  content: "\f51f"; }
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+  content: "\f56d"; }
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+  content: "\f56d"; }
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+  content: "\f8ff"; }
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+  content: "\e572"; }
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+  content: "\f0e7"; }
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+  content: "\f0e7"; }
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+  content: "\e4f4"; }
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+.fa-oil-well::before {
+  content: "\e532"; }
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+.fa-vault::before {
+  content: "\e2c5"; }
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+.fa-mars::before {
+  content: "\f222"; }
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+.fa-toilet::before {
+  content: "\f7d8"; }
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+  content: "\e557"; }
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+  content: "\f157"; }
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+  content: "\f157"; }
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+  content: "\f157"; }
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+  content: "\f157"; }
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+  content: "\f157"; }
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+  content: "\f158"; }
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+  content: "\f158"; }
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+  content: "\f158"; }
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+  content: "\f158"; }
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+  content: "\f185"; }
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+  content: "\f7a6"; }
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+  content: "\f59c"; }
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+  content: "\f59c"; }
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+  content: "\f7ab"; }
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+  content: "\e4c3"; }
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+  content: "\f275"; }
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+  content: "\f358"; }
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+  content: "\f358"; }
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+  content: "\e4c1"; }
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+  content: "\e184"; }
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+  content: "\f884"; }
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+  content: "\f884"; }
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+  content: "\f884"; }
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+  content: "\3c"; }
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+  content: "\f107"; }
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+  content: "\e4de"; }
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+  content: "\e061"; }
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+  content: "\f7a4"; }
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+  content: "\f165"; }
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+  content: "\f502"; }
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+  content: "\f178"; }
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+  content: "\f178"; }
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+  content: "\e4ac"; }
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+  content: "\f141"; }
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+  content: "\f141"; }
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+  content: "\f443"; }
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+.fa-kit-medical::before {
+  content: "\f479"; }
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+.fa-first-aid::before {
+  content: "\f479"; }
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+  content: "\e5a9"; }
+
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+  content: "\f552"; }
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+  content: "\e4fb"; }
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+  content: "\f188"; }
+
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+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
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+  content: "\e4f7"; }
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+  content: "\f5da"; }
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+  content: "\f5da"; }
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+  content: "\e52f"; }
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+  content: "\e4ba"; }
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+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
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+  content: "\e5a1"; }
+
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+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
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+.fa-square-h::before {
+  content: "\f0fd"; }
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+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
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+  content: "\e560"; }
+
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+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
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+  content: "\f5aa"; }
+
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+  content: "\f4c4"; }
+
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+  content: "\f4c4"; }
+
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+  content: "\f3c5"; }
+
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+  content: "\f3c5"; }
+
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+  content: "\f15b"; }
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+  content: "\3e"; }
+
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+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
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+  content: "\f063"; }
+
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+  content: "\f043"; }
+
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+  content: "\f043"; }
+
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+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
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+  content: "\f447"; }
+
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+  content: "\f550"; }
+
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+  content: "\f550"; }
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+  content: "\f550"; }
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+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
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+  content: "\f29d"; }
+
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+  content: "\f569"; }
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+  content: "\f810"; }
+
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+  content: "\e4fc"; }
+
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+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
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+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
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+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
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+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
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+  content: "\f2bd"; }
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+.fa-user-circle::before {
+  content: "\f2bd"; }
+
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+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
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+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
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+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
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+  content: "\f042"; }
+
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+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
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+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
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+  content: "\f00d"; }
+
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+  content: "\f00d"; }
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+.fa-multiply::before {
+  content: "\f00d"; }
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+  content: "\f00d"; }
+
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+  content: "\f00d"; }
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+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
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+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-arrow-turn-right::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
+  content: "\f1e9"; }
+
+.fa-cc-visa:before {
+  content: "\f1f0"; }
+
+.fa-lastfm:before {
+  content: "\f202"; }
+
+.fa-shopware:before {
+  content: "\f5b5"; }
+
+.fa-creative-commons-nc:before {
+  content: "\f4e8"; }
+
+.fa-aws:before {
+  content: "\f375"; }
+
+.fa-redhat:before {
+  content: "\f7bc"; }
+
+.fa-yoast:before {
+  content: "\f2b1"; }
+
+.fa-cloudflare:before {
+  content: "\e07d"; }
+
+.fa-ups:before {
+  content: "\f7e0"; }
+
+.fa-wpexplorer:before {
+  content: "\f2de"; }
+
+.fa-dyalog:before {
+  content: "\f399"; }
+
+.fa-bity:before {
+  content: "\f37a"; }
+
+.fa-stackpath:before {
+  content: "\f842"; }
+
+.fa-buysellads:before {
+  content: "\f20d"; }
+
+.fa-first-order:before {
+  content: "\f2b0"; }
+
+.fa-modx:before {
+  content: "\f285"; }
+
+.fa-guilded:before {
+  content: "\e07e"; }
+
+.fa-vnv:before {
+  content: "\f40b"; }
+
+.fa-square-js:before {
+  content: "\f3b9"; }
+
+.fa-js-square:before {
+  content: "\f3b9"; }
+
+.fa-microsoft:before {
+  content: "\f3ca"; }
+
+.fa-qq:before {
+  content: "\f1d6"; }
+
+.fa-orcid:before {
+  content: "\f8d2"; }
+
+.fa-java:before {
+  content: "\f4e4"; }
+
+.fa-invision:before {
+  content: "\f7b0"; }
+
+.fa-creative-commons-pd-alt:before {
+  content: "\f4ed"; }
+
+.fa-centercode:before {
+  content: "\f380"; }
+
+.fa-glide-g:before {
+  content: "\f2a6"; }
+
+.fa-drupal:before {
+  content: "\f1a9"; }
+
+.fa-hire-a-helper:before {
+  content: "\f3b0"; }
+
+.fa-creative-commons-by:before {
+  content: "\f4e7"; }
+
+.fa-unity:before {
+  content: "\e049"; }
+
+.fa-whmcs:before {
+  content: "\f40d"; }
+
+.fa-rocketchat:before {
+  content: "\f3e8"; }
+
+.fa-vk:before {
+  content: "\f189"; }
+
+.fa-untappd:before {
+  content: "\f405"; }
+
+.fa-mailchimp:before {
+  content: "\f59e"; }
+
+.fa-css3-alt:before {
+  content: "\f38b"; }
+
+.fa-square-reddit:before {
+  content: "\f1a2"; }
+
+.fa-reddit-square:before {
+  content: "\f1a2"; }
+
+.fa-vimeo-v:before {
+  content: "\f27d"; }
+
+.fa-contao:before {
+  content: "\f26d"; }
+
+.fa-square-font-awesome:before {
+  content: "\e5ad"; }
+
+.fa-deskpro:before {
+  content: "\f38f"; }
+
+.fa-sistrix:before {
+  content: "\f3ee"; }
+
+.fa-square-instagram:before {
+  content: "\e055"; }
+
+.fa-instagram-square:before {
+  content: "\e055"; }
+
+.fa-battle-net:before {
+  content: "\f835"; }
+
+.fa-the-red-yeti:before {
+  content: "\f69d"; }
+
+.fa-square-hacker-news:before {
+  content: "\f3af"; }
+
+.fa-hacker-news-square:before {
+  content: "\f3af"; }
+
+.fa-edge:before {
+  content: "\f282"; }
+
+.fa-threads:before {
+  content: "\e618"; }
+
+.fa-napster:before {
+  content: "\f3d2"; }
+
+.fa-square-snapchat:before {
+  content: "\f2ad"; }
+
+.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa-google-plus-g:before {
+  content: "\f0d5"; }
+
+.fa-artstation:before {
+  content: "\f77a"; }
+
+.fa-markdown:before {
+  content: "\f60f"; }
+
+.fa-sourcetree:before {
+  content: "\f7d3"; }
+
+.fa-google-plus:before {
+  content: "\f2b3"; }
+
+.fa-diaspora:before {
+  content: "\f791"; }
+
+.fa-foursquare:before {
+  content: "\f180"; }
+
+.fa-stack-overflow:before {
+  content: "\f16c"; }
+
+.fa-github-alt:before {
+  content: "\f113"; }
+
+.fa-phoenix-squadron:before {
+  content: "\f511"; }
+
+.fa-pagelines:before {
+  content: "\f18c"; }
+
+.fa-algolia:before {
+  content: "\f36c"; }
+
+.fa-red-river:before {
+  content: "\f3e3"; }
+
+.fa-creative-commons-sa:before {
+  content: "\f4ef"; }
+
+.fa-safari:before {
+  content: "\f267"; }
+
+.fa-google:before {
+  content: "\f1a0"; }
+
+.fa-square-font-awesome-stroke:before {
+  content: "\f35c"; }
+
+.fa-font-awesome-alt:before {
+  content: "\f35c"; }
+
+.fa-atlassian:before {
+  content: "\f77b"; }
+
+.fa-linkedin-in:before {
+  content: "\f0e1"; }
+
+.fa-digital-ocean:before {
+  content: "\f391"; }
+
+.fa-nimblr:before {
+  content: "\f5a8"; }
+
+.fa-chromecast:before {
+  content: "\f838"; }
+
+.fa-evernote:before {
+  content: "\f839"; }
+
+.fa-hacker-news:before {
+  content: "\f1d4"; }
+
+.fa-creative-commons-sampling:before {
+  content: "\f4f0"; }
+
+.fa-adversal:before {
+  content: "\f36a"; }
+
+.fa-creative-commons:before {
+  content: "\f25e"; }
+
+.fa-watchman-monitoring:before {
+  content: "\e087"; }
+
+.fa-fonticons:before {
+  content: "\f280"; }
+
+.fa-weixin:before {
+  content: "\f1d7"; }
+
+.fa-shirtsinbulk:before {
+  content: "\f214"; }
+
+.fa-codepen:before {
+  content: "\f1cb"; }
+
+.fa-git-alt:before {
+  content: "\f841"; }
+
+.fa-lyft:before {
+  content: "\f3c3"; }
+
+.fa-rev:before {
+  content: "\f5b2"; }
+
+.fa-windows:before {
+  content: "\f17a"; }
+
+.fa-wizards-of-the-coast:before {
+  content: "\f730"; }
+
+.fa-square-viadeo:before {
+  content: "\f2aa"; }
+
+.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa-meetup:before {
+  content: "\f2e0"; }
+
+.fa-centos:before {
+  content: "\f789"; }
+
+.fa-adn:before {
+  content: "\f170"; }
+
+.fa-cloudsmith:before {
+  content: "\f384"; }
+
+.fa-pied-piper-alt:before {
+  content: "\f1a8"; }
+
+.fa-square-dribbble:before {
+  content: "\f397"; }
+
+.fa-dribbble-square:before {
+  content: "\f397"; }
+
+.fa-codiepie:before {
+  content: "\f284"; }
+
+.fa-node:before {
+  content: "\f419"; }
+
+.fa-mix:before {
+  content: "\f3cb"; }
+
+.fa-steam:before {
+  content: "\f1b6"; }
+
+.fa-cc-apple-pay:before {
+  content: "\f416"; }
+
+.fa-scribd:before {
+  content: "\f28a"; }
+
+.fa-debian:before {
+  content: "\e60b"; }
+
+.fa-openid:before {
+  content: "\f19b"; }
+
+.fa-instalod:before {
+  content: "\e081"; }
+
+.fa-expeditedssl:before {
+  content: "\f23e"; }
+
+.fa-sellcast:before {
+  content: "\f2da"; }
+
+.fa-square-twitter:before {
+  content: "\f081"; }
+
+.fa-twitter-square:before {
+  content: "\f081"; }
+
+.fa-r-project:before {
+  content: "\f4f7"; }
+
+.fa-delicious:before {
+  content: "\f1a5"; }
+
+.fa-freebsd:before {
+  content: "\f3a4"; }
+
+.fa-vuejs:before {
+  content: "\f41f"; }
+
+.fa-accusoft:before {
+  content: "\f369"; }
+
+.fa-ioxhost:before {
+  content: "\f208"; }
+
+.fa-fonticons-fi:before {
+  content: "\f3a2"; }
+
+.fa-app-store:before {
+  content: "\f36f"; }
+
+.fa-cc-mastercard:before {
+  content: "\f1f1"; }
+
+.fa-itunes-note:before {
+  content: "\f3b5"; }
+
+.fa-golang:before {
+  content: "\e40f"; }
+
+.fa-kickstarter:before {
+  content: "\f3bb"; }
+
+.fa-grav:before {
+  content: "\f2d6"; }
+
+.fa-weibo:before {
+  content: "\f18a"; }
+
+.fa-uncharted:before {
+  content: "\e084"; }
+
+.fa-firstdraft:before {
+  content: "\f3a1"; }
+
+.fa-square-youtube:before {
+  content: "\f431"; }
+
+.fa-youtube-square:before {
+  content: "\f431"; }
+
+.fa-wikipedia-w:before {
+  content: "\f266"; }
+
+.fa-wpressr:before {
+  content: "\f3e4"; }
+
+.fa-rendact:before {
+  content: "\f3e4"; }
+
+.fa-angellist:before {
+  content: "\f209"; }
+
+.fa-galactic-republic:before {
+  content: "\f50c"; }
+
+.fa-nfc-directional:before {
+  content: "\e530"; }
+
+.fa-skype:before {
+  content: "\f17e"; }
+
+.fa-joget:before {
+  content: "\f3b7"; }
+
+.fa-fedora:before {
+  content: "\f798"; }
+
+.fa-stripe-s:before {
+  content: "\f42a"; }
+
+.fa-meta:before {
+  content: "\e49b"; }
+
+.fa-laravel:before {
+  content: "\f3bd"; }
+
+.fa-hotjar:before {
+  content: "\f3b1"; }
+
+.fa-bluetooth-b:before {
+  content: "\f294"; }
+
+.fa-sticker-mule:before {
+  content: "\f3f7"; }
+
+.fa-creative-commons-zero:before {
+  content: "\f4f3"; }
+
+.fa-hips:before {
+  content: "\f452"; }
+
+.fa-behance:before {
+  content: "\f1b4"; }
+
+.fa-reddit:before {
+  content: "\f1a1"; }
+
+.fa-discord:before {
+  content: "\f392"; }
+
+.fa-chrome:before {
+  content: "\f268"; }
+
+.fa-app-store-ios:before {
+  content: "\f370"; }
+
+.fa-cc-discover:before {
+  content: "\f1f2"; }
+
+.fa-wpbeginner:before {
+  content: "\f297"; }
+
+.fa-confluence:before {
+  content: "\f78d"; }
+
+.fa-mdb:before {
+  content: "\f8ca"; }
+
+.fa-dochub:before {
+  content: "\f394"; }
+
+.fa-accessible-icon:before {
+  content: "\f368"; }
+
+.fa-ebay:before {
+  content: "\f4f4"; }
+
+.fa-amazon:before {
+  content: "\f270"; }
+
+.fa-unsplash:before {
+  content: "\e07c"; }
+
+.fa-yarn:before {
+  content: "\f7e3"; }
+
+.fa-square-steam:before {
+  content: "\f1b7"; }
+
+.fa-steam-square:before {
+  content: "\f1b7"; }
+
+.fa-500px:before {
+  content: "\f26e"; }
+
+.fa-square-vimeo:before {
+  content: "\f194"; }
+
+.fa-vimeo-square:before {
+  content: "\f194"; }
+
+.fa-asymmetrik:before {
+  content: "\f372"; }
+
+.fa-font-awesome:before {
+  content: "\f2b4"; }
+
+.fa-font-awesome-flag:before {
+  content: "\f2b4"; }
+
+.fa-font-awesome-logo-full:before {
+  content: "\f2b4"; }
+
+.fa-gratipay:before {
+  content: "\f184"; }
+
+.fa-apple:before {
+  content: "\f179"; }
+
+.fa-hive:before {
+  content: "\e07f"; }
+
+.fa-gitkraken:before {
+  content: "\f3a6"; }
+
+.fa-keybase:before {
+  content: "\f4f5"; }
+
+.fa-apple-pay:before {
+  content: "\f415"; }
+
+.fa-padlet:before {
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+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/deps/font-awesome-6.4.2/css/all.min.css b/deps/font-awesome-6.4.2/css/all.min.css
new file mode 100644
index 0000000..6604a06
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,steps(8));animation-timing-function:var(--fa-animation-timing,steps(8))}@media (prefers-reduced-motion:reduce){.fa-beat,.fa-beat-fade,.fa-bounce,.fa-fade,.fa-flip,.fa-pulse,.fa-shake,.fa-spin,.fa-spin-pulse{-webkit-animation-delay:-1ms;animation-delay:-1ms;-webkit-animation-duration:1ms;animation-duration:1ms;-webkit-animation-iteration-count:1;animation-iteration-count:1;-webkit-transition-delay:0s;transition-delay:0s;-webkit-transition-duration:0s;transition-duration:0s}}@-webkit-keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@-webkit-keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) 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translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@-webkit-keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@-webkit-keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@-webkit-keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@-webkit-keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}.fa-rotate-90{-webkit-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{-webkit-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{-webkit-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{-webkit-transform:scaleX(-1);transform:scaleX(-1)}.fa-flip-vertical{-webkit-transform:scaleY(-1);transform:scaleY(-1)}.fa-flip-both,.fa-flip-horizontal.fa-flip-vertical{-webkit-transform:scale(-1);transform:scale(-1)}.fa-rotate-by{-webkit-transform:rotate(var(--fa-rotate-angle,none));transform:rotate(var(--fa-rotate-angle,none))}.fa-stack{display:inline-block;height:2em;line-height:2em;position:relative;vertical-align:middle;width:2.5em}.fa-stack-1x,.fa-stack-2x{left:0;position:absolute;text-align:center;width:100%;z-index:var(--fa-stack-z-index,auto)}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:var(--fa-inverse,#fff)}
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diff --git a/deps/font-awesome-6.4.2/css/v4-shims.css b/deps/font-awesome-6.4.2/css/v4-shims.css
new file mode 100644
index 0000000..a85953d
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
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+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-file-o:before {
+  content: "\f15b"; }
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+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
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+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
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+.fa.fa-video-camera:before {
+  content: "\f03d"; }
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+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f03e"; }
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+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-photo:before {
+  content: "\f03e"; }
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+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-image:before {
+  content: "\f03e"; }
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+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
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+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-edit:before {
+  content: "\f044"; }
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+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
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+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
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+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
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+.fa.fa-compress:before {
+  content: "\f422"; }
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+.fa.fa-eye {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-eye-slash {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-warning:before {
+  content: "\f071"; }
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+.fa.fa-calendar:before {
+  content: "\f073"; }
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+  content: "\f338"; }
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+  content: "\f337"; }
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+.fa.fa-bar-chart:before {
+  content: "\e0e3"; }
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+.fa.fa-bar-chart-o:before {
+  content: "\e0e3"; }
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+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
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+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/deps/font-awesome-6.4.2/css/v4-shims.min.css b/deps/font-awesome-6.4.2/css/v4-shims.min.css
new file mode 100644
index 0000000..64e4e8d
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 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+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/deps/headroom-0.11.0/jQuery.headroom.min.js b/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 0000000..17f70c9
--- /dev/null
+++ b/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/deps/search-1.0.0/autocomplete.jquery.min.js b/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 0000000..fb63a6a
--- /dev/null
+++ b/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 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i[a]&&"initialize"!==a?(b=this.filter(function(){return!!e(this).data(h)}),i[a].apply(b,[].slice.call(arguments,1))):i.initialize.apply(this,arguments)},e.fn.autocomplete.noConflict=function(){return e.fn.autocomplete=g,this},e.fn.autocomplete.sources=j.sources,e.fn.autocomplete.escapeHighlightedString=f.escapeHighlightedString,a.exports=e.fn.autocomplete},function(a,b){"use strict";a.exports={element:null}},function(a,b){a.exports=jQuery},function(a,b,c){"use strict";function d(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}var e=c(2);a.exports={isArray:null,isFunction:null,isObject:null,bind:null,each:null,map:null,mixin:null,isMsie:function(a){if(void 0===a&&(a=navigator.userAgent),/(msie|trident)/i.test(a)){var b=a.match(/(msie |rv:)(\d+(.\d+)?)/i);if(b)return b[2]}return!1},escapeRegExChars:function(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")},isNumber:function(a){return"number"==typeof a},toStr:function(a){return void 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f=document.createElement("div");f.appendChild(document.createTextNode(c));var g=document.createElement("div");return g.appendChild(document.createTextNode(a)),g.innerHTML.replace(RegExp(d(e.innerHTML),"g"),b).replace(RegExp(d(f.innerHTML),"g"),c)}}},function(a,b,c){"use strict";function d(a){var b,c;if(a=a||{},a.input||i.error("missing input"),this.isActivated=!1,this.debug=!!a.debug,this.autoselect=!!a.autoselect,this.autoselectOnBlur=!!a.autoselectOnBlur,this.openOnFocus=!!a.openOnFocus,this.minLength=i.isNumber(a.minLength)?a.minLength:1,this.autoWidth=void 0===a.autoWidth||!!a.autoWidth,this.clearOnSelected=!!a.clearOnSelected,this.tabAutocomplete=void 0===a.tabAutocomplete||!!a.tabAutocomplete,a.hint=!!a.hint,a.hint&&a.appendTo)throw new Error("[autocomplete.js] hint and appendTo options can't be used at the same 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d.Dropdown({appendTo:a.appendTo,wrapper:this.$node,menu:b,datasets:a.datasets,templates:a.templates,cssClasses:a.cssClasses,minLength:this.minLength}).onSync("suggestionClicked",this._onSuggestionClicked,this).onSync("cursorMoved",this._onCursorMoved,this).onSync("cursorRemoved",this._onCursorRemoved,this).onSync("opened",this._onOpened,this).onSync("closed",this._onClosed,this).onSync("shown",this._onShown,this).onSync("empty",this._onEmpty,this).onSync("redrawn",this._onRedrawn,this).onAsync("datasetRendered",this._onDatasetRendered,this),this.input=new 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g=n.dropdown.replace("%PREFIX%",a.cssClasses.prefix).replace("%DROPDOWN_MENU%",a.cssClasses.dropdownMenu);d=j.element(g).css(a.css.dropdown).attr({role:"listbox",id:a.listboxId}),a.templates&&a.templates.dropdownMenu&&d.html(i.templatify(a.templates.dropdownMenu)()),e=b.clone().css(a.css.hint).css(f(b)),e.val("").addClass(i.className(a.cssClasses.prefix,a.cssClasses.hint,!0)).removeAttr("id name placeholder required").prop("readonly",!0).attr({"aria-hidden":"true",autocomplete:"off",spellcheck:"false",tabindex:-1}),e.removeData&&e.removeData(),b.data(h,{"aria-autocomplete":b.attr("aria-autocomplete"),"aria-expanded":b.attr("aria-expanded"),"aria-owns":b.attr("aria-owns"),autocomplete:b.attr("autocomplete"),dir:b.attr("dir"),role:b.attr("role"),spellcheck:b.attr("spellcheck"),style:b.attr("style"),type:b.attr("type")}),b.addClass(i.className(a.cssClasses.prefix,a.cssClasses.input,!0)).attr({autocomplete:"off",spellcheck:!1,role:"combobox","aria-autocomplete":a.datasets&&a.datasets[0]&&a.datasets[0].displayKey?"both":"list","aria-expanded":"false","aria-label":a.ariaLabel,"aria-owns":a.listboxId}).css(a.hint?a.css.input:a.css.inputWithNoHint);try{b.attr("dir")||b.attr("dir","auto")}catch(a){}return c=a.appendTo?c.appendTo(j.element(a.appendTo).eq(0)).eq(0):b.wrap(c).parent(),c.prepend(a.hint?e:null).append(d),{wrapper:c,input:b,hint:e,menu:d}}function f(a){return{backgroundAttachment:a.css("background-attachment"),backgroundClip:a.css("background-clip"),backgroundColor:a.css("background-color"),backgroundImage:a.css("background-image"),backgroundOrigin:a.css("background-origin"),backgroundPosition:a.css("background-position"),backgroundRepeat:a.css("background-repeat"),backgroundSize:a.css("background-size")}}function g(a,b){var c=a.find(i.className(b.prefix,b.input));i.each(c.data(h),function(a,b){void 0===a?c.removeAttr(b):c.attr(b,a)}),c.detach().removeClass(i.className(b.prefix,b.input,!0)).insertAfter(a),c.removeData&&c.removeData(h),a.remove()}var h="aaAttrs",i=c(4),j=c(2),k=c(6),l=c(7),m=c(16),n=c(18),o=c(19);i.mixin(d.prototype,{_bindKeyboardShortcuts:function(a){if(a.keyboardShortcuts){var b=this.$input,c=[];i.each(a.keyboardShortcuts,function(a){"string"==typeof a&&(a=a.toUpperCase().charCodeAt(0)),c.push(a)}),j.element(document).keydown(function(a){var 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c=this.dropdown.getDatumForCursor(),d=this.dropdown.getCurrentCursor().attr("id");this.input.setActiveDescendant(d),c&&(b&&this.input.setInputValue(c.value,!0),this.eventBus.trigger("cursorchanged",c.raw,c.datasetName))},_onCursorRemoved:function(){this.input.resetInputValue(),this._updateHint(),this.eventBus.trigger("cursorremoved")},_onDatasetRendered:function(){this._updateHint(),this.eventBus.trigger("updated")},_onOpened:function(){this._updateHint(),this.input.expand(),this.eventBus.trigger("opened")},_onEmpty:function(){this.eventBus.trigger("empty")},_onRedrawn:function(){this.$node.css("top","0px"),this.$node.css("left","0px");var a=this.$input[0].getBoundingClientRect();this.autoWidth&&this.$node.css("width",a.width+"px");var b=this.$node[0].getBoundingClientRect(),c=a.bottom-b.top;this.$node.css("top",c+"px");var d=a.left-b.left;this.$node.css("left",d+"px"),this.eventBus.trigger("redrawn")},_onShown:function(){this.eventBus.trigger("shown"),this.autoselect&&this.dropdown.cursorTopSuggestion()},_onClosed:function(){this.input.clearHint(),this.input.removeActiveDescendant(),this.input.collapse(),this.eventBus.trigger("closed")},_onFocused:function(){if(this.isActivated=!0,this.openOnFocus){var a=this.input.getQuery();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty(),this.dropdown.open()}},_onBlurred:function(){var a,b;a=this.dropdown.getDatumForCursor(),b=this.dropdown.getDatumForTopSuggestion();var c={selectionMethod:"blur"};this.debug||(this.autoselectOnBlur&&a?this._select(a,c):this.autoselectOnBlur&&b?this._select(b,c):(this.isActivated=!1,this.dropdown.empty(),this.dropdown.close()))},_onEnterKeyed:function(a,b){var c,d;c=this.dropdown.getDatumForCursor(),d=this.dropdown.getDatumForTopSuggestion();var 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a=this.input.getInputValue();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty()}this.dropdown.open()},close:function(){this.dropdown.close()},setVal:function(a){a=i.toStr(a),this.isActivated?this.input.setInputValue(a):(this.input.setQuery(a),this.input.setInputValue(a,!0)),this._setLanguageDirection()},getVal:function(){return this.input.getQuery()},destroy:function(){this.input.destroy(),this.dropdown.destroy(),g(this.$node,this.cssClasses),this.$node=null},getWrapper:function(){return this.dropdown.$container[0]}}),d.Dropdown=m,d.Input=l,d.sources=c(20),a.exports=d},function(a,b,c){"use strict";function d(a){a&&a.el||e.error("EventBus initialized without el"),this.$el=f.element(a.el)}var e=c(4),f=c(2);e.mixin(d.prototype,{trigger:function(a,b,c,d){var f=e.Event("autocomplete:"+a);return this.$el.trigger(f,[b,c,d]),f}}),a.exports=d},function(a,b,c){"use strict";function d(a){var b,c,d,f,g=this;a=a||{},a.input||i.error("input is 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+var c=[].slice.call(arguments,1);this.cacheSuggestions(a,b,c),this._render.apply(this,[a,b].concat(c))}}if(this.query=a,this.canceled=!1,this.shouldFetchFromCache(a))b.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var c=this,d=function(){c.canceled||c.source(a,b.bind(c))};if(this.debounce){var e=function(){c.debounceTimeout=null,d()};clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout(e,this.debounce)}else d()}},cacheSuggestions:function(a,b,c){this.cachedQuery=a,this.cachedSuggestions=b,this.cachedRenderExtraArgs=c},shouldFetchFromCache:function(a){return this.cache&&this.cachedQuery===a&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),a.exports=d},function(a,b){"use strict";a.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},function(a,b,c){"use strict";var d=c(4),e={wrapper:{position:"relative",display:"inline-block"},hint:{position:"absolute",top:"0",left:"0",borderColor:"transparent",boxShadow:"none",opacity:"1"},input:{position:"relative",verticalAlign:"top",backgroundColor:"transparent"},inputWithNoHint:{position:"relative",verticalAlign:"top"},dropdown:{position:"absolute",top:"100%",left:"0",zIndex:"100",display:"none"},suggestions:{display:"block"},suggestion:{whiteSpace:"nowrap",cursor:"pointer"},suggestionChild:{whiteSpace:"normal"},ltr:{left:"0",right:"auto"},rtl:{left:"auto",right:"0"},defaultClasses:{root:"algolia-autocomplete",prefix:"aa",noPrefix:!1,dropdownMenu:"dropdown-menu",input:"input",hint:"hint",suggestions:"suggestions",suggestion:"suggestion",cursor:"cursor",dataset:"dataset",empty:"empty"},appendTo:{wrapper:{position:"absolute",zIndex:"100",display:"none"},input:{},inputWithNoHint:{},dropdown:{display:"block"}}};d.isMsie()&&d.mixin(e.input,{backgroundImage:"url(data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)"}),d.isMsie()&&d.isMsie()<=7&&d.mixin(e.input,{marginTop:"-1px"}),a.exports=e},function(a,b,c){"use strict";a.exports={hits:c(21),popularIn:c(24)}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23),g=function(){function a(a,d){return window.Promise.resolve().then(function(){return b.length&&(e=a.search(b),b=[]),e}).then(function(a){if(a)return c=a.results,c[d]})}var b=[],c=[],e=window.Promise.resolve();return function(c,e){return function(f,g){a(c.as,b.push({indexName:c.indexName,query:f,params:e})-1).then(function(a){a&&g(a.hits,a)}).catch(function(a){d.error(a.message)})}}}();a.exports=function(a,b){var c=f(a.as._ua);if(c&&c[0]>=3&&c[1]>20){var d="autocomplete.js "+e;a.as._ua.indexOf(d)===-1&&(a.as._ua+="; "+d)}return g(a,b)}},function(a,b){a.exports="0.38.0"},function(a,b){"use strict";a.exports=function(a){var b=a.match(/Algolia for JavaScript \((\d+\.)(\d+\.)(\d+)\)/)||a.match(/Algolia for vanilla JavaScript (\d+\.)(\d+\.)(\d+)/);if(b)return[b[1],b[2],b[3]]}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23);a.exports=function(a,b,c,g){function h(h,i){a.search(h,b,function(a,h){if(a)return void d.error(a.message);if(h.hits.length>0){var l=h.hits[0],m=d.mixin({hitsPerPage:0},c);delete m.source,delete m.index;var n=f(k.as._ua);return n&&n[0]>=3&&n[1]>20&&(b.additionalUA="autocomplete.js "+e),void k.search(j(l),m,function(a,b){if(a)return void d.error(a.message);var c=[];if(g.includeAll){var e=g.allTitle||"All departments";c.push(d.mixin({facet:{value:e,count:b.nbHits}},d.cloneDeep(l)))}d.each(b.facets,function(a,b){d.each(a,function(a,e){c.push(d.mixin({facet:{facet:b,value:e,count:a}},d.cloneDeep(l)))})});for(var f=1;f<h.hits.length;++f)c.push(h.hits[f]);i(c,h)})}i([])})}var i=f(a.as._ua);if(i&&i[0]>=3&&i[1]>20&&(b=b||{},b.additionalUA="autocomplete.js "+e),!c.source)return d.error("Missing 'source' key");var j=d.isFunction(c.source)?c.source:function(a){return a[c.source]};if(!c.index)return d.error("Missing 'index' key");var k=c.index;return g=g||{},h}}]);
\ No newline at end of file
diff --git a/deps/search-1.0.0/fuse.min.js b/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 0000000..7def598
--- /dev/null
+++ b/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?o(Object(n),!0).forEach((function(t){i(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):o(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function a(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),t&&u(e,t)}function s(e){return(s=Object.setPrototypeOf?Object.getPrototypeOf:function(e){return e.__proto__||Object.getPrototypeOf(e)})(e)}function u(e,t){return(u=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e})(e,t)}function h(e,t){return!t||"object"!=typeof t&&"function"!=typeof t?function(e){if(void 0===e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}(e):t}function f(e){var t=function(){if("undefined"==typeof 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\ No newline at end of file
diff --git a/deps/search-1.0.0/mark.min.js b/deps/search-1.0.0/mark.min.js
new file mode 100644
index 0000000..1eea053
--- /dev/null
+++ b/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):e.Mark=t()}(this,function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),r=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i=function(){function e(n){var r=!(arguments.length>1&&void 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index 768e2d5..97aa145 100644
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     <div id="navbar" class="collapse navbar-collapse ms-3">
       <ul class="navbar-nav me-auto">
-<li class="nav-item">
-  <a class="nav-link" href="articles/arf.html">Get started</a>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="reference/index.html">Reference</a>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="news/index.html">Changelog</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="articles/arf.html">Get started</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-home">
 <div class="row">
@@ -191,15 +169,15 @@ <h2 data-toc-skip>Dev status</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/katex-auto.js b/katex-auto.js
new file mode 100644
index 0000000..20651d9
--- /dev/null
+++ b/katex-auto.js
@@ -0,0 +1,14 @@
+// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345
+document.addEventListener("DOMContentLoaded", function () {
+  var mathElements = document.getElementsByClassName("math");
+  var macros = [];
+  for (var i = 0; i < mathElements.length; i++) {
+    var texText = mathElements[i].firstChild;
+    if (mathElements[i].tagName == "SPAN") {
+      katex.render(texText.data, mathElements[i], {
+        displayMode: mathElements[i].classList.contains("display"),
+        throwOnError: false,
+        macros: macros,
+        fleqn: false
+      });
+    }}});
diff --git a/lightswitch.js b/lightswitch.js
new file mode 100644
index 0000000..9467125
--- /dev/null
+++ b/lightswitch.js
@@ -0,0 +1,85 @@
+
+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under the Creative Commons Attribution 3.0 Unported License.
+ * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0.
+ */
+
+const getStoredTheme = () => localStorage.getItem('theme')
+const setStoredTheme = theme => localStorage.setItem('theme', theme)
+
+const getPreferredTheme = () => {
+  const storedTheme = getStoredTheme()
+  if (storedTheme) {
+    return storedTheme
+  }
+
+  return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'
+}
+
+const setTheme = theme => {
+  if (theme === 'auto') {
+    document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'))
+  } else {
+    document.documentElement.setAttribute('data-bs-theme', theme)
+  }
+}
+
+function bsSetupThemeToggle () {
+  'use strict'
+
+  const showActiveTheme = (theme, focus = false) => {
+    var activeLabel, activeIcon;
+
+    document.querySelectorAll('[data-bs-theme-value]').forEach(element => {
+      const buttonTheme = element.getAttribute('data-bs-theme-value')
+      const isActive = buttonTheme == theme
+      
+      element.classList.toggle('active', isActive)
+      element.setAttribute('aria-pressed', isActive)
+
+      if (isActive) {
+        activeLabel = element.textContent;
+        activeIcon = element.querySelector('span').classList.value;
+      }
+    })
+
+    const themeSwitcher = document.querySelector('#dropdown-lightswitch')
+    if (!themeSwitcher) {
+      return
+    }
+
+    themeSwitcher.setAttribute('aria-label', activeLabel)
+    themeSwitcher.querySelector('span').classList.value = activeIcon;
+
+    if (focus) {
+      themeSwitcher.focus()
+    }
+  }
+
+  window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => {
+    const storedTheme = getStoredTheme()
+    if (storedTheme !== 'light' && storedTheme !== 'dark') {
+      setTheme(getPreferredTheme())
+    }
+  })
+
+  window.addEventListener('DOMContentLoaded', () => {
+    showActiveTheme(getPreferredTheme())
+
+    document
+      .querySelectorAll('[data-bs-theme-value]')
+      .forEach(toggle => {
+        toggle.addEventListener('click', () => {
+          const theme = toggle.getAttribute('data-bs-theme-value')
+          setTheme(theme)
+          setStoredTheme(theme)
+          showActiveTheme(theme, true)
+        })
+      })
+  })
+}
+
+setTheme(getPreferredTheme());
+bsSetupThemeToggle();
diff --git a/news/index.html b/news/index.html
index ec6377d..1e39cf0 100644
--- a/news/index.html
+++ b/news/index.html
@@ -1,52 +1,42 @@
 <!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>Changelog • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Changelog"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
+<!-- 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>Changelog • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light" data-bs-theme="light"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="nav-item">
-  <a class="nav-link" href="../articles/arf.html">Get started</a>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
-<li class="active nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/arf.html">Get started</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="active nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-news">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Changelog</h1>
-      <small>Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
+      <small>Source: <a href="https://github.com/bips-hb/arf/blob/main/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.2.3" id="arf-023">arf 0.2.3<a class="anchor" aria-label="anchor" href="#arf-023"></a></h2>
+<ul><li>Add impute() function for direct missing data imputation with ARF</li>
+<li>Add one-line functions darf(), earf(), rarf()</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.2.2" id="arf-022">arf 0.2.2<a class="anchor" aria-label="anchor" href="#arf-022"></a></h2>
 <ul><li>Faster and vectorized conditional sampling</li>
@@ -71,7 +61,7 @@ <h2 class="pkg-version" data-toc-text="0.1.3" id="arf-013">arf 0.1.3<a class="an
 <li>alpha parameter for regularizing multinomial distributions in forde</li>
 <li>Unified treatment of colnames with internal semantics (y, obs, tree, leaf)</li>
 </ul></div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -80,14 +70,14 @@ <h2 class="pkg-version" data-toc-text="0.1.3" id="arf-013">arf 0.1.3<a class="an
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/pkgdown.js b/pkgdown.js
index 9bd6621..1a99c65 100644
--- a/pkgdown.js
+++ b/pkgdown.js
@@ -153,4 +153,10 @@ async function searchFuse(query, callback) {
   });
 })(window.jQuery || window.$)
 
-
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
+    event.preventDefault();  // Prevent any default action associated with the '/' key
+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/pkgdown.yml b/pkgdown.yml
index e03b5f5..309bcba 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,10 +1,9 @@
 pandoc: 3.1.11
-pkgdown: 2.0.9
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
   arf: arf.html
-last_built: 2024-07-02T13:43Z
+last_built: 2024-11-14T15:45Z
 urls:
   reference: https://bips-hb.github.io/arf/reference
   article: https://bips-hb.github.io/arf/articles
-
diff --git a/reference/adversarial_rf.html b/reference/adversarial_rf.html
index 7661ad9..64da0a1 100644
--- a/reference/adversarial_rf.html
+++ b/reference/adversarial_rf.html
@@ -1,50 +1,35 @@
 <!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="Implements an adversarial random forest to learn independence-inducing splits."><title>Adversarial Random Forests — adversarial_rf • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Adversarial Random Forests — adversarial_rf"><meta property="og:description" content="Implements an adversarial random forest to learn independence-inducing splits."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Adversarial Random Forests</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/adversarial_rf.R" class="external-link"><code>R/adversarial_rf.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/adversarial_rf.R" class="external-link"><code>R/adversarial_rf.R</code></a></small>
       <div class="d-none name"><code>adversarial_rf.Rd</code></div>
     </div>
 
@@ -70,57 +55,57 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Input data. Integer variables are recoded as ordered factors with
 a warning. See Details.</p></dd>
 
 
-<dt>num_trees</dt>
+<dt id="arg-num-trees">num_trees<a class="anchor" aria-label="anchor" href="#arg-num-trees"></a></dt>
 <dd><p>Number of trees to grow in each forest. The default works
 well for most generative modeling tasks, but should be increased for
 likelihood estimation. See Details.</p></dd>
 
 
-<dt>min_node_size</dt>
+<dt id="arg-min-node-size">min_node_size<a class="anchor" aria-label="anchor" href="#arg-min-node-size"></a></dt>
 <dd><p>Minimal number of real data samples in leaf nodes.</p></dd>
 
 
-<dt>delta</dt>
+<dt id="arg-delta">delta<a class="anchor" aria-label="anchor" href="#arg-delta"></a></dt>
 <dd><p>Tolerance parameter. Algorithm converges when OOB accuracy is
 &lt; 0.5 + <code>delta</code>.</p></dd>
 
 
-<dt>max_iters</dt>
+<dt id="arg-max-iters">max_iters<a class="anchor" aria-label="anchor" href="#arg-max-iters"></a></dt>
 <dd><p>Maximum iterations for the adversarial loop.</p></dd>
 
 
-<dt>early_stop</dt>
+<dt id="arg-early-stop">early_stop<a class="anchor" aria-label="anchor" href="#arg-early-stop"></a></dt>
 <dd><p>Terminate loop if performance fails to improve from one
 round to the next?</p></dd>
 
 
-<dt>prune</dt>
+<dt id="arg-prune">prune<a class="anchor" aria-label="anchor" href="#arg-prune"></a></dt>
 <dd><p>Impose <code>min_node_size</code> by pruning?</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>Print discriminator accuracy after each round?</p></dd>
 
 
-<dt>parallel</dt>
+<dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt>
 <dd><p>Compute in parallel? Must register backend beforehand, e.g.
 via <code>doParallel</code> or <code>doFuture</code>; see examples.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Extra parameters to be passed to <code>ranger</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A random forest object of class <code>ranger</code>.</p>
+    <p>A random forest object of class <code>ranger</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -167,8 +152,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <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"># Train ARF and estimate leaf parameters</span></span></span>
 <span class="r-in"><span><span class="va">arf</span> <span class="op">&lt;-</span> <span class="fu">adversarial_rf</span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 86.2%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 40.2%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 70.71%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 45.95%</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>executing %dopar% sequentially: no parallel backend registered</span>
 <span class="r-in"><span><span class="va">psi</span> <span class="op">&lt;-</span> <span class="fu"><a href="forde.html">forde</a></span><span class="op">(</span><span class="va">arf</span>, <span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -183,26 +168,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Estimate average log-likelihood</span></span></span>
 <span class="r-in"><span><span class="va">ll</span> <span class="op">&lt;-</span> <span class="fu"><a href="lik.html">lik</a></span><span class="op">(</span><span class="va">psi</span>, <span class="va">iris</span>, arf <span class="op">=</span> <span class="va">arf</span>, log <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.6882583</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.4833021</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Expectation of Sepal.Length for class setosa</span></span></span>
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="expct.html">expct</a></span><span class="op">(</span><span class="va">psi</span>, query <span class="op">=</span> <span class="st">"Sepal.Length"</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Sepal.Length</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1      5.01579</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1      5.01668</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># Parallelization with doParallel</span></span></span>
 <span class="r-in"><span><span class="fu">doParallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/doParallel/man/registerDoParallel.html" class="external-link">registerDoParallel</a></span><span class="op">(</span>cores <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># ... or with doFuture</span></span></span>
 <span class="r-in"><span><span class="fu">doFuture</span><span class="fu">::</span><span class="fu"><a href="https://doFuture.futureverse.org/reference/registerDoFuture.html" class="external-link">registerDoFuture</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">future</span><span class="fu">::</span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="st">"multisession"</span>, workers <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -211,14 +196,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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index f52d3ff..9d6b05d 100644
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-Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) arXiv:2205.09435."><title>arf: Adversarial Random Forests — arf-package • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="arf: Adversarial Random Forests — arf-package"><meta property="og:description" content="
-Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) arXiv:2205.09435."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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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+Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) arXiv:2205.09435."><meta property="og:description" content="
+Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) arXiv:2205.09435."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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       <img src="../logo.png" class="logo" alt=""><h1>arf: Adversarial Random Forests</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/arf-package.R" class="external-link"><code>R/arf-package.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/arf-package.R" class="external-link"><code>R/arf-package.R</code></a></small>
       <div class="d-none name"><code>arf-package.Rd</code></div>
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@@ -76,8 +61,8 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <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"># Train ARF and estimate leaf parameters</span></span></span>
 <span class="r-in"><span><span class="va">arf</span> <span class="op">&lt;-</span> <span class="fu"><a href="adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 84.75%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 41.75%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 78.19%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 39.04%</span>
 <span class="r-in"><span><span class="va">psi</span> <span class="op">&lt;-</span> <span class="fu"><a href="forde.html">forde</a></span><span class="op">(</span><span class="va">arf</span>, <span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Generate 100 synthetic samples from the iris dataset</span></span></span>
@@ -91,25 +76,25 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Estimate average log-likelihood</span></span></span>
 <span class="r-in"><span><span class="va">ll</span> <span class="op">&lt;-</span> <span class="fu"><a href="lik.html">lik</a></span><span class="op">(</span><span class="va">psi</span>, <span class="va">iris</span>, arf <span class="op">=</span> <span class="va">arf</span>, log <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.7161056</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.4604258</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Expectation of Sepal.Length for class setosa</span></span></span>
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="expct.html">expct</a></span><span class="op">(</span><span class="va">psi</span>, query <span class="op">=</span> <span class="st">"Sepal.Length"</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Sepal.Length</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.012414</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.022068</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># Parallelization with doParallel</span></span></span>
 <span class="r-in"><span><span class="fu">doParallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/doParallel/man/registerDoParallel.html" class="external-link">registerDoParallel</a></span><span class="op">(</span>cores <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># ... or with doFuture</span></span></span>
 <span class="r-in"><span><span class="fu">doFuture</span><span class="fu">::</span><span class="fu"><a href="https://doFuture.futureverse.org/reference/registerDoFuture.html" class="external-link">registerDoFuture</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">future</span><span class="fu">::</span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="st">"multisession"</span>, workers <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -118,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 5943485..39e4e71 100644
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       <img src="../logo.png" class="logo" alt=""><h1>Compute conditional circuit parameters</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>cforde.Rd</code></div>
     </div>
 
@@ -66,44 +51,44 @@ <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>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>Circuit parameters learned via <code><a href="forde.html">forde</a></code>.</p></dd>
 
 
-<dt>evidence</dt>
+<dt id="arg-evidence">evidence<a class="anchor" aria-label="anchor" href="#arg-evidence"></a></dt>
 <dd><p>Data frame of conditioning event(s).</p></dd>
 
 
-<dt>row_mode</dt>
+<dt id="arg-row-mode">row_mode<a class="anchor" aria-label="anchor" href="#arg-row-mode"></a></dt>
 <dd><p>Interpretation of rows in multi-row conditions.</p></dd>
 
 
-<dt>nomatch</dt>
+<dt id="arg-nomatch">nomatch<a class="anchor" aria-label="anchor" href="#arg-nomatch"></a></dt>
 <dd><p>What to do if no leaf matches a condition in <code>evidence</code>?
 Options are to force sampling from a random leaf, either with a warning (<code>"force_warning"</code>)
 or without a warning (<code>"force"</code>), or to return <code>NA</code>, also with a warning
 (<code>"na_warning"</code>) or without a warning (<code>"na"</code>). The default is <code>"force_warning"</code>.</p></dd>
 
 
-<dt>stepsize</dt>
+<dt id="arg-stepsize">stepsize<a class="anchor" aria-label="anchor" href="#arg-stepsize"></a></dt>
 <dd><p>Stepsize defining number of condition rows handled in one for each step.</p></dd>
 
 
-<dt>parallel</dt>
+<dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt>
 <dd><p>Compute in parallel? Must register backend beforehand, e.g.
 via <code>doParallel</code> or <code>doFuture</code>; see examples.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>List with conditions (<code>evidence_input</code>), prepared conditions (<code>evidence_prepped</code>)
+    <p>List with conditions (<code>evidence_input</code>), prepared conditions (<code>evidence_prepped</code>)
 and leaves that match the conditions in evidence with continuous data (<code>cnt</code>)
 and categorical data (<code>cat</code>) as well as leaf info (<code>forest</code>).</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -112,14 +97,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/col_rename.html b/reference/col_rename.html
index dc1c7d2..d3c7411 100644
--- a/reference/col_rename.html
+++ b/reference/col_rename.html
@@ -1,52 +1,37 @@
 <!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 function renames columns in case the input colnames includes any
-colnames required by internal functions (e.g., "y").'><title>Adaptive column renaming — col_rename • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Adaptive column renaming — col_rename"><meta property="og:description" content='This function renames columns in case the input colnames includes any
-colnames required by internal functions (e.g., "y").'><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
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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"><title>Adaptive column renaming — col_rename • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Adaptive column renaming — col_rename"><meta name="description" content='This function renames columns in case the input colnames includes any
+colnames required by internal functions (e.g., "y").'><meta property="og:description" content='This function renames columns in case the input colnames includes any
+colnames required by internal functions (e.g., "y").'><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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+
 
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Adaptive column renaming</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>col_rename.Rd</code></div>
     </div>
 
@@ -62,16 +47,18 @@ <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>cn</dt>
+
+
+<dl><dt id="arg-cn">cn<a class="anchor" aria-label="anchor" href="#arg-cn"></a></dt>
 <dd><p>Column names.</p></dd>
 
 
-<dt>old_name</dt>
+<dt id="arg-old-name">old_name<a class="anchor" aria-label="anchor" href="#arg-old-name"></a></dt>
 <dd><p>Name of column to be renamed.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -80,14 +67,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/col_rename_all.html b/reference/col_rename_all.html
index 48bd63e..619326c 100644
--- a/reference/col_rename_all.html
+++ b/reference/col_rename_all.html
@@ -1,50 +1,35 @@
 <!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"><title>Rename all problematic columns with col_rename(). — col_rename_all • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Rename all problematic columns with col_rename(). — col_rename_all"><meta name="description" content="Rename all problematic columns with col_rename()."><meta property="og:description" content="Rename all problematic columns with col_rename()."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Rename all problematic columns with col_rename().</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>col_rename_all.Rd</code></div>
     </div>
 
@@ -59,18 +44,18 @@ <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>cn</dt>
+
+
+<dl><dt id="arg-cn">cn<a class="anchor" aria-label="anchor" href="#arg-cn"></a></dt>
 <dd><p>Old column names.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>New columns names.</p>
+    <p>New columns names.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -79,14 +64,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/darf.html b/reference/darf.html
index c436ef0..48b095f 100644
--- a/reference/darf.html
+++ b/reference/darf.html
@@ -1,54 +1,39 @@
 <!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="Calls adversarial_rf, forde and lik.
+<!-- 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>Shortcut likelihood function — darf • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Shortcut likelihood function — darf"><meta name="description" content="Calls adversarial_rf, forde and lik.
 For repeated application, it is faster to save outputs of adversarial_rf
-and forde and pass them via ... or directly use lik."><title>Shortcut likelihood function — darf • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Shortcut likelihood function — darf"><meta property="og:description" content="Calls adversarial_rf, forde and lik.
+and forde and pass them via ... or directly use lik."><meta property="og:description" content="Calls adversarial_rf, forde and lik.
 For repeated application, it is faster to save outputs of adversarial_rf
-and forde and pass them via ... or directly use lik."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
+and forde and pass them via ... or directly use lik."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light" data-bs-theme="light"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/arf.html">Get started</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Shortcut likelihood function</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/shortcut_functions.R" class="external-link"><code>R/shortcut_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/shortcut_functions.R" class="external-link"><code>R/shortcut_functions.R</code></a></small>
       <div class="d-none name"><code>darf.Rd</code></div>
     </div>
 
@@ -65,26 +50,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Input data. Integer variables are recoded as ordered factors with
 a warning. See Details.</p></dd>
 
 
-<dt>query</dt>
+<dt id="arg-query">query<a class="anchor" aria-label="anchor" href="#arg-query"></a></dt>
 <dd><p>Data frame of samples, optionally comprising just a subset of
 training features. See Details of <code>lik</code>. Is set to <code>x</code> if <code>zero</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Extra parameters to be passed to <code>adversarial_rf</code>, <code>forde</code>
 and <code>lik</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A vector of likelihoods, optionally on the log scale. A dataset of <code>n_synth</code> synthetic samples or of <code>nrow(x)</code> synthetic
+    <p>A vector of likelihoods, optionally on the log scale. A dataset of <code>n_synth</code> synthetic samples or of <code>nrow(x)</code> synthetic
 samples if <code>n_synth</code> is undefined.</p>
     </div>
     <div class="section level2">
@@ -113,7 +98,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -122,14 +107,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/earf.html b/reference/earf.html
index 3b3311f..8f22945 100644
--- a/reference/earf.html
+++ b/reference/earf.html
@@ -1,54 +1,39 @@
 <!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="Calls adversarial_rf, forde and expct.
+<!-- 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>Shortcut expectation function — earf • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Shortcut expectation function — earf"><meta name="description" content="Calls adversarial_rf, forde and expct.
 For repeated application, it is faster to save outputs of adversarial_rf
-and forde and pass them via ... or directly use expct."><title>Shortcut expectation function — earf • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Shortcut expectation function — earf"><meta property="og:description" content="Calls adversarial_rf, forde and expct.
+and forde and pass them via ... or directly use expct."><meta property="og:description" content="Calls adversarial_rf, forde and expct.
 For repeated application, it is faster to save outputs of adversarial_rf
-and forde and pass them via ... or directly use expct."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
+and forde and pass them via ... or directly use expct."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/arf.html">Get started</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Shortcut expectation function</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/shortcut_functions.R" class="external-link"><code>R/shortcut_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/shortcut_functions.R" class="external-link"><code>R/shortcut_functions.R</code></a></small>
       <div class="d-none name"><code>earf.Rd</code></div>
     </div>
 
@@ -65,21 +50,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Input data. Integer variables are recoded as ordered factors with
 a warning. See Details.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Extra parameters to be passed to <code>adversarial_rf</code>, <code>forde</code>
 and <code>expct</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A one row data frame with values for all query variables.</p>
+    <p>A one row data frame with values for all query variables.</p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
@@ -108,12 +93,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># What if we condition on Species = "setosa"?</span></span></span>
 <span class="r-in"><span><span class="fu">earf</span><span class="op">(</span><span class="va">iris</span>, query <span class="op">=</span> <span class="st">"Sepal.Length"</span>, evidence <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>Species <span class="op">=</span> <span class="st">"setosa"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Sepal.Length</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.021825</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.010467</span>
 <span class="r-in"><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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -122,14 +107,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/expct.html b/reference/expct.html
index d23e2fa..e3e1ab3 100644
--- a/reference/expct.html
+++ b/reference/expct.html
@@ -1,52 +1,37 @@
 <!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="Compute the expectation of some query variable(s), optionally conditioned
-on some event(s)."><title>Expected Value — expct • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Expected Value — expct"><meta property="og:description" content="Compute the expectation of some query variable(s), optionally conditioned
-on some event(s)."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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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+<!-- 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>Expected Value — expct • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Expected Value — expct"><meta name="description" content="Compute the expectation of some query variable(s), optionally conditioned
+on some event(s)."><meta property="og:description" content="Compute the expectation of some query variable(s), optionally conditioned
+on some event(s)."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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+
 
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Expected Value</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/expct.R" class="external-link"><code>R/expct.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/expct.R" class="external-link"><code>R/expct.R</code></a></small>
       <div class="d-none name"><code>expct.Rd</code></div>
     </div>
 
@@ -71,18 +56,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>Circuit parameters learned via <code><a href="forde.html">forde</a></code>.</p></dd>
 
 
-<dt>query</dt>
+<dt id="arg-query">query<a class="anchor" aria-label="anchor" href="#arg-query"></a></dt>
 <dd><p>Optional character vector of variable names. Estimates will be
 computed for each. If <code>NULL</code>, all variables other than those in
 <code>evidence</code> will be estimated. If evidence contains <code>NA</code>s, those
 variables will be estimated and a full dataset is returned.</p></dd>
 
 
-<dt>evidence</dt>
+<dt id="arg-evidence">evidence<a class="anchor" aria-label="anchor" href="#arg-evidence"></a></dt>
 <dd><p>Optional set of conditioning events. This can take one of
 three forms: (1) a partial sample, i.e. a single row of data with
 some but not all columns; (2) a data frame of conditioning events,
@@ -90,38 +77,36 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 see Details and Examples.</p></dd>
 
 
-<dt>evidence_row_mode</dt>
+<dt id="arg-evidence-row-mode">evidence_row_mode<a class="anchor" aria-label="anchor" href="#arg-evidence-row-mode"></a></dt>
 <dd><p>Interpretation of rows in multi-row evidence. If <code>'separate'</code>,
 each row in <code>evidence</code> is a separate conditioning event for which <code>n_synth</code> synthetic samples
 are generated. If <code>'or'</code>, the rows are combined with a logical or; see Examples.</p></dd>
 
 
-<dt>round</dt>
+<dt id="arg-round">round<a class="anchor" aria-label="anchor" href="#arg-round"></a></dt>
 <dd><p>Round continuous variables to their respective maximum precision in the real data set?</p></dd>
 
 
-<dt>nomatch</dt>
+<dt id="arg-nomatch">nomatch<a class="anchor" aria-label="anchor" href="#arg-nomatch"></a></dt>
 <dd><p>What to do if no leaf matches a condition in <code>evidence</code>?
 Options are to force sampling from a random leaf, either with a warning (<code>"force_warning"</code>)
 or without a warning (<code>"force"</code>), or to return <code>NA</code>, also with a warning
 (<code>"na_warning"</code>) or without a warning (<code>"na"</code>). The default is <code>"force_warning"</code>.</p></dd>
 
 
-<dt>stepsize</dt>
+<dt id="arg-stepsize">stepsize<a class="anchor" aria-label="anchor" href="#arg-stepsize"></a></dt>
 <dd><p>Stepsize defining number of evidence rows handled in one for each step.
 Defaults to nrow(evidence)/num_registered_workers for <code>parallel == TRUE</code>.</p></dd>
 
 
-<dt>parallel</dt>
+<dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt>
 <dd><p>Compute in parallel? Must register backend beforehand, e.g.
 via <code>doParallel</code> or <code>doFuture</code>; see examples.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A one row data frame with values for all query variables.</p>
+    <p>A one row data frame with values for all query variables.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -153,8 +138,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <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"># Train ARF and estimate leaf parameters</span></span></span>
 <span class="r-in"><span><span class="va">arf</span> <span class="op">&lt;-</span> <span class="fu"><a href="adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 90.44%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 43.43%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 77.03%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 40%</span>
 <span class="r-in"><span><span class="va">psi</span> <span class="op">&lt;-</span> <span class="fu"><a href="forde.html">forde</a></span><span class="op">(</span><span class="va">arf</span>, <span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># What is the expected value of Sepal.Length?</span></span></span>
@@ -166,12 +151,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">expct</span><span class="op">(</span><span class="va">psi</span>, query <span class="op">=</span> <span class="st">"Sepal.Length"</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Sepal.Length</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.016955</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.026998</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Compute expectations for all features other than Species</span></span></span>
 <span class="r-in"><span><span class="fu">expct</span><span class="op">(</span><span class="va">psi</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Sepal.Length Sepal.Width Petal.Length Petal.Width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.016955    3.420397     1.504233   0.2623856</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.026998    3.414044     1.542446   0.2771403</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Condition on first two data rows with some missing values</span></span></span>
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</span> <span class="va">iris</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>,<span class="op">]</span></span></span>
@@ -180,18 +165,18 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">evi</span><span class="op">[</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NA_real_</span></span></span>
 <span class="r-in"><span><span class="va">x_synth</span> <span class="op">&lt;-</span> <span class="fu">expct</span><span class="op">(</span><span class="va">psi</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># Parallelization with doParallel</span></span></span>
 <span class="r-in"><span><span class="fu">doParallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/doParallel/man/registerDoParallel.html" class="external-link">registerDoParallel</a></span><span class="op">(</span>cores <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># ... or with doFuture</span></span></span>
 <span class="r-in"><span><span class="fu">doFuture</span><span class="fu">::</span><span class="fu"><a href="https://doFuture.futureverse.org/reference/registerDoFuture.html" class="external-link">registerDoFuture</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">future</span><span class="fu">::</span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="st">"multisession"</span>, workers <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -200,14 +185,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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index 4ac1775..438540d 100644
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@@ -1,50 +1,35 @@
 <!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"><title>Forests for Density Estimation — forde • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Forests for Density Estimation — forde"><meta name="description" content="Uses a pre-trained ARF model to estimate leaf and distribution parameters."><meta property="og:description" content="Uses a pre-trained ARF model to estimate leaf and distribution parameters."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
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     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Forests for Density Estimation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/forde.R" class="external-link"><code>R/forde.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/forde.R" class="external-link"><code>R/forde.R</code></a></small>
       <div class="d-none name"><code>forde.Rd</code></div>
     </div>
 
@@ -68,42 +53,44 @@ <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>arf</dt>
+
+
+<dl><dt id="arg-arf">arf<a class="anchor" aria-label="anchor" href="#arg-arf"></a></dt>
 <dd><p>Pre-trained <code><a href="adversarial_rf.html">adversarial_rf</a></code>. Alternatively, any
 object of class <code>ranger</code>.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Training data for estimating parameters.</p></dd>
 
 
-<dt>oob</dt>
+<dt id="arg-oob">oob<a class="anchor" aria-label="anchor" href="#arg-oob"></a></dt>
 <dd><p>Only use out-of-bag samples for parameter estimation? If
 <code>TRUE</code>, <code>x</code> must be the same dataset used to train <code>arf</code>.</p></dd>
 
 
-<dt>family</dt>
+<dt id="arg-family">family<a class="anchor" aria-label="anchor" href="#arg-family"></a></dt>
 <dd><p>Distribution to use for density estimation of continuous
 features. Current options include truncated normal (the default
 <code>family = "truncnorm"</code>) and uniform (<code>family = "unif"</code>). See
 Details.</p></dd>
 
 
-<dt>finite_bounds</dt>
+<dt id="arg-finite-bounds">finite_bounds<a class="anchor" aria-label="anchor" href="#arg-finite-bounds"></a></dt>
 <dd><p>Impose finite bounds on all continuous variables? If
 <code>'local'</code>, infinite bounds are shrinked to empirical extrema within leaves.
 If <code>'global'</code>, infinite bounds are shrinked to global empirical extrema.
 if <code>'no'</code> (the default), do not impose finite bounds.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>Optional pseudocount for Laplace smoothing of categorical
 features. This avoids zero-mass points when test data fall outside the
 support of training data. Effectively parametrizes a flat Dirichlet prior
 on multinomial likelihoods.</p></dd>
 
 
-<dt>epsilon</dt>
+<dt id="arg-epsilon">epsilon<a class="anchor" aria-label="anchor" href="#arg-epsilon"></a></dt>
 <dd><p>Optional slack parameter on empirical bounds when
 <code>finite_bounds != 'no'</code>. This avoids
 zero-density points when test data fall outside the support of training
@@ -111,16 +98,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>1 + epsilon</code>.</p></dd>
 
 
-<dt>parallel</dt>
+<dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt>
 <dd><p>Compute in parallel? Must register backend beforehand, e.g.
 via <code>doParallel</code> or <code>doFuture</code>; see examples.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A <code>list</code> with 5 elements: (1) parameters for continuous data; (2)
+    <p>A <code>list</code> with 5 elements: (1) parameters for continuous data; (2)
 parameters for discrete data; (3) leaf indices and coverage; (4) metadata on
 variables; and (5) the data input class. This list is used for estimating
 likelihoods with <code><a href="lik.html">lik</a></code> and generating data with <code><a href="forge.html">forge</a></code>.</p>
@@ -164,8 +149,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <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"># Train ARF and estimate leaf parameters</span></span></span>
 <span class="r-in"><span><span class="va">arf</span> <span class="op">&lt;-</span> <span class="fu"><a href="adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 86.2%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 42.37%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 78.52%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 40.82%</span>
 <span class="r-in"><span><span class="va">psi</span> <span class="op">&lt;-</span> <span class="fu">forde</span><span class="op">(</span><span class="va">arf</span>, <span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Generate 100 synthetic samples from the iris dataset</span></span></span>
@@ -179,26 +164,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Estimate average log-likelihood</span></span></span>
 <span class="r-in"><span><span class="va">ll</span> <span class="op">&lt;-</span> <span class="fu"><a href="lik.html">lik</a></span><span class="op">(</span><span class="va">psi</span>, <span class="va">iris</span>, arf <span class="op">=</span> <span class="va">arf</span>, log <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.7392139</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.4118479</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Expectation of Sepal.Length for class setosa</span></span></span>
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="expct.html">expct</a></span><span class="op">(</span><span class="va">psi</span>, query <span class="op">=</span> <span class="st">"Sepal.Length"</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Sepal.Length</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     5.011261</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1      5.01768</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># Parallelization with doParallel</span></span></span>
 <span class="r-in"><span><span class="fu">doParallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/doParallel/man/registerDoParallel.html" class="external-link">registerDoParallel</a></span><span class="op">(</span>cores <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># ... or with doFuture</span></span></span>
 <span class="r-in"><span><span class="fu">doFuture</span><span class="fu">::</span><span class="fu"><a href="https://doFuture.futureverse.org/reference/registerDoFuture.html" class="external-link">registerDoFuture</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">future</span><span class="fu">::</span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="st">"multisession"</span>, workers <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -207,14 +192,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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index 2765eaf..7a5db1e 100644
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+++ b/reference/forge.html
@@ -1,50 +1,35 @@
 <!DOCTYPE html>
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-<![endif]--></head><body>
+<!-- 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>Forests for Generative Modeling — forge • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Forests for Generative Modeling — forge"><meta name="description" content="Uses pre-trained FORDE model to simulate synthetic data."><meta property="og:description" content="Uses pre-trained FORDE model to simulate synthetic data."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Forests for Generative Modeling</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/forge.R" class="external-link"><code>R/forge.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/forge.R" class="external-link"><code>R/forge.R</code></a></small>
       <div class="d-none name"><code>forge.Rd</code></div>
     </div>
 
@@ -69,15 +54,17 @@ <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>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>Circuit parameters learned via <code><a href="forde.html">forde</a></code>.</p></dd>
 
 
-<dt>n_synth</dt>
+<dt id="arg-n-synth">n_synth<a class="anchor" aria-label="anchor" href="#arg-n-synth"></a></dt>
 <dd><p>Number of synthetic samples to generate.</p></dd>
 
 
-<dt>evidence</dt>
+<dt id="arg-evidence">evidence<a class="anchor" aria-label="anchor" href="#arg-evidence"></a></dt>
 <dd><p>Optional set of conditioning events. This can take one of
 three forms: (1) a partial sample, i.e. a single row of data with
 some but not all columns; (2) a data frame of conditioning events,
@@ -85,42 +72,40 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 see Details and Examples.</p></dd>
 
 
-<dt>evidence_row_mode</dt>
+<dt id="arg-evidence-row-mode">evidence_row_mode<a class="anchor" aria-label="anchor" href="#arg-evidence-row-mode"></a></dt>
 <dd><p>Interpretation of rows in multi-row evidence. If <code>'separate'</code>,
 each row in <code>evidence</code> is a separate conditioning event for which <code>n_synth</code> synthetic samples
 are generated. If <code>'or'</code>, the rows are combined with a logical or; see Examples.</p></dd>
 
 
-<dt>round</dt>
+<dt id="arg-round">round<a class="anchor" aria-label="anchor" href="#arg-round"></a></dt>
 <dd><p>Round continuous variables to their respective maximum precision in the real data set?</p></dd>
 
 
-<dt>sample_NAs</dt>
+<dt id="arg-sample-nas">sample_NAs<a class="anchor" aria-label="anchor" href="#arg-sample-nas"></a></dt>
 <dd><p>Sample NAs respecting the probability for missing values in the original data.</p></dd>
 
 
-<dt>nomatch</dt>
+<dt id="arg-nomatch">nomatch<a class="anchor" aria-label="anchor" href="#arg-nomatch"></a></dt>
 <dd><p>What to do if no leaf matches a condition in <code>evidence</code>?
 Options are to force sampling from a random leaf, either with a warning (<code>"force_warning"</code>)
 or without a warning (<code>"force"</code>), or to return <code>NA</code>, also with a warning
 (<code>"na_warning"</code>) or without a warning (<code>"na"</code>). The default is <code>"force_warning"</code>.</p></dd>
 
 
-<dt>stepsize</dt>
+<dt id="arg-stepsize">stepsize<a class="anchor" aria-label="anchor" href="#arg-stepsize"></a></dt>
 <dd><p>Stepsize defining number of evidence rows handled in one for each step.
 Defaults to nrow(evidence)/num_registered_workers for <code>parallel == TRUE</code>.</p></dd>
 
 
-<dt>parallel</dt>
+<dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt>
 <dd><p>Compute in parallel? Must register backend beforehand, e.g.
 via <code>doParallel</code> or <code>doFuture</code>; see examples.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A dataset of <code>n_synth</code> synthetic samples.</p>
+    <p>A dataset of <code>n_synth</code> synthetic samples.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -155,8 +140,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <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"># Train ARF and estimate leaf parameters</span></span></span>
 <span class="r-in"><span><span class="va">arf</span> <span class="op">&lt;-</span> <span class="fu"><a href="adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 82.94%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 41.41%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 76.61%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 42.47%</span>
 <span class="r-in"><span><span class="va">psi</span> <span class="op">&lt;-</span> <span class="fu"><a href="forde.html">forde</a></span><span class="op">(</span><span class="va">arf</span>, <span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Generate 100 synthetic samples from the iris dataset</span></span></span>
@@ -192,18 +177,18 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</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>f_idx <span class="op">=</span> <span class="va">psi</span><span class="op">$</span><span class="va">forest</span><span class="op">$</span><span class="va">f_idx</span>, wt <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Exponential.html" class="external-link">rexp</a></span><span class="op">(</span><span class="va">n_leaves</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">x_synth</span> <span class="op">&lt;-</span> <span class="fu">forge</span><span class="op">(</span><span class="va">psi</span>, n_synth <span class="op">=</span> <span class="fl">100</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># Parallelization with doParallel</span></span></span>
 <span class="r-in"><span><span class="fu">doParallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/doParallel/man/registerDoParallel.html" class="external-link">registerDoParallel</a></span><span class="op">(</span>cores <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># ... or with doFuture</span></span></span>
 <span class="r-in"><span><span class="fu">doFuture</span><span class="fu">::</span><span class="fu"><a href="https://doFuture.futureverse.org/reference/registerDoFuture.html" class="external-link">registerDoFuture</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">future</span><span class="fu">::</span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="st">"multisession"</span>, workers <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -212,14 +197,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
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diff --git a/reference/impute.html b/reference/impute.html
new file mode 100644
index 0000000..be74e11
--- /dev/null
+++ b/reference/impute.html
@@ -0,0 +1,152 @@
+<!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>Missing value imputation with ARF — impute • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Missing value imputation with ARF — impute"><meta name="description" content="Imputed a dataset with missing values using adversarial random forests (ARF).
+Calls adversarial_rf, forde and expct/forge."><meta property="og:description" content="Imputed a dataset with missing values using adversarial random forests (ARF).
+Calls adversarial_rf, forde and expct/forge."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">arf</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/arf.html">Get started</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.png" class="logo" alt=""><h1>Missing value imputation with ARF</h1>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/impute.R" class="external-link"><code>R/impute.R</code></a></small>
+      <div class="d-none name"><code>impute.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Imputed a dataset with missing values using adversarial random forests (ARF).
+Calls <code>adversarial_rf</code>, <code>forde</code> and <code>expct</code>/<code>forge</code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">impute</span><span class="op">(</span></span>
+<span>  <span class="va">x</span>,</span>
+<span>  m <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  expectation <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">m</span> <span class="op">==</span> <span class="fl">1</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span>,</span>
+<span>  num_trees <span class="op">=</span> <span class="fl">100L</span>,</span>
+<span>  min_node_size <span class="op">=</span> <span class="fl">10L</span>,</span>
+<span>  round <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  finite_bounds <span class="op">=</span> <span class="st">"local"</span>,</span>
+<span>  epsilon <span class="op">=</span> <span class="fl">1e-14</span>,</span>
+<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>Input data.</p></dd>
+
+
+<dt id="arg-m">m<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
+<dd><p>Number of multiple imputations. The default is single imputation (<code>m=1</code>).</p></dd>
+
+
+<dt id="arg-expectation">expectation<a class="anchor" aria-label="anchor" href="#arg-expectation"></a></dt>
+<dd><p>Return expected value instead of multiple imputations. By default, for single imputation (<code>m=1</code>), the expected value is returned.</p></dd>
+
+
+<dt id="arg-num-trees">num_trees<a class="anchor" aria-label="anchor" href="#arg-num-trees"></a></dt>
+<dd><p>Number of trees in ARF.</p></dd>
+
+
+<dt id="arg-min-node-size">min_node_size<a class="anchor" aria-label="anchor" href="#arg-min-node-size"></a></dt>
+<dd><p>Minimum node size in ARF.</p></dd>
+
+
+<dt id="arg-round">round<a class="anchor" aria-label="anchor" href="#arg-round"></a></dt>
+<dd><p>Round imputed values to their respective maximum precision in the original data set?</p></dd>
+
+
+<dt id="arg-finite-bounds">finite_bounds<a class="anchor" aria-label="anchor" href="#arg-finite-bounds"></a></dt>
+<dd><p>Impose finite bounds on all continuous variables? See <code><a href="forde.html">forde</a></code>.</p></dd>
+
+
+<dt id="arg-epsilon">epsilon<a class="anchor" aria-label="anchor" href="#arg-epsilon"></a></dt>
+<dd><p>Slack parameter on empirical bounds; see <code><a href="forde.html">forde</a></code>.</p></dd>
+
+
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
+<dd><p>Print progress for <code>adversarial_rf</code>?</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>Extra parameters to be passed to <code>adversarial_rf</code>, <code>forde</code>
+and <code>expct</code>/<code>forge</code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Imputed data. A single data table is returned for <code>m=1</code> and a list of data table for <code>m &gt; 1</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"># Generate some missings</span></span></span>
+<span class="r-in"><span><span class="va">iris_na</span> <span class="op">&lt;-</span> <span class="va">iris</span></span></span>
+<span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">j</span> <span class="kw">in</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">ncol</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span>  <span class="va">iris_na</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></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">iris</span><span class="op">)</span>, <span class="fl">5</span><span class="op">)</span>, <span class="va">j</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># Single imputation</span></span></span>
+<span class="r-in"><span><span class="va">iris_imputed</span> <span class="op">&lt;-</span> <span class="fu">arf</span><span class="fu">::</span><span class="fu">impute</span><span class="op">(</span><span class="va">iris_na</span>, m <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"># Multiple imputation</span></span></span>
+<span class="r-in"><span><span class="va">iris_imputed</span> <span class="op">&lt;-</span> <span class="fu">arf</span><span class="fu">::</span><span class="fu">impute</span><span class="op">(</span><span class="va">iris_na</span>, m <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="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="co"># Parallelization with doParallel</span></span></span>
+<span class="r-in"><span><span class="fu">doParallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/doParallel/man/registerDoParallel.html" class="external-link">registerDoParallel</a></span><span class="op">(</span>cores <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># ... or with doFuture</span></span></span>
+<span class="r-in"><span><span class="fu">doFuture</span><span class="fu">::</span><span class="fu"><a href="https://doFuture.futureverse.org/reference/registerDoFuture.html" class="external-link">registerDoFuture</a></span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">future</span><span class="fu">::</span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="st">"multisession"</span>, workers <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Marvin N. Wright, David S. Watson, Kristin Blesch, Jan Kapar.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+</div>
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+
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+
+
+
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+
diff --git a/reference/index.html b/reference/index.html
index ef9fb4d..91262fc 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -1,105 +1,105 @@
 <!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 • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
+<!-- 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>Package index • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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       <dl><dt>
-          
-          <code><a href="adversarial_rf.html">adversarial_rf()</a></code> 
+
+          <code><a href="adversarial_rf.html">adversarial_rf()</a></code>
+
         </dt>
         <dd>Adversarial Random Forests</dd>
       </dl><dl><dt>
-          
-          <code><a href="arf-package.html">arf</a></code> <code><a href="arf-package.html">arf-package</a></code> 
+
+          <code><a href="arf-package.html">arf</a></code> <code><a href="arf-package.html">arf-package</a></code>
+
         </dt>
         <dd>arf: Adversarial Random Forests</dd>
       </dl><dl><dt>
-          
-          <code><a href="darf.html">darf()</a></code> 
+
+          <code><a href="darf.html">darf()</a></code>
+
         </dt>
         <dd>Shortcut likelihood function</dd>
       </dl><dl><dt>
-          
-          <code><a href="earf.html">earf()</a></code> 
+
+          <code><a href="earf.html">earf()</a></code>
+
         </dt>
         <dd>Shortcut expectation function</dd>
       </dl><dl><dt>
-          
-          <code><a href="expct.html">expct()</a></code> 
+
+          <code><a href="expct.html">expct()</a></code>
+
         </dt>
         <dd>Expected Value</dd>
       </dl><dl><dt>
-          
-          <code><a href="forde.html">forde()</a></code> 
+
+          <code><a href="forde.html">forde()</a></code>
+
         </dt>
         <dd>Forests for Density Estimation</dd>
       </dl><dl><dt>
-          
-          <code><a href="forge.html">forge()</a></code> 
+
+          <code><a href="forge.html">forge()</a></code>
+
         </dt>
         <dd>Forests for Generative Modeling</dd>
       </dl><dl><dt>
-          
-          <code><a href="lik.html">lik()</a></code> 
+
+          <code><a href="impute.html">impute()</a></code>
+
+        </dt>
+        <dd>Missing value imputation with ARF</dd>
+      </dl><dl><dt>
+
+          <code><a href="lik.html">lik()</a></code>
+
         </dt>
         <dd>Likelihood Estimation</dd>
       </dl><dl><dt>
-          
-          <code><a href="rarf.html">rarf()</a></code> 
+
+          <code><a href="rarf.html">rarf()</a></code>
+
         </dt>
         <dd>Shortcut sampling function</dd>
       </dl></div>
@@ -111,14 +111,14 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
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index 71256d8..0232c2d 100644
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 <!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="Compute the likelihood of input data, optionally conditioned on some event(s)."><title>Likelihood Estimation — lik • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Likelihood Estimation — lik"><meta property="og:description" content="Compute the likelihood of input data, optionally conditioned on some event(s)."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Likelihood Estimation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/lik.R" class="external-link"><code>R/lik.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/lik.R" class="external-link"><code>R/lik.R</code></a></small>
       <div class="d-none name"><code>lik.Rd</code></div>
     </div>
 
@@ -68,56 +53,56 @@ <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>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>Circuit parameters learned via <code><a href="forde.html">forde</a></code>.</p></dd>
 
 
-<dt>query</dt>
+<dt id="arg-query">query<a class="anchor" aria-label="anchor" href="#arg-query"></a></dt>
 <dd><p>Data frame of samples, optionally comprising just a subset of
 training features. Likelihoods will be computed for each sample. Missing
 features will be marginalized out. See Details.</p></dd>
 
 
-<dt>evidence</dt>
+<dt id="arg-evidence">evidence<a class="anchor" aria-label="anchor" href="#arg-evidence"></a></dt>
 <dd><p>Optional set of conditioning events. This can take one of
 three forms: (1) a partial sample, i.e. a single row of data with some but
 not all columns; (2) a data frame of conditioning events, which allows for
 inequalities; or (3) a posterior distribution over leaves. See Details.</p></dd>
 
 
-<dt>arf</dt>
+<dt id="arg-arf">arf<a class="anchor" aria-label="anchor" href="#arg-arf"></a></dt>
 <dd><p>Pre-trained <code><a href="adversarial_rf.html">adversarial_rf</a></code> or other object of class
 <code>ranger</code>. This is not required but speeds up computation considerably
 for total evidence queries. (Ignored for partial evidence queries.)</p></dd>
 
 
-<dt>oob</dt>
+<dt id="arg-oob">oob<a class="anchor" aria-label="anchor" href="#arg-oob"></a></dt>
 <dd><p>Only use out-of-bag leaves for likelihood estimation? If
 <code>TRUE</code>, <code>x</code> must be the same dataset used to train <code>arf</code>.
 Only applicable for total evidence queries.</p></dd>
 
 
-<dt>log</dt>
+<dt id="arg-log">log<a class="anchor" aria-label="anchor" href="#arg-log"></a></dt>
 <dd><p>Return likelihoods on log scale? Recommended to prevent underflow.</p></dd>
 
 
-<dt>batch</dt>
+<dt id="arg-batch">batch<a class="anchor" aria-label="anchor" href="#arg-batch"></a></dt>
 <dd><p>Batch size. The default is to compute densities for all of
 queries in one round, which is always the fastest option if memory allows.
 However, with large samples or many trees, it can be more memory efficient
 to split the data into batches. This has no impact on results.</p></dd>
 
 
-<dt>parallel</dt>
+<dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt>
 <dd><p>Compute in parallel? Must register backend beforehand, e.g.
 via <code>doParallel</code> or <code>doFuture</code>; see examples.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A vector of likelihoods, optionally on the log scale.</p>
+    <p>A vector of likelihoods, optionally on the log scale.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -149,48 +134,48 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <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"># Train ARF and estimate leaf parameters</span></span></span>
 <span class="r-in"><span><span class="va">arf</span> <span class="op">&lt;-</span> <span class="fu"><a href="adversarial_rf.html">adversarial_rf</a></span><span class="op">(</span><span class="va">iris</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 82.65%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 38.67%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 0, Accuracy: 72.05%</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Iteration: 1, Accuracy: 35.35%</span>
 <span class="r-in"><span><span class="va">psi</span> <span class="op">&lt;-</span> <span class="fu"><a href="forde.html">forde</a></span><span class="op">(</span><span class="va">arf</span>, <span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Estimate average log-likelihood</span></span></span>
 <span class="r-in"><span><span class="va">ll</span> <span class="op">&lt;-</span> <span class="fu">lik</span><span class="op">(</span><span class="va">psi</span>, <span class="va">iris</span>, arf <span class="op">=</span> <span class="va">arf</span>, log <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.6148863</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.3617124</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Identical but slower</span></span></span>
 <span class="r-in"><span><span class="va">ll</span> <span class="op">&lt;-</span> <span class="fu">lik</span><span class="op">(</span><span class="va">psi</span>, <span class="va">iris</span>, log <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>For total evidence queries, it is faster to include the pre-trained arf.</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">ll</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.6148863</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.3617124</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Partial evidence query</span></span></span>
 <span class="r-in"><span><span class="fu">lik</span><span class="op">(</span><span class="va">psi</span>, query <span class="op">=</span> <span class="va">iris</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.5710183</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.4205125</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Condition on Species = "setosa"</span></span></span>
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">lik</span><span class="op">(</span><span class="va">psi</span>, query <span class="op">=</span> <span class="va">iris</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.668789</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.519125</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Condition on Species = "setosa" and Petal.Width &gt; 0.3</span></span></span>
 <span class="r-in"><span><span class="va">evi</span> <span class="op">&lt;-</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>Species <span class="op">=</span> <span class="st">"setosa"</span>, </span></span>
 <span class="r-in"><span>                  Petal.Width <span class="op">=</span> <span class="st">"&gt;0.3"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">lik</span><span class="op">(</span><span class="va">psi</span>, query <span class="op">=</span> <span class="va">iris</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, evidence <span class="op">=</span> <span class="va">evi</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.323916</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.703543</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># Parallelization with doParallel</span></span></span>
 <span class="r-in"><span><span class="fu">doParallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/doParallel/man/registerDoParallel.html" class="external-link">registerDoParallel</a></span><span class="op">(</span>cores <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># ... or with doFuture</span></span></span>
 <span class="r-in"><span><span class="fu">doFuture</span><span class="fu">::</span><span class="fu"><a href="https://doFuture.futureverse.org/reference/registerDoFuture.html" class="external-link">registerDoFuture</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">future</span><span class="fu">::</span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="st">"multisession"</span>, workers <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -199,14 +184,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/post_x.html b/reference/post_x.html
index b9c737f..d5b68f9 100644
--- a/reference/post_x.html
+++ b/reference/post_x.html
@@ -1,50 +1,35 @@
 <!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 function prepares output data for forge."><title>Post-process data — post_x • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Post-process data — post_x"><meta property="og:description" content="This function prepares output data for forge."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Post-process data</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>post_x.Rd</code></div>
     </div>
 
@@ -59,20 +44,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Input data.frame.</p></dd>
 
 
-<dt>params</dt>
+<dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>Circuit parameters learned via <code><a href="forde.html">forde</a></code>.</p></dd>
 
 
-<dt>round</dt>
+<dt id="arg-round">round<a class="anchor" aria-label="anchor" href="#arg-round"></a></dt>
 <dd><p>Round continuous variables to their respective maximum precision in the real data set?</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -81,14 +68,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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+
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diff --git a/reference/prep_cond.html b/reference/prep_cond.html
index 0d4c477..3e3c0d2 100644
--- a/reference/prep_cond.html
+++ b/reference/prep_cond.html
@@ -1,50 +1,35 @@
 <!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 function prepares conditions for computing conditional circuit paramaters via cforde"><title>Preprocess conditions — prep_cond • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Preprocess conditions — prep_cond"><meta property="og:description" content="This function prepares conditions for computing conditional circuit paramaters via cforde"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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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       <img src="../logo.png" class="logo" alt=""><h1>Preprocess conditions</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>prep_cond.Rd</code></div>
     </div>
 
@@ -59,20 +44,22 @@ <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>evidence</dt>
+
+
+<dl><dt id="arg-evidence">evidence<a class="anchor" aria-label="anchor" href="#arg-evidence"></a></dt>
 <dd><p>Optional set of conditioning events.</p></dd>
 
 
-<dt>params</dt>
+<dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>Circuit parameters learned via <code><a href="forde.html">forde</a></code>.</p></dd>
 
 
-<dt>row_mode</dt>
+<dt id="arg-row-mode">row_mode<a class="anchor" aria-label="anchor" href="#arg-row-mode"></a></dt>
 <dd><p>Interpretation of rows in multi-row conditions.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -81,14 +68,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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 <!DOCTYPE html>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
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       <img src="../logo.png" class="logo" alt=""><h1>Preprocess input data</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>prep_x.Rd</code></div>
     </div>
 
@@ -59,12 +44,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Input data.frame.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -73,14 +60,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/rarf.html b/reference/rarf.html
index 43d645f..1753c61 100644
--- a/reference/rarf.html
+++ b/reference/rarf.html
@@ -1,54 +1,39 @@
 <!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="Calls adversarial_rf, forde and forge.
+<!-- 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>Shortcut sampling function — rarf • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Shortcut sampling function — rarf"><meta name="description" content="Calls adversarial_rf, forde and forge.
 For repeated application, it is faster to save outputs of adversarial_rf
-and forde and pass them via ... or directly use forge."><title>Shortcut sampling function — rarf • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Shortcut sampling function — rarf"><meta property="og:description" content="Calls adversarial_rf, forde and forge.
+and forde and pass them via ... or directly use forge."><meta property="og:description" content="Calls adversarial_rf, forde and forge.
 For repeated application, it is faster to save outputs of adversarial_rf
-and forde and pass them via ... or directly use forge."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
+and forde and pass them via ... or directly use forge."><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-  <a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/arf.html">Get started</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Shortcut sampling function</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/shortcut_functions.R" class="external-link"><code>R/shortcut_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/shortcut_functions.R" class="external-link"><code>R/shortcut_functions.R</code></a></small>
       <div class="d-none name"><code>rarf.Rd</code></div>
     </div>
 
@@ -65,26 +50,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Input data. Integer variables are recoded as ordered factors with
 a warning. See Details.</p></dd>
 
 
-<dt>n_synth</dt>
+<dt id="arg-n-synth">n_synth<a class="anchor" aria-label="anchor" href="#arg-n-synth"></a></dt>
 <dd><p>Number of synthetic samples to generate. Is set to <code>nrow(x)</code> if
 <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Extra parameters to be passed to <code>adversarial_rf</code>, <code>forde</code>
 and <code>forge</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A dataset of <code>n_synth</code> synthetic samples or of <code>nrow(x)</code> synthetic
+    <p>A dataset of <code>n_synth</code> synthetic samples or of <code>nrow(x)</code> synthetic
 samples if <code>n_synth</code> is undefined.</p>
     </div>
     <div class="section level2">
@@ -112,7 +97,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <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>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -121,14 +106,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/resample.html b/reference/resample.html
index a39fc40..75c6cf4 100644
--- a/reference/resample.html
+++ b/reference/resample.html
@@ -1,50 +1,35 @@
 <!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="Safer version of sample()"><title>Safer version of sample() — resample • arf</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><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/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></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="Safer version of sample() — resample"><meta property="og:description" content="Safer version of sample()"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"><!-- 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>
+<!-- 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>Safer version of sample() — resample • arf</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><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Safer version of sample() — resample"><meta name="description" content="Safer version of sample()"><meta property="og:description" content="Safer version of sample()"><meta property="og:image" content="https://bips-hb.github.io/arf/logo.png"></head><body>
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-    
 
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">arf</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.2</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.3</small>
+
 
-    
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+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/arf.html">Get started</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/bips-hb/arf/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Safer version of sample()</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/main/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>resample.Rd</code></div>
     </div>
 
@@ -59,22 +44,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A vector of one or more elements from which to choose.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments for sample().</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A vector of length size with elements drawn from x.</p>
+    <p>A vector of length size with elements drawn from x.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -83,14 +68,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
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       <img src="../logo.png" class="logo" alt=""><h1>which.max() with random at ties</h1>
-      <small class="dont-index">Source: <a href="https://github.com/bips-hb/arf/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
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       <div class="d-none name"><code>which.max.random.Rd</code></div>
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-    <dl><dt>x</dt>
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 <dd><p>A numeric vector.</p></dd>
 
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     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
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-<p>Index of maximum value in x, with random tie-breaking.</p>
+    <p>Index of maximum value in x, with random tie-breaking.</p>
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diff --git a/search.json b/search.json
index 21cbe78..4099c72 100644
--- a/search.json
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@@ -1 +1 @@
-[{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"adversarial-training","dir":"Articles","previous_headings":"","what":"Adversarial Training","title":"Adversarial Random Forests","text":"ARF algorithm iterative procedure. first instance, generate synthetic data independently sampling marginals feature training random forest (RF) distinguish original synthetic samples. accuracy greater \\(0.5 + \\delta\\) (delta user-controlled tolerance parameter, generally set 0), create new dataset sampling marginals within leaf training another RF classifier. procedure repeats original synthetic samples reliably distinguished. default verbose = TRUE, algorithm print accuracy iteration. printouts can turned setting verbose = FALSE. Accuracy still stored within arf object, can evaluate convergence fact. warning appears just per session. can suppressed setting parallel = FALSE registering parallel backend ().  find quick drop accuracy following resampling procedure, desired. ARF converged, resulting splits form fully factorized leaves, .e. subregions feature space variables locally independent. ARF convergence asymptotically guaranteed \\(n \\rightarrow \\infty\\) (see Watson et al., 2023, Thm. 1). However, implications finite sample performance. practice, often find adversarial training completes just one two rounds, may hold datasets. avoid infinite loops, users can increase slack parameter delta set max_iters argument (default = 10). addition failsafes, adversarial_rf uses early stopping default (early_stop = TRUE), terminates training factorization improve one round next. recommended, since discriminator accuracy rarely falls much lower increased. density estimation tasks, recommend increasing default number trees. generally use 100 experiments, though may suboptimal datasets. Likelihood estimates sensitive parameter certain threshold, larger models incur extra costs time memory. can speed computations registering parallel backend, case ARF training distributed across cores using ranger package. Much like ranger, default behavior adversarial_rf compute parallel possible. exactly done varies across operating systems. following code works Unix machines. Windows requires different setup. either case, can now execute parallel. result object class ranger, can input downstream functions.","code":"# Load libraries library(arf) library(data.table) library(ggplot2)  # Set seed set.seed(123, \"L'Ecuyer-CMRG\")  # Train ARF arf_iris <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 85.52% #> Iteration: 1, Accuracy: 42.33% #> Warning: executing %dopar% sequentially: no parallel backend registered # Train ARF with no printouts arf_iris <- adversarial_rf(iris, verbose = FALSE)  # Plot accuracy against iterations (model converges when accuracy <= 0.5) tmp <- data.frame('Accuracy' = arf_iris$acc,                    'Iteration' = seq_len(length(arf_iris$acc))) ggplot(tmp, aes(Iteration, Accuracy)) +    geom_point() +    geom_path() +   geom_hline(yintercept = 0.5, linetype = 'dashed', color = 'red') # Register cores - Unix library(doParallel) registerDoParallel(cores = 2) # Register cores - Windows library(doParallel) cl <- makeCluster(2) registerDoParallel(cl) # Rerun ARF, now in parallel and with more trees arf_iris <- adversarial_rf(iris, num_trees = 100) #> Iteration: 0, Accuracy: 91.33% #> Iteration: 1, Accuracy: 33%"},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"parameter-learning","dir":"Articles","previous_headings":"","what":"Parameter Learning","title":"Adversarial Random Forests","text":"next step learn leaf distribution parameters using forests density estimation (FORDE). function calculates coverage, bounds, pdf/pmf parameters every variable every leaf. can expensive computation large datasets, requires \\(\\mathcal{O}\\big(B \\cdot d \\cdot n \\cdot \\log(n)\\big)\\) operations, \\(B\\) number trees, \\(d\\) data dimensionality, \\(n\\) sample size. , process parallelized default. Default behavior use truncated normal distribution continuous data (boundaries given tree’s split parameters) multinomial distribution categorical data. find produces stable results wide range settings. can also use uniform distribution continuous features setting family = 'unif', thereby instantiating piecewise constant density estimator. method tends perform poorly practice, recommend . option implemented primarily benchmarking purposes. Alternative families, e.g. truncated Poisson beta distributions, may useful certain problems. Future releases expand range options family argument. alpha epsilon arguments allow optional regularization multinomial uniform distributions, respectively. help prevent zero likelihood samples test data fall outside support training data. former pseudocount parameter applies Laplace smoothing within leaves, preventing unobserved values assigned zero probability unless splits explicitly rule . words, impose flat Dirichlet prior report posterior probabilities rather maximum likelihood estimates. latter slack parameter empirical bounds expands estimated extrema continuous features factor \\(1 + \\epsilon\\). Compare results original probability estimates Species variable obtained adding pseudocount \\(\\alpha = 0.1\\). Laplace smoothing, extreme probabilities occur splits explicitly demand . Otherwise, values shrink toward uniform prior. Note two data tables may exactly rows, omit zero probability events conserve memory. However, can verify probabilities sum unity leaf-variable combination. forde function outputs list length 6, entries (1) continuous features; (2) categorical features; (3) leaf parameters; (4) variable metadata; (5) factor levels; (6) data input class. parameters can used variety downstream tasks, likelihood estimation data synthesis.","code":"# Compute leaf and distribution parameters params_iris <- forde(arf_iris, iris) # Recompute with uniform density params_unif <- forde(arf_iris, iris, family = 'unif') #> Warning in forde(arf_iris, iris, family = \"unif\"): Denisity estimation with #> uniform distribution requires finite bounds. finite_bounds has been set to #> 'local'. # Recompute with additive smoothing params_alpha <- forde(arf_iris, iris, alpha = 0.1)  # Compare results head(params_iris$cat) #> Key: <f_idx, variable> #>    f_idx variable       val  prob NA_share #>    <int>   <char>    <char> <num>    <num> #> 1:     1  Species virginica     1        0 #> 2:     2  Species    setosa     1        0 #> 3:     3  Species    setosa     1        0 #> 4:     4  Species virginica     1        0 #> 5:     5  Species    setosa     1        0 #> 6:     6  Species    setosa     1        0 head(params_alpha$cat) #> Key: <f_idx, variable> #>    f_idx variable        val       prob NA_share #>    <int>   <char>     <char>      <num>    <num> #> 1:     1  Species  virginica 0.93939394        0 #> 2:     1  Species     setosa 0.03030303        0 #> 3:     1  Species versicolor 0.03030303        0 #> 4:     2  Species  virginica 0.03030303        0 #> 5:     2  Species     setosa 0.93939394        0 #> 6:     2  Species versicolor 0.03030303        0 # Sum probabilities summary(params_iris$cat[, sum(prob), by = .(f_idx, variable)]$V1) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>       1       1       1       1       1       1 summary(params_alpha$cat[, sum(prob), by = .(f_idx, variable)]$V1) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>       1       1       1       1       1       1 params_iris #> $cnt #> Key: <f_idx, variable> #>       f_idx     variable   min   max       mu      sigma NA_share #>       <int>       <char> <num> <num>    <num>      <num>    <num> #>    1:     1 Petal.Length  -Inf   Inf 5.933333 0.20816660        0 #>    2:     1  Petal.Width  2.45   Inf 2.500000 0.01041469        0 #>    3:     1 Sepal.Length  -Inf   Inf 6.733333 0.45092498        0 #>    4:     1  Sepal.Width  -Inf   Inf 3.400000 0.17320508        0 #>    5:     2 Petal.Length  -Inf   Inf 1.466667 0.05773503        0 #>   ---                                                             #> 8468:  2117  Sepal.Width  3.30  3.45 3.400000 0.02209289        0 #> 8469:  2118 Petal.Length  1.35  1.45 1.400000 0.01803877        0 #> 8470:  2118  Petal.Width  -Inf  0.50 0.200000 0.08164966        0 #> 8471:  2118 Sepal.Length  4.65  5.30 4.875000 0.09574271        0 #> 8472:  2118  Sepal.Width  2.45  3.30 3.075000 0.15000000        0 #>  #> $cat #> Key: <f_idx, variable> #>       f_idx variable        val  prob NA_share #>       <int>   <char>     <char> <num>    <num> #>    1:     1  Species  virginica     1        0 #>    2:     2  Species     setosa     1        0 #>    3:     3  Species     setosa     1        0 #>    4:     4  Species  virginica     1        0 #>    5:     5  Species     setosa     1        0 #>   ---                                          #> 2408:  2114  Species versicolor     1        0 #> 2409:  2115  Species     setosa     1        0 #> 2410:  2116  Species     setosa     1        0 #> 2411:  2117  Species     setosa     1        0 #> 2412:  2118  Species     setosa     1        0 #>  #> $forest #> Key: <tree, leaf> #>       f_idx  tree  leaf        cvg #>       <int> <int> <int>      <num> #>    1:     1     1     6 0.02000000 #>    2:     2     1    11 0.02000000 #>    3:     3     1    15 0.02000000 #>    4:     4     1    25 0.05333333 #>    5:     5     1    31 0.01333333 #>   ---                              #> 2114:  2114   100    95 0.03333333 #> 2115:  2115   100    97 0.01333333 #> 2116:  2116   100   101 0.04000000 #> 2117:  2117   100   103 0.04000000 #> 2118:  2118   100   105 0.02666667 #>  #> $meta #>        variable   class    family decimals #>          <char>  <char>    <char>    <int> #> 1: Sepal.Length numeric truncnorm        1 #> 2:  Sepal.Width numeric truncnorm        1 #> 3: Petal.Length numeric truncnorm        1 #> 4:  Petal.Width numeric truncnorm        1 #> 5:      Species  factor  multinom       NA #>  #> $levels #>    variable        val #>      <char>     <char> #> 1:  Species     setosa #> 2:  Species versicolor #> 3:  Species  virginica #>  #> $input_class #> [1] \"data.frame\""},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"likelihood-estimation","dir":"Articles","previous_headings":"","what":"Likelihood Estimation","title":"Adversarial Random Forests","text":"calculate log-likelihoods, pass params lik function, along data whose likelihood wish evaluate. total evidence queries (.e., spanning variables conditioning events), faster also include arf function call. Note piecewise constant estimator considerably worse experiment. lik function can also used compute likelihood partial state, .e. setting variable values specified. Let’s take look iris dataset: Say want calculate sample likelihoods using continuous data. , provide values first four variables exclude fifth. case, model marginalize Species:  find likelihoods almost identical, slightly higher likelihood average partial samples. expected, since less variation model. example, used data throughout. may lead overfitting. sufficient data, preferable use training set adversarial_rf, validation set forde, test set lik. Alternatively, can set oob argument TRUE either latter two functions, case computations performed --bag (OOB) data. samples randomly excluded given tree due bootstrapping subroutine RF classifier. Note works dataset x passed forde lik one used train arf. Recall sample’s probability excluded single tree \\(\\exp(-1) \\approx 0.368\\). using oob = TRUE, sure include enough trees every observation likely OOB least times.","code":"# Compute likelihood under truncated normal and uniform distributions ll <- lik(params_iris, iris, arf = arf_iris) ll_unif <- lik(params_unif, iris, arf = arf_iris)  # Compare average negative log-likelihood (lower is better) -mean(ll) #> [1] 0.5255625 -mean(ll_unif) #> [1] -4.363783 head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa # Compute likelihoods after marginalizing over Species iris_without_species <- iris[, -5] ll_cnt <- lik(params_iris, iris_without_species)  # Compare results tmp <- data.frame(Total = ll, Partial = ll_cnt) ggplot(tmp, aes(Total, Partial)) +    geom_point() +    geom_abline(slope = 1, intercept = 0, color = 'red')"},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"data-synthesis","dir":"Articles","previous_headings":"","what":"Data Synthesis","title":"Adversarial Random Forests","text":"experiment, use smiley simulation mlbench package, allows easy visual assessment. draw training set \\(n = 1000\\) simulate \\(1000\\) synthetic datapoints. Resulting data plotted side side.  general shape clearly recognizable, even stray samples evident borders always crisp. can improved training data.","code":"# Simulate training data library(mlbench) x <- mlbench.smiley(1000) x <- data.frame(x$x, x$classes) colnames(x) <- c('X', 'Y', 'Class')  # Fit ARF arf_smiley <- adversarial_rf(x, mtry = 2) #> Iteration: 0, Accuracy: 90.68% #> Iteration: 1, Accuracy: 38.64% # Estimate parameters params_smiley <- forde(arf_smiley, x)  # Simulate data synth <- forge(params_smiley, n_synth = 1000)  # Compare structure str(x) #> 'data.frame':    1000 obs. of  3 variables: #>  $ X    : num  -0.841 -0.911 -0.91 -0.743 -0.863 ... #>  $ Y    : num  0.874 0.926 1.051 0.918 1.157 ... #>  $ Class: Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 1 1 1 1 1 1 1 1 1 1 ... str(synth) #> 'data.frame':    1000 obs. of  3 variables: #>  $ X    : num  0.779 -0.721 0.645 0.643 0.97 ... #>  $ Y    : num  -0.446 0.937 -0.529 -0.569 0.983 ... #>  $ Class: Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 4 1 4 4 2 4 1 3 3 4 ... # Put it all together x$Data <- 'Original' synth$Data <- 'Synthetic' df <- rbind(x, synth)  # Plot results ggplot(df, aes(X, Y, color = Class, shape = Class)) +    geom_point() +    facet_wrap(~ Data)"},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"conditioning","dir":"Articles","previous_headings":"","what":"Conditioning","title":"Adversarial Random Forests","text":"ARFs can also used compute likelihoods synthesize data conditioning events specify values ranges input features. instance, say want evaluate likelihood samples iris dataset condition Species = 'setosa'. several ways encode evidence, simplest pass partial observation.  expected, measurements non-setosa samples appear much less likely conditioning event. partial observation method passing evidence requires users specify unique feature values conditioning variable. flexible alternative construct data frame conditioning events, potentially including inequalities. example, may want calculate likelihood samples given Species = 'setosa' Petal.Width > 0.3. can also define intervals: Resulting likelihoods computed condition queries drawn setosa flowers petal width interval \\((0.3, 0.5)\\). final method passing evidence directly compute posterior distribution leaves. useful particularly complex conditioning events currently inbuilt interface, polynomial constraints arbitrary propositions disjunctive normal form. case, just require data frame columns f_idx wt. latter sum unity, distribution normalized warning. methods can used conditional sampling well.  conditioning Class = 4, restrict sampling smile , rather eyes nose. Computing conditional expectations similarly straightforward. average \\(X, Y\\) coordinates smile .","code":"# Compute conditional likelihoods evi <- data.frame(Species = 'setosa') ll_conditional <- lik(params_iris, query = iris_without_species, evidence = evi)  # Compare NLL across species (shifting to positive range for visualization) tmp <- iris tmp$NLL <- -ll_conditional + max(ll_conditional) + 1 ggplot(tmp, aes(Species, NLL, fill = Species)) +    geom_boxplot() +    scale_y_log10() +    ylab('Negative Log-Likelihood') +    theme(legend.position = 'none') #> Warning: Removed 6 rows containing non-finite outside the scale range #> (`stat_boxplot()`). # Data frame of conditioning events evi <- data.frame(Species = \"setosa\",                    Petal.Width = \">0.3\") evi #>   Species Petal.Width #> 1  setosa        >0.3 evi <- data.frame(Species = \"setosa\",                    Petal.Width = \"(0.3,0.5)\") evi #>   Species Petal.Width #> 1  setosa   (0.3,0.5) # Drawing random weights evi <- data.frame(f_idx = params_iris$forest$f_idx,                   wt = rexp(nrow(params_iris$forest))) evi$wt <- evi$wt / sum(evi$wt) head(evi) #>   f_idx           wt #> 1     1 6.787756e-05 #> 2     2 2.290688e-04 #> 3     3 7.573631e-05 #> 4     4 2.252819e-05 #> 5     5 5.651368e-04 #> 6     6 5.034869e-04 # Simulate class-conditional data for smiley example evi <- data.frame(Class = 4) synth2 <- forge(params_smiley, n_synth = 250, evidence = evi)  # Put it all together synth2$Data <- 'Synthetic' df <- rbind(x, synth2)  # Plot results ggplot(df, aes(X, Y, color = Class, shape = Class)) +    geom_point() +    facet_wrap(~ Data) expct(params_smiley, evidence = evi) #>             X          Y #> 1 -0.01207312 -0.6730464"},{"path":"https://bips-hb.github.io/arf/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Marvin N. Wright. Author, maintainer. David S. Watson. Author. Kristin Blesch. Author. Jan Kapar. Author.","code":""},{"path":"https://bips-hb.github.io/arf/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wright M, Watson D, Blesch K, Kapar J (2024). arf: Adversarial Random Forests. R package version 0.2.2,  https://bips-hb.github.io/arf/, https://github.com/bips-hb/arf.","code":"@Manual{,   title = {arf: Adversarial Random Forests},   author = {Marvin N. Wright and David S. Watson and Kristin Blesch and Jan Kapar},   year = {2024},   note = {R package version 0.2.2,  https://bips-hb.github.io/arf/},   url = {https://github.com/bips-hb/arf}, }"},{"path":[]},{"path":"https://bips-hb.github.io/arf/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Adversarial Random Forests","text":"Adversarial random forests (ARFs) recursively partition data fully factorized leaves, features jointly independent. procedure iterative, alternating rounds generation discrimination. Data become increasingly realistic round, original synthetic samples can longer reliably distinguished. useful several unsupervised learning tasks, density estimation data synthesis. Methods implemented package. ARFs naturally handle unstructured data mixed continuous categorical covariates. inherit many benefits RFs, including speed, flexibility, solid performance default parameters.","code":""},{"path":"https://bips-hb.github.io/arf/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Adversarial Random Forests","text":"arf package available CRAN: install development version GitHub using devtools, run:","code":"install.packages(\"arf\") devtools::install_github(\"bips-hb/arf\")"},{"path":"https://bips-hb.github.io/arf/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Adversarial Random Forests","text":"Using Fisher’s iris dataset, train ARF estimate distribution parameters:","code":"# Train the ARF arf <- adversarial_rf(iris)  # Estimate distribution parameters psi <- forde(arf, iris)"},{"path":"https://bips-hb.github.io/arf/index.html","id":"density-estimation","dir":"","previous_headings":"Examples","what":"Density estimation","title":"Adversarial Random Forests","text":"estimate log-likelihoods:","code":"mean(lik(arf, psi, iris))"},{"path":"https://bips-hb.github.io/arf/index.html","id":"generative-modeling","dir":"","previous_headings":"Examples","what":"Generative modeling","title":"Adversarial Random Forests","text":"generate 100 synthetic samples:","code":"forge(psi, 100)"},{"path":"https://bips-hb.github.io/arf/index.html","id":"conditional-expectations","dir":"","previous_headings":"Examples","what":"Conditional expectations","title":"Adversarial Random Forests","text":"estimate mean variable(s), optionally conditioned event(s): detailed examples, see package vignette.","code":"evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi)"},{"path":"https://bips-hb.github.io/arf/index.html","id":"python-library","dir":"","previous_headings":"","what":"Python library","title":"Adversarial Random Forests","text":"Python implementation ARF, arfpy, available PyPI. development version, see .","code":""},{"path":"https://bips-hb.github.io/arf/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Adversarial Random Forests","text":"Watson, D. S., Blesch, K., Kapar, J. & Wright, M. N. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics. Link .","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":null,"dir":"Reference","previous_headings":"","what":"Adversarial Random Forests — adversarial_rf","title":"Adversarial Random Forests — adversarial_rf","text":"Implements adversarial random forest learn independence-inducing splits.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adversarial Random Forests — adversarial_rf","text":"","code":"adversarial_rf(   x,   num_trees = 10L,   min_node_size = 2L,   delta = 0,   max_iters = 10L,   early_stop = TRUE,   prune = TRUE,   verbose = TRUE,   parallel = TRUE,   ... )"},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adversarial Random Forests — adversarial_rf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. num_trees Number trees grow forest. default works well generative modeling tasks, increased likelihood estimation. See Details. min_node_size Minimal number real data samples leaf nodes. delta Tolerance parameter. Algorithm converges OOB accuracy < 0.5 + delta. max_iters Maximum iterations adversarial loop. early_stop Terminate loop performance fails improve one round next? prune Impose min_node_size pruning? verbose Print discriminator accuracy round? parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples. ... Extra parameters passed ranger.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adversarial Random Forests — adversarial_rf","text":"random forest object class ranger.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adversarial Random Forests — adversarial_rf","text":"adversarial random forest (ARF) algorithm partitions data fully factorized leaves features jointly independent. ARFs trained iteratively, alternating rounds generation discrimination. first instance, synthetic data generated via independent bootstraps feature, RF classifier trained distinguish real fake samples. subsequent rounds, synthetic data generated separately leaf, using splits previous forest. creates increasingly realistic data satisfies local independence construction. algorithm converges RF reliably distinguish two classes, .e. OOB accuracy falls 0.5 + delta. ARFs useful several unsupervised learning tasks, density estimation (see forde) data synthesis (see forge). former, recommend increasing number trees improved performance (typically order 100-1000 depending sample size). Integer variables recoded warning. Default behavior convert six unique values numeric, five unique values treated ordered factors. override behavior, explicitly recode integer variables target type prior training. Note: convergence guaranteed finite samples. max_iters argument sets upper bound number training rounds. Similar results may attained increasing delta. Even single round can often give good performance, data strong complex dependencies may require iterations. default early_stop = TRUE, adversarial loop terminates performance improve one round next, case training may pointless.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Adversarial Random Forests — adversarial_rf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adversarial Random Forests — adversarial_rf","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 86.2% #> Iteration: 1, Accuracy: 40.2% #> Warning: executing %dopar% sequentially: no parallel backend registered psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.6882583  # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1      5.01579  if (FALSE) { # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) }"},{"path":"https://bips-hb.github.io/arf/reference/arf-package.html","id":null,"dir":"Reference","previous_headings":"","what":"arf: Adversarial Random Forests — arf-package","title":"arf: Adversarial Random Forests — arf-package","text":"Adversarial random forests (ARFs) recursively partition data fully factorized leaves, features jointly independent. procedure iterative, alternating rounds generation discrimination. Data becomes increasingly realistic round, original synthetic samples can longer reliably distinguished. useful several unsupervised learning tasks, density estimation data synthesis. Methods implemented package. ARFs naturally handle unstructured data mixed continuous categorical covariates. inherit many benefits random forests, including speed, flexibility, solid performance default parameters. details, see Watson et al. (2022) arXiv:2205.09435.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/arf-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"arf: Adversarial Random Forests — arf-package","text":"Maintainer: Marvin N. Wright cran@wrig.de (ORCID) Authors: David S. Watson david.s.watson11@gmail.com (ORCID) Kristin Blesch (ORCID) Jan Kapar (ORCID)","code":""},{"path":"https://bips-hb.github.io/arf/reference/arf-package.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"arf: Adversarial Random Forests — arf-package","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 84.75% #> Iteration: 1, Accuracy: 41.75% psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.7161056  # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1     5.012414  if (FALSE) { # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) }"},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute conditional circuit parameters — cforde","title":"Compute conditional circuit parameters — cforde","text":"Compute conditional circuit parameters","code":""},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute conditional circuit parameters — cforde","text":"","code":"cforde(   params,   evidence,   row_mode = c(\"separate\", \"or\"),   nomatch = c(\"force_warning\", \"force\", \"na_warning\", \"na\"),   stepsize = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute conditional circuit parameters — cforde","text":"params Circuit parameters learned via forde. evidence Data frame conditioning event(s). row_mode Interpretation rows multi-row conditions. nomatch leaf matches condition evidence? Options force sampling random leaf, either warning (\"force_warning\") without warning (\"force\"), return NA, also warning (\"na_warning\") without warning (\"na\"). default \"force_warning\". stepsize Stepsize defining number condition rows handled one step. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute conditional circuit parameters — cforde","text":"List conditions (evidence_input), prepared conditions (evidence_prepped) leaves match conditions evidence continuous data (cnt) categorical data (cat) well leaf info (forest).","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive column renaming — col_rename","title":"Adaptive column renaming — col_rename","text":"function renames columns case input colnames includes colnames required internal functions (e.g., \"y\").","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive column renaming — col_rename","text":"","code":"col_rename(cn, old_name)"},{"path":"https://bips-hb.github.io/arf/reference/col_rename.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive column renaming — col_rename","text":"cn Column names. old_name Name column renamed.","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Rename all problematic columns with col_rename(). — col_rename_all","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"Rename problematic columns col_rename().","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"","code":"col_rename_all(cn)"},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"cn Old column names.","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"New columns names.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":null,"dir":"Reference","previous_headings":"","what":"Shortcut likelihood function — darf","title":"Shortcut likelihood function — darf","text":"Calls adversarial_rf, forde lik. repeated application, faster save outputs adversarial_rf forde pass via ... directly use lik.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shortcut likelihood function — darf","text":"","code":"darf(x, query = NULL, ...)"},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shortcut likelihood function — darf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. query Data frame samples, optionally comprising just subset training features. See Details lik. set x zero. ... Extra parameters passed adversarial_rf, forde lik.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shortcut likelihood function — darf","text":"vector likelihoods, optionally log scale. dataset n_synth synthetic samples nrow(x) synthetic samples n_synth undefined.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shortcut likelihood function — darf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shortcut likelihood function — darf","text":"","code":"# Estimate log-likelihoods ll <- darf(iris)  # Partial evidence query ll <- darf(iris, query = iris[1, 1:3])  # Condition on Species = \"setosa\" ll <- darf(iris, query = iris[1, 1:3], evidence = data.frame(Species = \"setosa\"))"},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":null,"dir":"Reference","previous_headings":"","what":"Shortcut expectation function — earf","title":"Shortcut expectation function — earf","text":"Calls adversarial_rf, forde expct. repeated application, faster save outputs adversarial_rf forde pass via ... directly use expct.","code":""},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shortcut expectation function — earf","text":"","code":"earf(x, ...)"},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shortcut expectation function — earf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. ... Extra parameters passed adversarial_rf, forde expct.","code":""},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shortcut expectation function — earf","text":"one row data frame values query variables.","code":""},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shortcut expectation function — earf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shortcut expectation function — earf","text":"","code":"# What is the expected values of each feature? earf(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species #> 1     5.843333    3.057333        3.758    1.199333 virginica  #' # What is the expected values of Sepal.Length? earf(iris, query = \"Sepal.Length\") #>   Sepal.Length #> 1     5.843333  # What if we condition on Species = \"setosa\"? earf(iris, query = \"Sepal.Length\", evidence = data.frame(Species = \"setosa\")) #>   Sepal.Length #> 1     5.021825"},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Value — expct","title":"Expected Value — expct","text":"Compute expectation query variable(s), optionally conditioned event(s).","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Value — expct","text":"","code":"expct(   params,   query = NULL,   evidence = NULL,   evidence_row_mode = c(\"separate\", \"or\"),   round = FALSE,   nomatch = c(\"force_warning\", \"force\", \"na_warning\", \"na\"),   stepsize = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Value — expct","text":"params Circuit parameters learned via forde. query Optional character vector variable names. Estimates computed . NULL, variables evidence estimated. evidence contains NAs, variables estimated full dataset returned. evidence Optional set conditioning events. can take one three forms: (1) partial sample, .e. single row data columns; (2) data frame conditioning events, allows inequalities intervals; (3) posterior distribution leaves; see Details Examples. evidence_row_mode Interpretation rows multi-row evidence. 'separate', row evidence separate conditioning event n_synth synthetic samples generated. '', rows combined logical ; see Examples. round Round continuous variables respective maximum precision real data set? nomatch leaf matches condition evidence? Options force sampling random leaf, either warning (\"force_warning\") without warning (\"force\"), return NA, also warning (\"na_warning\") without warning (\"na\"). default \"force_warning\". stepsize Stepsize defining number evidence rows handled one step. Defaults nrow(evidence)/num_registered_workers parallel == TRUE. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expected Value — expct","text":"one row data frame values query variables.","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expected Value — expct","text":"function computes expected values subset features, optionally conditioned event(s). three methods (optionally) encoding conditioning events via evidence argument. first provide partial sample, columns training data missing set NA. second provide data frame condition events. supports inequalities intervals. Alternatively, users may directly input pre-calculated posterior distribution leaves, columns f_idx wt. may preferable complex constraints. See Examples. Please note results continuous features included query evidence interval condition currently inconsistent.","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Expected Value — expct","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expected Value — expct","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 90.44% #> Iteration: 1, Accuracy: 43.43% psi <- forde(arf, iris)  # What is the expected value of Sepal.Length? expct(psi, query = \"Sepal.Length\") #>   Sepal.Length #> 1     5.843333  # What if we condition on Species = \"setosa\"? evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1     5.016955  # Compute expectations for all features other than Species expct(psi, evidence = evi) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1     5.016955    3.420397     1.504233   0.2623856  # Condition on first two data rows with some missing values evi <- iris[1:2,] evi[1, 1] <- NA_real_ evi[1, 5] <- NA_character_ evi[2, 2] <- NA_real_ x_synth <- expct(psi, evidence = evi)  if (FALSE) { # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) }"},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":null,"dir":"Reference","previous_headings":"","what":"Forests for Density Estimation — forde","title":"Forests for Density Estimation — forde","text":"Uses pre-trained ARF model estimate leaf distribution parameters.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forests for Density Estimation — forde","text":"","code":"forde(   arf,   x,   oob = FALSE,   family = \"truncnorm\",   finite_bounds = c(\"no\", \"local\", \"global\"),   alpha = 0,   epsilon = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forests for Density Estimation — forde","text":"arf Pre-trained adversarial_rf. Alternatively, object class ranger. x Training data estimating parameters. oob use --bag samples parameter estimation? TRUE, x must dataset used train arf. family Distribution use density estimation continuous features. Current options include truncated normal (default family = \"truncnorm\") uniform (family = \"unif\"). See Details. finite_bounds Impose finite bounds continuous variables? 'local', infinite bounds shrinked empirical extrema within leaves. 'global', infinite bounds shrinked global empirical extrema. '' (default), impose finite bounds. alpha Optional pseudocount Laplace smoothing categorical features. avoids zero-mass points test data fall outside support training data. Effectively parametrizes flat Dirichlet prior multinomial likelihoods. epsilon Optional slack parameter empirical bounds finite_bounds != ''. avoids zero-density points test data fall outside support training data. gap lower upper bounds expanded factor 1 + epsilon. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forests for Density Estimation — forde","text":"list 5 elements: (1) parameters continuous data; (2) parameters discrete data; (3) leaf indices coverage; (4) metadata variables; (5) data input class. list used estimating likelihoods lik generating data forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forests for Density Estimation — forde","text":"forde extracts leaf parameters pretrained forest learns distribution parameters data within leaf. former includes coverage (proportion data falling leaf) split criteria. latter includes proportions categorical features mean/variance continuous features. result probabilistic circuit, stored data.table, can used various downstream inference tasks. Currently, forde provides support limited number distributional families: truncated normal uniform continuous data, multinomial discrete data. Future releases accommodate larger set options. Though forde designed take adversarial random forest input, function's first argument can principle object class ranger. allows users test performance alternative pipelines (e.g., supervised forest input). also requirement x data used fit arf, unless oob = TRUE. fact, using another dataset may protect overfitting. connects Wager & Athey's (2018) notion \"honest trees\".","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forests for Density Estimation — forde","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375. Wager, S. & Athey, S. (2018). Estimation inference heterogeneous treatment effects using random forests. J. . Stat. Assoc., 113(523): 1228-1242.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forests for Density Estimation — forde","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 86.2% #> Iteration: 1, Accuracy: 42.37% psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.7392139  # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1     5.011261  if (FALSE) { # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) }"},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":null,"dir":"Reference","previous_headings":"","what":"Forests for Generative Modeling — forge","title":"Forests for Generative Modeling — forge","text":"Uses pre-trained FORDE model simulate synthetic data.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forests for Generative Modeling — forge","text":"","code":"forge(   params,   n_synth,   evidence = NULL,   evidence_row_mode = c(\"separate\", \"or\"),   round = TRUE,   sample_NAs = FALSE,   nomatch = c(\"force_warning\", \"force\", \"na_warning\", \"na\"),   stepsize = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forests for Generative Modeling — forge","text":"params Circuit parameters learned via forde. n_synth Number synthetic samples generate. evidence Optional set conditioning events. can take one three forms: (1) partial sample, .e. single row data columns; (2) data frame conditioning events, allows inequalities intervals; (3) posterior distribution leaves; see Details Examples. evidence_row_mode Interpretation rows multi-row evidence. 'separate', row evidence separate conditioning event n_synth synthetic samples generated. '', rows combined logical ; see Examples. round Round continuous variables respective maximum precision real data set? sample_NAs Sample NAs respecting probability missing values original data. nomatch leaf matches condition evidence? Options force sampling random leaf, either warning (\"force_warning\") without warning (\"force\"), return NA, also warning (\"na_warning\") without warning (\"na\"). default \"force_warning\". stepsize Stepsize defining number evidence rows handled one step. Defaults nrow(evidence)/num_registered_workers parallel == TRUE. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forests for Generative Modeling — forge","text":"dataset n_synth synthetic samples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forests for Generative Modeling — forge","text":"forge simulates synthetic dataset n_synth samples. First, leaves sampled proportion either coverage (evidence = NULL) posterior probability. , feature sampled independently within leaf according probability mass density function learned forde. create realistic data long adversarial RF used previous step satisfies local independence criterion. See Watson et al. (2023). three methods (optionally) encoding conditioning events via evidence argument. first provide partial sample, columns training data missing set NA. second provide data frame condition events. supports inequalities intervals. Alternatively, users may directly input pre-calculated posterior distribution leaves, columns f_idx wt. may preferable complex constraints. See Examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forests for Generative Modeling — forge","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forests for Generative Modeling — forge","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 82.94% #> Iteration: 1, Accuracy: 41.41% psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" evi <- data.frame(Species = \"setosa\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Alternative syntax for <\/> conditions evi <- data.frame(Sepal.Length = \">6\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Negation condition, i.e. all classes except \"setosa\" evi <- data.frame(Species = \"!setosa\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Condition on first two data rows with some missing values evi <- iris[1:2,] evi[1, 1] <- NA_real_ evi[1, 5] <- NA_character_ evi[2, 2] <- NA_real_ x_synth <- forge(psi, n_synth = 1, evidence = evi)  # Or just input some distribution on leaves # (Weights that do not sum to unity are automatically scaled) n_leaves <- nrow(psi$forest) evi <- data.frame(f_idx = psi$forest$f_idx, wt = rexp(n_leaves)) x_synth <- forge(psi, n_synth = 100, evidence = evi)  if (FALSE) { # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) }"},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":null,"dir":"Reference","previous_headings":"","what":"Likelihood Estimation — lik","title":"Likelihood Estimation — lik","text":"Compute likelihood input data, optionally conditioned event(s).","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Likelihood Estimation — lik","text":"","code":"lik(   params,   query,   evidence = NULL,   arf = NULL,   oob = FALSE,   log = TRUE,   batch = NULL,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Likelihood Estimation — lik","text":"params Circuit parameters learned via forde. query Data frame samples, optionally comprising just subset training features. Likelihoods computed sample. Missing features marginalized . See Details. evidence Optional set conditioning events. can take one three forms: (1) partial sample, .e. single row data columns; (2) data frame conditioning events, allows inequalities; (3) posterior distribution leaves. See Details. arf Pre-trained adversarial_rf object class ranger. required speeds computation considerably total evidence queries. (Ignored partial evidence queries.) oob use --bag leaves likelihood estimation? TRUE, x must dataset used train arf. applicable total evidence queries. log Return likelihoods log scale? Recommended prevent underflow. batch Batch size. default compute densities queries one round, always fastest option memory allows. However, large samples many trees, can memory efficient split data batches. impact results. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Likelihood Estimation — lik","text":"vector likelihoods, optionally log scale.","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Likelihood Estimation — lik","text":"function computes likelihood input data, optionally conditioned event(s). Queries may partial, .e. covering features, case excluded variables marginalized . three methods (optionally) encoding conditioning events via evidence argument. first provide partial sample, columns training data present. second provide data frame three columns: variable, relation, value. supports inequalities via relation. Alternatively, users may directly input pre-calculated posterior distribution leaves, columns f_idx wt. may preferable complex constraints. See Examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Likelihood Estimation — lik","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Likelihood Estimation — lik","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 82.65% #> Iteration: 1, Accuracy: 38.67% psi <- forde(arf, iris)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.6148863  # Identical but slower ll <- lik(psi, iris, log = TRUE) #> Warning: For total evidence queries, it is faster to include the pre-trained arf. mean(ll) #> [1] -0.6148863  # Partial evidence query lik(psi, query = iris[1, 1:3]) #> [1] 0.5710183  # Condition on Species = \"setosa\" evi <- data.frame(Species = \"setosa\") lik(psi, query = iris[1, 1:3], evidence = evi) #> [1] 1.668789  # Condition on Species = \"setosa\" and Petal.Width > 0.3 evi <- data.frame(Species = \"setosa\",                    Petal.Width = \">0.3\") lik(psi, query = iris[1, 1:3], evidence = evi) #> [1] 1.323916  if (FALSE) { # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) }"},{"path":"https://bips-hb.github.io/arf/reference/post_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Post-process data — post_x","title":"Post-process data — post_x","text":"function prepares output data forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/post_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Post-process data — post_x","text":"","code":"post_x(x, params, round = TRUE)"},{"path":"https://bips-hb.github.io/arf/reference/post_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Post-process data — post_x","text":"x Input data.frame. params Circuit parameters learned via forde. round Round continuous variables respective maximum precision real data set?","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess conditions — prep_cond","title":"Preprocess conditions — prep_cond","text":"function prepares conditions computing conditional circuit paramaters via cforde","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess conditions — prep_cond","text":"","code":"prep_cond(evidence, params, row_mode)"},{"path":"https://bips-hb.github.io/arf/reference/prep_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess conditions — prep_cond","text":"evidence Optional set conditioning events. params Circuit parameters learned via forde. row_mode Interpretation rows multi-row conditions.","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess input data — prep_x","title":"Preprocess input data — prep_x","text":"function prepares input data ARFs.","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess input data — prep_x","text":"","code":"prep_x(x)"},{"path":"https://bips-hb.github.io/arf/reference/prep_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess input data — prep_x","text":"x Input data.frame.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":null,"dir":"Reference","previous_headings":"","what":"Shortcut sampling function — rarf","title":"Shortcut sampling function — rarf","text":"Calls adversarial_rf, forde forge. repeated application, faster save outputs adversarial_rf forde pass via ... directly use forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shortcut sampling function — rarf","text":"","code":"rarf(x, n_synth = NULL, ...)"},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shortcut sampling function — rarf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. n_synth Number synthetic samples generate. set nrow(x) NULL. ... Extra parameters passed adversarial_rf, forde forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shortcut sampling function — rarf","text":"dataset n_synth synthetic samples nrow(x) synthetic samples n_synth undefined.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shortcut sampling function — rarf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shortcut sampling function — rarf","text":"","code":"# Generate 150 (size of original iris dataset) synthetic samples from the iris dataset x_synth <- rarf(iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- rarf(iris, n_synth = 100)  # Condition on Species = \"setosa\" x_synth <- rarf(iris, evidence = data.frame(Species = \"setosa\"))"},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Safer version of sample() — resample","title":"Safer version of sample() — resample","text":"Safer version sample()","code":""},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Safer version of sample() — resample","text":"","code":"resample(x, ...)"},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Safer version of sample() — resample","text":"x vector one elements choose. ... arguments sample().","code":""},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Safer version of sample() — resample","text":"vector length size elements drawn x.","code":""},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":null,"dir":"Reference","previous_headings":"","what":"which.max() with random at ties — which.max.random","title":"which.max() with random at ties — which.max.random","text":".max() random ties","code":""},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"which.max() with random at ties — which.max.random","text":"","code":"which.max.random(x)"},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"which.max() with random at ties — which.max.random","text":"x numeric vector.","code":""},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"which.max() with random at ties — which.max.random","text":"Index maximum value x, random tie-breaking.","code":""},{"path":"https://bips-hb.github.io/arf/news/index.html","id":"arf-022","dir":"Changelog","previous_headings":"","what":"arf 0.2.2","title":"arf 0.2.2","text":"Faster vectorized conditional sampling Use min.bucket argument ranger avoid pruning possible Option sample NAs generated data original data contains NAs Stepsize forge() reduce memory usage Option local global finite bounds","code":""},{"path":"https://bips-hb.github.io/arf/news/index.html","id":"arf-020","dir":"Changelog","previous_headings":"","what":"arf 0.2.0","title":"arf 0.2.0","text":"CRAN release: 2024-01-24 Vectorized adversarial resampling Speed boost compiling probabilistic circuit Conditional densities sampling Bayesian solution invariant continuous data within leaf nodes New function computing (conditional) expectations Options missing data","code":""},{"path":"https://bips-hb.github.io/arf/news/index.html","id":"arf-013","dir":"Changelog","previous_headings":"","what":"arf 0.1.3","title":"arf 0.1.3","text":"CRAN release: 2023-02-06 Speed boost adversarial resampling step Early stopping option adversarial training alpha parameter regularizing multinomial distributions forde Unified treatment colnames internal semantics (y, obs, tree, leaf)","code":""}]
+[{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"adversarial-training","dir":"Articles","previous_headings":"","what":"Adversarial Training","title":"Adversarial Random Forests","text":"ARF algorithm iterative procedure. first instance, generate synthetic data independently sampling marginals feature training random forest (RF) distinguish original synthetic samples. accuracy greater 0.5+δ0.5 + \\delta (delta user-controlled tolerance parameter, generally set 0), create new dataset sampling marginals within leaf training another RF classifier. procedure repeats original synthetic samples reliably distinguished. default verbose = TRUE, algorithm print accuracy iteration. printouts can turned setting verbose = FALSE. Accuracy still stored within arf object, can evaluate convergence fact. warning appears just per session. can suppressed setting parallel = FALSE registering parallel backend ().  find quick drop accuracy following resampling procedure, desired. ARF converged, resulting splits form fully factorized leaves, .e. subregions feature space variables locally independent. ARF convergence asymptotically guaranteed n→∞n \\rightarrow \\infty (see Watson et al., 2023, Thm. 1). However, implications finite sample performance. practice, often find adversarial training completes just one two rounds, may hold datasets. avoid infinite loops, users can increase slack parameter delta set max_iters argument (default = 10). addition failsafes, adversarial_rf uses early stopping default (early_stop = TRUE), terminates training factorization improve one round next. recommended, since discriminator accuracy rarely falls much lower increased. density estimation tasks, recommend increasing default number trees. generally use 100 experiments, though may suboptimal datasets. Likelihood estimates sensitive parameter certain threshold, larger models incur extra costs time memory. can speed computations registering parallel backend, case ARF training distributed across cores using ranger package. Much like ranger, default behavior adversarial_rf compute parallel possible. exactly done varies across operating systems. following code works Unix machines. Windows requires different setup. either case, can now execute parallel. result object class ranger, can input downstream functions.","code":"# Load libraries library(arf) library(data.table) library(ggplot2)  # Set seed set.seed(123, \"L'Ecuyer-CMRG\")  # Train ARF arf_iris <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 76.09% #> Iteration: 1, Accuracy: 39.67% #> Warning: executing %dopar% sequentially: no parallel backend registered # Train ARF with no printouts arf_iris <- adversarial_rf(iris, verbose = FALSE)  # Plot accuracy against iterations (model converges when accuracy <= 0.5) tmp <- data.frame('Accuracy' = arf_iris$acc,                    'Iteration' = seq_len(length(arf_iris$acc))) ggplot(tmp, aes(Iteration, Accuracy)) +    geom_point() +    geom_path() +   geom_hline(yintercept = 0.5, linetype = 'dashed', color = 'red') # Register cores - Unix library(doParallel) registerDoParallel(cores = 2) # Register cores - Windows library(doParallel) cl <- makeCluster(2) registerDoParallel(cl) # Rerun ARF, now in parallel and with more trees arf_iris <- adversarial_rf(iris, num_trees = 100) #> Iteration: 0, Accuracy: 85.67% #> Iteration: 1, Accuracy: 39.67%"},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"parameter-learning","dir":"Articles","previous_headings":"","what":"Parameter Learning","title":"Adversarial Random Forests","text":"next step learn leaf distribution parameters using forests density estimation (FORDE). function calculates coverage, bounds, pdf/pmf parameters every variable every leaf. can expensive computation large datasets, requires 𝒪(B⋅d⋅n⋅log(n))\\mathcal{O}\\big(B \\cdot d \\cdot n \\cdot \\log(n)\\big) operations, BB number trees, dd data dimensionality, nn sample size. , process parallelized default. Default behavior use truncated normal distribution continuous data (boundaries given tree’s split parameters) multinomial distribution categorical data. find produces stable results wide range settings. can also use uniform distribution continuous features setting family = 'unif', thereby instantiating piecewise constant density estimator. method tends perform poorly practice, recommend . option implemented primarily benchmarking purposes. Alternative families, e.g. truncated Poisson beta distributions, may useful certain problems. Future releases expand range options family argument. alpha epsilon arguments allow optional regularization multinomial uniform distributions, respectively. help prevent zero likelihood samples test data fall outside support training data. former pseudocount parameter applies Laplace smoothing within leaves, preventing unobserved values assigned zero probability unless splits explicitly rule . words, impose flat Dirichlet prior report posterior probabilities rather maximum likelihood estimates. latter slack parameter empirical bounds expands estimated extrema continuous features factor 1+ϵ1 + \\epsilon. Compare results original probability estimates Species variable obtained adding pseudocount α=0.1\\alpha = 0.1. Laplace smoothing, extreme probabilities occur splits explicitly demand . Otherwise, values shrink toward uniform prior. Note two data tables may exactly rows, omit zero probability events conserve memory. However, can verify probabilities sum unity leaf-variable combination. forde function outputs list length 6, entries (1) continuous features; (2) categorical features; (3) leaf parameters; (4) variable metadata; (5) factor levels; (6) data input class. parameters can used variety downstream tasks, likelihood estimation data synthesis.","code":"# Compute leaf and distribution parameters params_iris <- forde(arf_iris, iris) # Recompute with uniform density params_unif <- forde(arf_iris, iris, family = 'unif') #> Warning in forde(arf_iris, iris, family = \"unif\"): Denisity estimation with #> uniform distribution requires finite bounds. finite_bounds has been set to #> 'local'. # Recompute with additive smoothing params_alpha <- forde(arf_iris, iris, alpha = 0.1)  # Compare results head(params_iris$cat) #> Key: <f_idx, variable> #>    f_idx variable       val  prob NA_share #>    <int>   <char>    <char> <num>    <num> #> 1:     1  Species virginica     1        0 #> 2:     2  Species virginica     1        0 #> 3:     3  Species virginica     1        0 #> 4:     4  Species virginica     1        0 #> 5:     5  Species    setosa     1        0 #> 6:     6  Species    setosa     1        0 head(params_alpha$cat) #> Key: <f_idx, variable> #>    f_idx variable        val       prob NA_share #>    <int>   <char>     <char>      <num>    <num> #> 1:     1  Species  virginica 0.93939394        0 #> 2:     1  Species     setosa 0.03030303        0 #> 3:     1  Species versicolor 0.03030303        0 #> 4:     2  Species  virginica 0.91304348        0 #> 5:     2  Species     setosa 0.04347826        0 #> 6:     2  Species versicolor 0.04347826        0 # Sum probabilities summary(params_iris$cat[, sum(prob), by = .(f_idx, variable)]$V1) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>       1       1       1       1       1       1 summary(params_alpha$cat[, sum(prob), by = .(f_idx, variable)]$V1) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>       1       1       1       1       1       1 params_iris #> $cnt #> Key: <f_idx, variable> #>        f_idx     variable   min   max       mu      sigma NA_share #>        <int>       <char> <num> <num>    <num>      <num>    <num> #>     1:     1 Petal.Length  -Inf   Inf 5.933333 0.20816660        0 #>     2:     1  Petal.Width  2.45   Inf 2.500000 0.01041469        0 #>     3:     1 Sepal.Length  -Inf   Inf 6.733333 0.45092498        0 #>     4:     1  Sepal.Width  -Inf   Inf 3.400000 0.17320508        0 #>     5:     2 Petal.Length  -Inf   Inf 5.250000 0.21213203        0 #>    ---                                                             #> 10484:  2621  Sepal.Width  3.30  3.45 3.400000 0.02209289        0 #> 10485:  2622 Petal.Length  -Inf  4.20 1.400000 0.10000000        0 #> 10486:  2622  Petal.Width  -Inf  0.25 0.200000 0.03124407        0 #> 10487:  2622 Sepal.Length  4.65  5.55 5.266667 0.20816660        0 #> 10488:  2622  Sepal.Width  3.45  3.55 3.500000 0.02082938        0 #>  #> $cat #> Key: <f_idx, variable> #>       f_idx variable        val      prob NA_share #>       <int>   <char>     <char>     <num>    <num> #>    1:     1  Species  virginica 1.0000000        0 #>    2:     2  Species  virginica 1.0000000        0 #>    3:     3  Species  virginica 1.0000000        0 #>    4:     4  Species  virginica 1.0000000        0 #>    5:     5  Species     setosa 1.0000000        0 #>   ---                                              #> 2919:  2618  Species versicolor 0.4615385        0 #> 2920:  2619  Species     setosa 1.0000000        0 #> 2921:  2620  Species     setosa 1.0000000        0 #> 2922:  2621  Species     setosa 1.0000000        0 #> 2923:  2622  Species     setosa 1.0000000        0 #>  #> $forest #> Key: <tree, leaf> #>       f_idx  tree  leaf        cvg #>       <int> <int> <int>      <num> #>    1:     1     1     6 0.02000000 #>    2:     2     1    10 0.01333333 #>    3:     3     1    13 0.04000000 #>    4:     4     1    16 0.03333333 #>    5:     5     1    21 0.02000000 #>   ---                              #> 2618:  2618   100    49 0.08666667 #> 2619:  2619   100    57 0.02000000 #> 2620:  2620   100    58 0.04666667 #> 2621:  2621   100    60 0.04000000 #> 2622:  2622   100    61 0.02000000 #>  #> $meta #>        variable   class    family decimals #>          <char>  <char>    <char>    <int> #> 1: Sepal.Length numeric truncnorm        1 #> 2:  Sepal.Width numeric truncnorm        1 #> 3: Petal.Length numeric truncnorm        1 #> 4:  Petal.Width numeric truncnorm        1 #> 5:      Species  factor  multinom       NA #>  #> $levels #>    variable        val #>      <char>     <char> #> 1:  Species     setosa #> 2:  Species versicolor #> 3:  Species  virginica #>  #> $input_class #> [1] \"data.frame\""},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"likelihood-estimation","dir":"Articles","previous_headings":"","what":"Likelihood Estimation","title":"Adversarial Random Forests","text":"calculate log-likelihoods, pass params lik function, along data whose likelihood wish evaluate. total evidence queries (.e., spanning variables conditioning events), faster also include arf function call. Note piecewise constant estimator considerably worse experiment. lik function can also used compute likelihood partial state, .e. setting variable values specified. Let’s take look iris dataset: Say want calculate sample likelihoods using continuous data. , provide values first four variables exclude fifth. case, model marginalize Species:  find likelihoods almost identical, slightly higher likelihood average partial samples. expected, since less variation model. example, used data throughout. may lead overfitting. sufficient data, preferable use training set adversarial_rf, validation set forde, test set lik. Alternatively, can set oob argument TRUE either latter two functions, case computations performed --bag (OOB) data. samples randomly excluded given tree due bootstrapping subroutine RF classifier. Note works dataset x passed forde lik one used train arf. Recall sample’s probability excluded single tree exp(−1)≈0.368\\exp(-1) \\approx 0.368. using oob = TRUE, sure include enough trees every observation likely OOB least times.","code":"# Compute likelihood under truncated normal and uniform distributions ll <- lik(params_iris, iris, arf = arf_iris) ll_unif <- lik(params_unif, iris, arf = arf_iris)  # Compare average negative log-likelihood (lower is better) -mean(ll) #> [1] 0.3196718 -mean(ll_unif) #> [1] -7.291452 head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa # Compute likelihoods after marginalizing over Species iris_without_species <- iris[, -5] ll_cnt <- lik(params_iris, iris_without_species)  # Compare results tmp <- data.frame(Total = ll, Partial = ll_cnt) ggplot(tmp, aes(Total, Partial)) +    geom_point() +    geom_abline(slope = 1, intercept = 0, color = 'red')"},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"data-synthesis","dir":"Articles","previous_headings":"","what":"Data Synthesis","title":"Adversarial Random Forests","text":"experiment, use smiley simulation mlbench package, allows easy visual assessment. draw training set n=1000n = 1000 simulate 10001000 synthetic datapoints. Resulting data plotted side side.  general shape clearly recognizable, even stray samples evident borders always crisp. can improved training data.","code":"# Simulate training data library(mlbench) x <- mlbench.smiley(1000) x <- data.frame(x$x, x$classes) colnames(x) <- c('X', 'Y', 'Class')  # Fit ARF arf_smiley <- adversarial_rf(x, mtry = 2) #> Iteration: 0, Accuracy: 84.35% #> Iteration: 1, Accuracy: 42.29%  # Estimate parameters params_smiley <- forde(arf_smiley, x)  # Simulate data synth <- forge(params_smiley, n_synth = 1000)  # Compare structure str(x) #> 'data.frame':    1000 obs. of  3 variables: #>  $ X    : num  -0.841 -0.911 -0.91 -0.743 -0.863 ... #>  $ Y    : num  0.874 0.926 1.051 0.918 1.157 ... #>  $ Class: Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 1 1 1 1 1 1 1 1 1 1 ... str(synth) #> 'data.frame':    1000 obs. of  3 variables: #>  $ X    : num  -0.3742 -0.0897 0.0322 0.0271 -0.8529 ... #>  $ Y    : num  -0.786 0.371 0.548 0.546 -0.247 ... #>  $ Class: Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 4 3 3 3 4 4 3 2 3 3 ...  # Put it all together x$Data <- 'Original' synth$Data <- 'Synthetic' df <- rbind(x, synth)  # Plot results ggplot(df, aes(X, Y, color = Class, shape = Class)) +    geom_point() +    facet_wrap(~ Data)"},{"path":"https://bips-hb.github.io/arf/articles/arf.html","id":"conditioning","dir":"Articles","previous_headings":"","what":"Conditioning","title":"Adversarial Random Forests","text":"ARFs can also used compute likelihoods synthesize data conditioning events specify values ranges input features. instance, say want evaluate likelihood samples iris dataset condition Species = 'setosa'. several ways encode evidence, simplest pass partial observation.  expected, measurements non-setosa samples appear much less likely conditioning event. partial observation method passing evidence requires users specify unique feature values conditioning variable. flexible alternative construct data frame conditioning events, potentially including inequalities. example, may want calculate likelihood samples given Species = 'setosa' Petal.Width > 0.3. can also define intervals: Resulting likelihoods computed condition queries drawn setosa flowers petal width interval (0.3,0.5)(0.3, 0.5). final method passing evidence directly compute posterior distribution leaves. useful particularly complex conditioning events currently inbuilt interface, polynomial constraints arbitrary propositions disjunctive normal form. case, just require data frame columns f_idx wt. latter sum unity, distribution normalized warning. methods can used conditional sampling well.  conditioning Class = 4, restrict sampling smile , rather eyes nose. Computing conditional expectations similarly straightforward. average X,YX, Y coordinates smile .","code":"# Compute conditional likelihoods evi <- data.frame(Species = 'setosa') ll_conditional <- lik(params_iris, query = iris_without_species, evidence = evi)  # Compare NLL across species (shifting to positive range for visualization) tmp <- iris tmp$NLL <- -ll_conditional + max(ll_conditional) + 1 ggplot(tmp, aes(Species, NLL, fill = Species)) +    geom_boxplot() +    scale_y_log10() +    ylab('Negative Log-Likelihood') +    theme(legend.position = 'none') #> Warning: Removed 11 rows containing non-finite outside the scale range #> (`stat_boxplot()`). # Data frame of conditioning events evi <- data.frame(Species = \"setosa\",                    Petal.Width = \">0.3\") evi #>   Species Petal.Width #> 1  setosa        >0.3 evi <- data.frame(Species = \"setosa\",                    Petal.Width = \"(0.3,0.5)\") evi #>   Species Petal.Width #> 1  setosa   (0.3,0.5) # Drawing random weights evi <- data.frame(f_idx = params_iris$forest$f_idx,                   wt = rexp(nrow(params_iris$forest))) evi$wt <- evi$wt / sum(evi$wt) head(evi) #>   f_idx           wt #> 1     1 4.483113e-04 #> 2     2 3.997981e-05 #> 3     3 2.888576e-05 #> 4     4 8.726013e-04 #> 5     5 4.433602e-04 #> 6     6 3.536210e-05 # Simulate class-conditional data for smiley example evi <- data.frame(Class = 4) synth2 <- forge(params_smiley, n_synth = 250, evidence = evi)  # Put it all together synth2$Data <- 'Synthetic' df <- rbind(x, synth2)  # Plot results ggplot(df, aes(X, Y, color = Class, shape = Class)) +    geom_point() +    facet_wrap(~ Data) expct(params_smiley, evidence = evi) #>             X          Y #> 1 -0.01177414 -0.6728099"},{"path":"https://bips-hb.github.io/arf/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Marvin N. Wright. Author, maintainer. David S. Watson. Author. Kristin Blesch. Author. Jan Kapar. Author.","code":""},{"path":"https://bips-hb.github.io/arf/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wright M, Watson D, Blesch K, Kapar J (2024). arf: Adversarial Random Forests. R package version 0.2.3, https://bips-hb.github.io/arf/, https://github.com/bips-hb/arf.","code":"@Manual{,   title = {arf: Adversarial Random Forests},   author = {Marvin N. Wright and David S. Watson and Kristin Blesch and Jan Kapar},   year = {2024},   note = {R package version 0.2.3,     https://bips-hb.github.io/arf/},   url = {https://github.com/bips-hb/arf}, }"},{"path":[]},{"path":"https://bips-hb.github.io/arf/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Adversarial Random Forests","text":"Adversarial random forests (ARFs) recursively partition data fully factorized leaves, features jointly independent. procedure iterative, alternating rounds generation discrimination. Data become increasingly realistic round, original synthetic samples can longer reliably distinguished. useful several unsupervised learning tasks, density estimation data synthesis. Methods implemented package. ARFs naturally handle unstructured data mixed continuous categorical covariates. inherit many benefits RFs, including speed, flexibility, solid performance default parameters.","code":""},{"path":"https://bips-hb.github.io/arf/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Adversarial Random Forests","text":"arf package available CRAN: install development version GitHub using devtools, run:","code":"install.packages(\"arf\") devtools::install_github(\"bips-hb/arf\")"},{"path":"https://bips-hb.github.io/arf/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Adversarial Random Forests","text":"Using Fisher’s iris dataset, train ARF estimate distribution parameters:","code":"# Train the ARF arf <- adversarial_rf(iris)  # Estimate distribution parameters psi <- forde(arf, iris)"},{"path":"https://bips-hb.github.io/arf/index.html","id":"density-estimation","dir":"","previous_headings":"Examples","what":"Density estimation","title":"Adversarial Random Forests","text":"estimate log-likelihoods:","code":"mean(lik(arf, psi, iris))"},{"path":"https://bips-hb.github.io/arf/index.html","id":"generative-modeling","dir":"","previous_headings":"Examples","what":"Generative modeling","title":"Adversarial Random Forests","text":"generate 100 synthetic samples:","code":"forge(psi, 100)"},{"path":"https://bips-hb.github.io/arf/index.html","id":"conditional-expectations","dir":"","previous_headings":"Examples","what":"Conditional expectations","title":"Adversarial Random Forests","text":"estimate mean variable(s), optionally conditioned event(s): detailed examples, see package vignette.","code":"evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi)"},{"path":"https://bips-hb.github.io/arf/index.html","id":"python-library","dir":"","previous_headings":"","what":"Python library","title":"Adversarial Random Forests","text":"Python implementation ARF, arfpy, available PyPI. development version, see .","code":""},{"path":"https://bips-hb.github.io/arf/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Adversarial Random Forests","text":"Watson, D. S., Blesch, K., Kapar, J. & Wright, M. N. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics. Link .","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":null,"dir":"Reference","previous_headings":"","what":"Adversarial Random Forests — adversarial_rf","title":"Adversarial Random Forests — adversarial_rf","text":"Implements adversarial random forest learn independence-inducing splits.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adversarial Random Forests — adversarial_rf","text":"","code":"adversarial_rf(   x,   num_trees = 10L,   min_node_size = 2L,   delta = 0,   max_iters = 10L,   early_stop = TRUE,   prune = TRUE,   verbose = TRUE,   parallel = TRUE,   ... )"},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adversarial Random Forests — adversarial_rf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. num_trees Number trees grow forest. default works well generative modeling tasks, increased likelihood estimation. See Details. min_node_size Minimal number real data samples leaf nodes. delta Tolerance parameter. Algorithm converges OOB accuracy < 0.5 + delta. max_iters Maximum iterations adversarial loop. early_stop Terminate loop performance fails improve one round next? prune Impose min_node_size pruning? verbose Print discriminator accuracy round? parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples. ... Extra parameters passed ranger.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adversarial Random Forests — adversarial_rf","text":"random forest object class ranger.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adversarial Random Forests — adversarial_rf","text":"adversarial random forest (ARF) algorithm partitions data fully factorized leaves features jointly independent. ARFs trained iteratively, alternating rounds generation discrimination. first instance, synthetic data generated via independent bootstraps feature, RF classifier trained distinguish real fake samples. subsequent rounds, synthetic data generated separately leaf, using splits previous forest. creates increasingly realistic data satisfies local independence construction. algorithm converges RF reliably distinguish two classes, .e. OOB accuracy falls 0.5 + delta. ARFs useful several unsupervised learning tasks, density estimation (see forde) data synthesis (see forge). former, recommend increasing number trees improved performance (typically order 100-1000 depending sample size). Integer variables recoded warning. Default behavior convert six unique values numeric, five unique values treated ordered factors. override behavior, explicitly recode integer variables target type prior training. Note: convergence guaranteed finite samples. max_iters argument sets upper bound number training rounds. Similar results may attained increasing delta. Even single round can often give good performance, data strong complex dependencies may require iterations. default early_stop = TRUE, adversarial loop terminates performance improve one round next, case training may pointless.","code":""},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Adversarial Random Forests — adversarial_rf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/adversarial_rf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adversarial Random Forests — adversarial_rf","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 70.71% #> Iteration: 1, Accuracy: 45.95% #> Warning: executing %dopar% sequentially: no parallel backend registered psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.4833021  # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1      5.01668  if (FALSE) { # \\dontrun{ # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) } # }"},{"path":"https://bips-hb.github.io/arf/reference/arf-package.html","id":null,"dir":"Reference","previous_headings":"","what":"arf: Adversarial Random Forests — arf-package","title":"arf: Adversarial Random Forests — arf-package","text":"Adversarial random forests (ARFs) recursively partition data fully factorized leaves, features jointly independent. procedure iterative, alternating rounds generation discrimination. Data becomes increasingly realistic round, original synthetic samples can longer reliably distinguished. useful several unsupervised learning tasks, density estimation data synthesis. Methods implemented package. ARFs naturally handle unstructured data mixed continuous categorical covariates. inherit many benefits random forests, including speed, flexibility, solid performance default parameters. details, see Watson et al. (2022) arXiv:2205.09435.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/arf-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"arf: Adversarial Random Forests — arf-package","text":"Maintainer: Marvin N. Wright cran@wrig.de (ORCID) Authors: David S. Watson david.s.watson11@gmail.com (ORCID) Kristin Blesch (ORCID) Jan Kapar (ORCID)","code":""},{"path":"https://bips-hb.github.io/arf/reference/arf-package.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"arf: Adversarial Random Forests — arf-package","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 78.19% #> Iteration: 1, Accuracy: 39.04% psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.4604258  # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1     5.022068  if (FALSE) { # \\dontrun{ # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) } # }"},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute conditional circuit parameters — cforde","title":"Compute conditional circuit parameters — cforde","text":"Compute conditional circuit parameters","code":""},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute conditional circuit parameters — cforde","text":"","code":"cforde(   params,   evidence,   row_mode = c(\"separate\", \"or\"),   nomatch = c(\"force_warning\", \"force\", \"na_warning\", \"na\"),   stepsize = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute conditional circuit parameters — cforde","text":"params Circuit parameters learned via forde. evidence Data frame conditioning event(s). row_mode Interpretation rows multi-row conditions. nomatch leaf matches condition evidence? Options force sampling random leaf, either warning (\"force_warning\") without warning (\"force\"), return NA, also warning (\"na_warning\") without warning (\"na\"). default \"force_warning\". stepsize Stepsize defining number condition rows handled one step. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/cforde.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute conditional circuit parameters — cforde","text":"List conditions (evidence_input), prepared conditions (evidence_prepped) leaves match conditions evidence continuous data (cnt) categorical data (cat) well leaf info (forest).","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive column renaming — col_rename","title":"Adaptive column renaming — col_rename","text":"function renames columns case input colnames includes colnames required internal functions (e.g., \"y\").","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive column renaming — col_rename","text":"","code":"col_rename(cn, old_name)"},{"path":"https://bips-hb.github.io/arf/reference/col_rename.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive column renaming — col_rename","text":"cn Column names. old_name Name column renamed.","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Rename all problematic columns with col_rename(). — col_rename_all","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"Rename problematic columns col_rename().","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"","code":"col_rename_all(cn)"},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"cn Old column names.","code":""},{"path":"https://bips-hb.github.io/arf/reference/col_rename_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rename all problematic columns with col_rename(). — col_rename_all","text":"New columns names.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":null,"dir":"Reference","previous_headings":"","what":"Shortcut likelihood function — darf","title":"Shortcut likelihood function — darf","text":"Calls adversarial_rf, forde lik. repeated application, faster save outputs adversarial_rf forde pass via ... directly use lik.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shortcut likelihood function — darf","text":"","code":"darf(x, query = NULL, ...)"},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shortcut likelihood function — darf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. query Data frame samples, optionally comprising just subset training features. See Details lik. set x zero. ... Extra parameters passed adversarial_rf, forde lik.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shortcut likelihood function — darf","text":"vector likelihoods, optionally log scale. dataset n_synth synthetic samples nrow(x) synthetic samples n_synth undefined.","code":""},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shortcut likelihood function — darf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/darf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shortcut likelihood function — darf","text":"","code":"# Estimate log-likelihoods ll <- darf(iris)  # Partial evidence query ll <- darf(iris, query = iris[1, 1:3])  # Condition on Species = \"setosa\" ll <- darf(iris, query = iris[1, 1:3], evidence = data.frame(Species = \"setosa\"))"},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":null,"dir":"Reference","previous_headings":"","what":"Shortcut expectation function — earf","title":"Shortcut expectation function — earf","text":"Calls adversarial_rf, forde expct. repeated application, faster save outputs adversarial_rf forde pass via ... directly use expct.","code":""},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shortcut expectation function — earf","text":"","code":"earf(x, ...)"},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shortcut expectation function — earf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. ... Extra parameters passed adversarial_rf, forde expct.","code":""},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shortcut expectation function — earf","text":"one row data frame values query variables.","code":""},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shortcut expectation function — earf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/earf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shortcut expectation function — earf","text":"","code":"# What is the expected values of each feature? earf(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species #> 1     5.843333    3.057333        3.758    1.199333 virginica  #' # What is the expected values of Sepal.Length? earf(iris, query = \"Sepal.Length\") #>   Sepal.Length #> 1     5.843333  # What if we condition on Species = \"setosa\"? earf(iris, query = \"Sepal.Length\", evidence = data.frame(Species = \"setosa\")) #>   Sepal.Length #> 1     5.010467"},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Value — expct","title":"Expected Value — expct","text":"Compute expectation query variable(s), optionally conditioned event(s).","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Value — expct","text":"","code":"expct(   params,   query = NULL,   evidence = NULL,   evidence_row_mode = c(\"separate\", \"or\"),   round = FALSE,   nomatch = c(\"force_warning\", \"force\", \"na_warning\", \"na\"),   stepsize = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Value — expct","text":"params Circuit parameters learned via forde. query Optional character vector variable names. Estimates computed . NULL, variables evidence estimated. evidence contains NAs, variables estimated full dataset returned. evidence Optional set conditioning events. can take one three forms: (1) partial sample, .e. single row data columns; (2) data frame conditioning events, allows inequalities intervals; (3) posterior distribution leaves; see Details Examples. evidence_row_mode Interpretation rows multi-row evidence. 'separate', row evidence separate conditioning event n_synth synthetic samples generated. '', rows combined logical ; see Examples. round Round continuous variables respective maximum precision real data set? nomatch leaf matches condition evidence? Options force sampling random leaf, either warning (\"force_warning\") without warning (\"force\"), return NA, also warning (\"na_warning\") without warning (\"na\"). default \"force_warning\". stepsize Stepsize defining number evidence rows handled one step. Defaults nrow(evidence)/num_registered_workers parallel == TRUE. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expected Value — expct","text":"one row data frame values query variables.","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expected Value — expct","text":"function computes expected values subset features, optionally conditioned event(s). three methods (optionally) encoding conditioning events via evidence argument. first provide partial sample, columns training data missing set NA. second provide data frame condition events. supports inequalities intervals. Alternatively, users may directly input pre-calculated posterior distribution leaves, columns f_idx wt. may preferable complex constraints. See Examples. Please note results continuous features included query evidence interval condition currently inconsistent.","code":""},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Expected Value — expct","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/expct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expected Value — expct","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 77.03% #> Iteration: 1, Accuracy: 40% psi <- forde(arf, iris)  # What is the expected value of Sepal.Length? expct(psi, query = \"Sepal.Length\") #>   Sepal.Length #> 1     5.843333  # What if we condition on Species = \"setosa\"? evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1     5.026998  # Compute expectations for all features other than Species expct(psi, evidence = evi) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1     5.026998    3.414044     1.542446   0.2771403  # Condition on first two data rows with some missing values evi <- iris[1:2,] evi[1, 1] <- NA_real_ evi[1, 5] <- NA_character_ evi[2, 2] <- NA_real_ x_synth <- expct(psi, evidence = evi)  if (FALSE) { # \\dontrun{ # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) } # }"},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":null,"dir":"Reference","previous_headings":"","what":"Forests for Density Estimation — forde","title":"Forests for Density Estimation — forde","text":"Uses pre-trained ARF model estimate leaf distribution parameters.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forests for Density Estimation — forde","text":"","code":"forde(   arf,   x,   oob = FALSE,   family = \"truncnorm\",   finite_bounds = c(\"no\", \"local\", \"global\"),   alpha = 0,   epsilon = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forests for Density Estimation — forde","text":"arf Pre-trained adversarial_rf. Alternatively, object class ranger. x Training data estimating parameters. oob use --bag samples parameter estimation? TRUE, x must dataset used train arf. family Distribution use density estimation continuous features. Current options include truncated normal (default family = \"truncnorm\") uniform (family = \"unif\"). See Details. finite_bounds Impose finite bounds continuous variables? 'local', infinite bounds shrinked empirical extrema within leaves. 'global', infinite bounds shrinked global empirical extrema. '' (default), impose finite bounds. alpha Optional pseudocount Laplace smoothing categorical features. avoids zero-mass points test data fall outside support training data. Effectively parametrizes flat Dirichlet prior multinomial likelihoods. epsilon Optional slack parameter empirical bounds finite_bounds != ''. avoids zero-density points test data fall outside support training data. gap lower upper bounds expanded factor 1 + epsilon. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forests for Density Estimation — forde","text":"list 5 elements: (1) parameters continuous data; (2) parameters discrete data; (3) leaf indices coverage; (4) metadata variables; (5) data input class. list used estimating likelihoods lik generating data forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forests for Density Estimation — forde","text":"forde extracts leaf parameters pretrained forest learns distribution parameters data within leaf. former includes coverage (proportion data falling leaf) split criteria. latter includes proportions categorical features mean/variance continuous features. result probabilistic circuit, stored data.table, can used various downstream inference tasks. Currently, forde provides support limited number distributional families: truncated normal uniform continuous data, multinomial discrete data. Future releases accommodate larger set options. Though forde designed take adversarial random forest input, function's first argument can principle object class ranger. allows users test performance alternative pipelines (e.g., supervised forest input). also requirement x data used fit arf, unless oob = TRUE. fact, using another dataset may protect overfitting. connects Wager & Athey's (2018) notion \"honest trees\".","code":""},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forests for Density Estimation — forde","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375. Wager, S. & Athey, S. (2018). Estimation inference heterogeneous treatment effects using random forests. J. . Stat. Assoc., 113(523): 1228-1242.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/forde.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forests for Density Estimation — forde","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 78.52% #> Iteration: 1, Accuracy: 40.82% psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.4118479  # Expectation of Sepal.Length for class setosa evi <- data.frame(Species = \"setosa\") expct(psi, query = \"Sepal.Length\", evidence = evi) #>   Sepal.Length #> 1      5.01768  if (FALSE) { # \\dontrun{ # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) } # }"},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":null,"dir":"Reference","previous_headings":"","what":"Forests for Generative Modeling — forge","title":"Forests for Generative Modeling — forge","text":"Uses pre-trained FORDE model simulate synthetic data.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forests for Generative Modeling — forge","text":"","code":"forge(   params,   n_synth,   evidence = NULL,   evidence_row_mode = c(\"separate\", \"or\"),   round = TRUE,   sample_NAs = FALSE,   nomatch = c(\"force_warning\", \"force\", \"na_warning\", \"na\"),   stepsize = 0,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forests for Generative Modeling — forge","text":"params Circuit parameters learned via forde. n_synth Number synthetic samples generate. evidence Optional set conditioning events. can take one three forms: (1) partial sample, .e. single row data columns; (2) data frame conditioning events, allows inequalities intervals; (3) posterior distribution leaves; see Details Examples. evidence_row_mode Interpretation rows multi-row evidence. 'separate', row evidence separate conditioning event n_synth synthetic samples generated. '', rows combined logical ; see Examples. round Round continuous variables respective maximum precision real data set? sample_NAs Sample NAs respecting probability missing values original data. nomatch leaf matches condition evidence? Options force sampling random leaf, either warning (\"force_warning\") without warning (\"force\"), return NA, also warning (\"na_warning\") without warning (\"na\"). default \"force_warning\". stepsize Stepsize defining number evidence rows handled one step. Defaults nrow(evidence)/num_registered_workers parallel == TRUE. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forests for Generative Modeling — forge","text":"dataset n_synth synthetic samples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forests for Generative Modeling — forge","text":"forge simulates synthetic dataset n_synth samples. First, leaves sampled proportion either coverage (evidence = NULL) posterior probability. , feature sampled independently within leaf according probability mass density function learned forde. create realistic data long adversarial RF used previous step satisfies local independence criterion. See Watson et al. (2023). three methods (optionally) encoding conditioning events via evidence argument. first provide partial sample, columns training data missing set NA. second provide data frame condition events. supports inequalities intervals. Alternatively, users may directly input pre-calculated posterior distribution leaves, columns f_idx wt. may preferable complex constraints. See Examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forests for Generative Modeling — forge","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/forge.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forests for Generative Modeling — forge","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 76.61% #> Iteration: 1, Accuracy: 42.47% psi <- forde(arf, iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- forge(psi, n_synth = 100)  # Condition on Species = \"setosa\" evi <- data.frame(Species = \"setosa\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Condition on Species = \"setosa\" and Sepal.Length > 6 evi <- data.frame(Species = \"setosa\",                   Sepal.Length = \"(6, Inf)\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Alternative syntax for <\/> conditions evi <- data.frame(Sepal.Length = \">6\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Negation condition, i.e. all classes except \"setosa\" evi <- data.frame(Species = \"!setosa\") x_synth <- forge(psi, n_synth = 100, evidence = evi)  # Condition on first two data rows with some missing values evi <- iris[1:2,] evi[1, 1] <- NA_real_ evi[1, 5] <- NA_character_ evi[2, 2] <- NA_real_ x_synth <- forge(psi, n_synth = 1, evidence = evi)  # Or just input some distribution on leaves # (Weights that do not sum to unity are automatically scaled) n_leaves <- nrow(psi$forest) evi <- data.frame(f_idx = psi$forest$f_idx, wt = rexp(n_leaves)) x_synth <- forge(psi, n_synth = 100, evidence = evi)  if (FALSE) { # \\dontrun{ # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) } # }"},{"path":"https://bips-hb.github.io/arf/reference/impute.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value imputation with ARF — impute","title":"Missing value imputation with ARF — impute","text":"Imputed dataset missing values using adversarial random forests (ARF). Calls adversarial_rf, forde expct/forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value imputation with ARF — impute","text":"","code":"impute(   x,   m = 1,   expectation = ifelse(m == 1, TRUE, FALSE),   num_trees = 100L,   min_node_size = 10L,   round = TRUE,   finite_bounds = \"local\",   epsilon = 1e-14,   verbose = FALSE,   ... )"},{"path":"https://bips-hb.github.io/arf/reference/impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value imputation with ARF — impute","text":"x Input data. m Number multiple imputations. default single imputation (m=1). expectation Return expected value instead multiple imputations. default, single imputation (m=1), expected value returned. num_trees Number trees ARF. min_node_size Minimum node size ARF. round Round imputed values respective maximum precision original data set? finite_bounds Impose finite bounds continuous variables? See forde. epsilon Slack parameter empirical bounds; see forde. verbose Print progress adversarial_rf? ... Extra parameters passed adversarial_rf, forde expct/forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/impute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value imputation with ARF — impute","text":"Imputed data. single data table returned m=1 list data table m > 1.","code":""},{"path":"https://bips-hb.github.io/arf/reference/impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value imputation with ARF — impute","text":"","code":"# Generate some missings iris_na <- iris for (j in 1:ncol(iris)) {   iris_na[sample(1:nrow(iris), 5), j] <- NA }  # Single imputation iris_imputed <- arf::impute(iris_na, m = 1)  # Multiple imputation iris_imputed <- arf::impute(iris_na, m = 20)  if (FALSE) { # \\dontrun{ # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) } # }"},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":null,"dir":"Reference","previous_headings":"","what":"Likelihood Estimation — lik","title":"Likelihood Estimation — lik","text":"Compute likelihood input data, optionally conditioned event(s).","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Likelihood Estimation — lik","text":"","code":"lik(   params,   query,   evidence = NULL,   arf = NULL,   oob = FALSE,   log = TRUE,   batch = NULL,   parallel = TRUE )"},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Likelihood Estimation — lik","text":"params Circuit parameters learned via forde. query Data frame samples, optionally comprising just subset training features. Likelihoods computed sample. Missing features marginalized . See Details. evidence Optional set conditioning events. can take one three forms: (1) partial sample, .e. single row data columns; (2) data frame conditioning events, allows inequalities; (3) posterior distribution leaves. See Details. arf Pre-trained adversarial_rf object class ranger. required speeds computation considerably total evidence queries. (Ignored partial evidence queries.) oob use --bag leaves likelihood estimation? TRUE, x must dataset used train arf. applicable total evidence queries. log Return likelihoods log scale? Recommended prevent underflow. batch Batch size. default compute densities queries one round, always fastest option memory allows. However, large samples many trees, can memory efficient split data batches. impact results. parallel Compute parallel? Must register backend beforehand, e.g. via doParallel doFuture; see examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Likelihood Estimation — lik","text":"vector likelihoods, optionally log scale.","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Likelihood Estimation — lik","text":"function computes likelihood input data, optionally conditioned event(s). Queries may partial, .e. covering features, case excluded variables marginalized . three methods (optionally) encoding conditioning events via evidence argument. first provide partial sample, columns training data present. second provide data frame three columns: variable, relation, value. supports inequalities via relation. Alternatively, users may directly input pre-calculated posterior distribution leaves, columns f_idx wt. may preferable complex constraints. See Examples.","code":""},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Likelihood Estimation — lik","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/lik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Likelihood Estimation — lik","text":"","code":"# Train ARF and estimate leaf parameters arf <- adversarial_rf(iris) #> Iteration: 0, Accuracy: 72.05% #> Iteration: 1, Accuracy: 35.35% psi <- forde(arf, iris)  # Estimate average log-likelihood ll <- lik(psi, iris, arf = arf, log = TRUE) mean(ll) #> [1] -0.3617124  # Identical but slower ll <- lik(psi, iris, log = TRUE) #> Warning: For total evidence queries, it is faster to include the pre-trained arf. mean(ll) #> [1] -0.3617124  # Partial evidence query lik(psi, query = iris[1, 1:3]) #> [1] 0.4205125  # Condition on Species = \"setosa\" evi <- data.frame(Species = \"setosa\") lik(psi, query = iris[1, 1:3], evidence = evi) #> [1] 1.519125  # Condition on Species = \"setosa\" and Petal.Width > 0.3 evi <- data.frame(Species = \"setosa\",                    Petal.Width = \">0.3\") lik(psi, query = iris[1, 1:3], evidence = evi) #> [1] 1.703543  if (FALSE) { # \\dontrun{ # Parallelization with doParallel doParallel::registerDoParallel(cores = 4)  # ... or with doFuture doFuture::registerDoFuture() future::plan(\"multisession\", workers = 4) } # }"},{"path":"https://bips-hb.github.io/arf/reference/post_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Post-process data — post_x","title":"Post-process data — post_x","text":"function prepares output data forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/post_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Post-process data — post_x","text":"","code":"post_x(x, params, round = TRUE)"},{"path":"https://bips-hb.github.io/arf/reference/post_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Post-process data — post_x","text":"x Input data.frame. params Circuit parameters learned via forde. round Round continuous variables respective maximum precision real data set?","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess conditions — prep_cond","title":"Preprocess conditions — prep_cond","text":"function prepares conditions computing conditional circuit paramaters via cforde","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess conditions — prep_cond","text":"","code":"prep_cond(evidence, params, row_mode)"},{"path":"https://bips-hb.github.io/arf/reference/prep_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess conditions — prep_cond","text":"evidence Optional set conditioning events. params Circuit parameters learned via forde. row_mode Interpretation rows multi-row conditions.","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess input data — prep_x","title":"Preprocess input data — prep_x","text":"function prepares input data ARFs.","code":""},{"path":"https://bips-hb.github.io/arf/reference/prep_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess input data — prep_x","text":"","code":"prep_x(x)"},{"path":"https://bips-hb.github.io/arf/reference/prep_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess input data — prep_x","text":"x Input data.frame.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":null,"dir":"Reference","previous_headings":"","what":"Shortcut sampling function — rarf","title":"Shortcut sampling function — rarf","text":"Calls adversarial_rf, forde forge. repeated application, faster save outputs adversarial_rf forde pass via ... directly use forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shortcut sampling function — rarf","text":"","code":"rarf(x, n_synth = NULL, ...)"},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shortcut sampling function — rarf","text":"x Input data. Integer variables recoded ordered factors warning. See Details. n_synth Number synthetic samples generate. set nrow(x) NULL. ... Extra parameters passed adversarial_rf, forde forge.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shortcut sampling function — rarf","text":"dataset n_synth synthetic samples nrow(x) synthetic samples n_synth undefined.","code":""},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Shortcut sampling function — rarf","text":"Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests density estimation generative modeling. Proceedings 26th International Conference Artificial Intelligence Statistics, pp. 5357-5375.","code":""},{"path":[]},{"path":"https://bips-hb.github.io/arf/reference/rarf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shortcut sampling function — rarf","text":"","code":"# Generate 150 (size of original iris dataset) synthetic samples from the iris dataset x_synth <- rarf(iris)  # Generate 100 synthetic samples from the iris dataset x_synth <- rarf(iris, n_synth = 100)  # Condition on Species = \"setosa\" x_synth <- rarf(iris, evidence = data.frame(Species = \"setosa\"))"},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Safer version of sample() — resample","title":"Safer version of sample() — resample","text":"Safer version sample()","code":""},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Safer version of sample() — resample","text":"","code":"resample(x, ...)"},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Safer version of sample() — resample","text":"x vector one elements choose. ... arguments sample().","code":""},{"path":"https://bips-hb.github.io/arf/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Safer version of sample() — resample","text":"vector length size elements drawn x.","code":""},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":null,"dir":"Reference","previous_headings":"","what":"which.max() with random at ties — which.max.random","title":"which.max() with random at ties — which.max.random","text":".max() random ties","code":""},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"which.max() with random at ties — which.max.random","text":"","code":"which.max.random(x)"},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"which.max() with random at ties — which.max.random","text":"x numeric vector.","code":""},{"path":"https://bips-hb.github.io/arf/reference/which.max.random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"which.max() with random at ties — which.max.random","text":"Index maximum value x, random tie-breaking.","code":""},{"path":"https://bips-hb.github.io/arf/news/index.html","id":"arf-023","dir":"Changelog","previous_headings":"","what":"arf 0.2.3","title":"arf 0.2.3","text":"Add impute() function direct missing data imputation ARF Add one-line functions darf(), earf(), rarf()","code":""},{"path":"https://bips-hb.github.io/arf/news/index.html","id":"arf-022","dir":"Changelog","previous_headings":"","what":"arf 0.2.2","title":"arf 0.2.2","text":"Faster vectorized conditional sampling Use min.bucket argument ranger avoid pruning possible Option sample NAs generated data original data contains NAs Stepsize forge() reduce memory usage Option local global finite bounds","code":""},{"path":"https://bips-hb.github.io/arf/news/index.html","id":"arf-020","dir":"Changelog","previous_headings":"","what":"arf 0.2.0","title":"arf 0.2.0","text":"CRAN release: 2024-01-24 Vectorized adversarial resampling Speed boost compiling probabilistic circuit Conditional densities sampling Bayesian solution invariant continuous data within leaf nodes New function computing (conditional) expectations Options missing data","code":""},{"path":"https://bips-hb.github.io/arf/news/index.html","id":"arf-013","dir":"Changelog","previous_headings":"","what":"arf 0.1.3","title":"arf 0.1.3","text":"CRAN release: 2023-02-06 Speed boost adversarial resampling step Early stopping option adversarial training alpha parameter regularizing multinomial distributions forde Unified treatment colnames internal semantics (y, obs, tree, leaf)","code":""}]
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