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2026-06-23 20:16:24 +02:00 · 2026-06-23 18:00:14 +00:00 · 2026-05-02 13:06:13 +00:00 · 2026-04-30 17:52:11 +00:00 · 2026-04-30 08:07:42 +00:00 · 2026-04-25 14:29:32 +00:00
505 changed files with 29135 additions and 7388 deletions
--- a/404.html
+++ b/404.html
@@ -12,8 +12,8 @@
 <link rel="icon" sizes="any" href="https://amr-for-r.org/favicon.ico">
 <link rel="manifest" href="https://amr-for-r.org/site.webmanifest">
 <script src="https://amr-for-r.org/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<link href="https://amr-for-r.org/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="https://amr-for-r.org/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://amr-for-r.org/deps/Lato-0.4.10/font.css" rel="stylesheet">
+<link href="https://amr-for-r.org/deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
+<script src="https://amr-for-r.org/deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="https://amr-for-r.org/deps/Lato-0.4.10/font.css" rel="stylesheet">
 <link href="https://amr-for-r.org/deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
 <link href="https://amr-for-r.org/deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
 <link href="https://amr-for-r.org/deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
@@ -31,7 +31,7 @@

    <a class="navbar-brand me-2" href="https://amr-for-r.org/index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
--- a/404.md
+++ b/404.md
@@ -0,0 +1,3 @@
+Content not found. Please use links in the navbar.
+
+# Page not found (404)
--- a/CLAUDE.html
+++ b/CLAUDE.html
@@ -0,0 +1,280 @@
+<!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>CLAUDE.md — AMR R Package • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/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><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="CLAUDE.md — AMR R Package"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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 dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
+    <li><a class="dropdown-item" href="articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <li><a class="dropdown-item" href="reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
+<li class="nav-item"><a class="nav-link" href="authors.html"><span class="fa fa-users"></span> Authors</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="nav-link" href="news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul></div>
+
+
+  </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.svg" class="logo" alt=""><h1>CLAUDE.md — AMR R Package</h1>
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/CLAUDE.md" class="external-link"><code>CLAUDE.md</code></a></small>
+    </div>
+
+<div id="claudemd--amr-r-package" class="section level1">
+
+<p>This file provides context for Claude Code when working in this repository.</p>
+<div class="section level2">
+<h2 id="project-overview">Project Overview<a class="anchor" aria-label="anchor" href="#project-overview"></a></h2>
+<p><strong>AMR</strong> is a zero-dependency R package for antimicrobial resistance (AMR) data analysis using a One Health approach. It is peer-reviewed, used in 175+ countries, and supports 28 languages.</p>
+<p>Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, and WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) and disk diffusion handling - Multilingual output (28 languages)</p>
+</div>
+<div class="section level2">
+<h2 id="common-commands">Common Commands<a class="anchor" aria-label="anchor" href="#common-commands"></a></h2>
+<p>All commands run inside an R session:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)</span></span>
+<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">document</span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Run all tests</span></span>
+<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">test</span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Full package check (CRAN-level: docs + tests + checks)</span></span>
+<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">check</span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Build pkgdown website locally</span></span>
+<span><span class="fu">pkgdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgdown.r-lib.org/reference/build_site.html" class="external-link">build_site</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Code coverage report</span></span>
+<span><span class="fu">covr</span><span class="fu">::</span><span class="fu"><a href="http://covr.r-lib.org/reference/package_coverage.html" class="external-link">package_coverage</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p>From the shell:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># CRAN check from parent directory</span></span>
+<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="ex">R</span> CMD check AMR</span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="repository-structure">Repository Structure<a class="anchor" aria-label="anchor" href="#repository-structure"></a></h2>
+<pre><code>R/              # All R source files (62 files, ~28,000 lines)
+man/            # Auto-generated .Rd documentation (do not edit manually)
+tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
+data/           # Pre-compiled .rda datasets
+data-raw/       # Scripts used to generate data/ files
+vignettes/      # Rmd vignette articles
+inst/           # Installed files (translations, etc.)
+_pkgdown.yml    # pkgdown website configuration</code></pre>
+</div>
+<div class="section level2">
+<h2 id="r-source-file-conventions">R Source File Conventions<a class="anchor" aria-label="anchor" href="#r-source-file-conventions"></a></h2>
+<p><strong>Naming conventions in <code>R/</code>:</strong></p>
+<table class="table"><thead><tr><th>Prefix/Name</th>
+<th>Purpose</th>
+</tr></thead><tbody><tr><td><code>aa_*.R</code></td>
+<td>Loaded first (helpers, globals, options, package docs)</td>
+</tr><tr><td><code>zz_deprecated.R</code></td>
+<td>Deprecated function wrappers</td>
+</tr><tr><td><code>zzz.R</code></td>
+<td>
+<code>.onLoad</code> / <code>.onAttach</code> initialization</td>
+</tr></tbody></table><p><strong>Key source files:</strong></p>
+<ul><li>
+<code>aa_helper_functions.R</code> / <code>aa_helper_pm_functions.R</code> — internal utility functions (large; ~63 KB and ~37 KB)</li>
+<li>
+<code>aa_globals.R</code> — global constants and breakpoint lookup structures</li>
+<li>
+<code>aa_options.R</code> — <code>amr_options()</code> / <code>get_AMR_option()</code> system</li>
+<li>
+<code>mo.R</code> / <code>mo_property.R</code> — microorganism lookup and properties</li>
+<li>
+<code>ab.R</code> / <code>ab_property.R</code> — antimicrobial drug functions</li>
+<li>
+<code>av.R</code> / <code>av_property.R</code> — antiviral drug functions</li>
+<li>
+<code>sir.R</code> / <code>sir_calc.R</code> / <code>sir_df.R</code> — SIR classification engine</li>
+<li>
+<code>mic.R</code> / <code>disk.R</code> — MIC and disk diffusion classes</li>
+<li>
+<code>antibiogram.R</code> — antibiogram generation (traditional, combined, syndromic, WISCA)</li>
+<li>
+<code>first_isolate.R</code> — first-isolate identification algorithms</li>
+<li>
+<code>mdro.R</code> — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)</li>
+<li>
+<code>amr_selectors.R</code> — tidyselect helpers for selecting AMR columns</li>
+<li>
+<code>interpretive_rules.R</code> / <code>custom_eucast_rules.R</code> — clinical interpretation rules</li>
+<li>
+<code>translate.R</code> — 28-language translation system</li>
+<li>
+<code>ggplot_sir.R</code> / <code>ggplot_pca.R</code> / <code>plotting.R</code> — visualisation functions</li>
+</ul></div>
+<div class="section level2">
+<h2 id="custom-s3-classes">Custom S3 Classes<a class="anchor" aria-label="anchor" href="#custom-s3-classes"></a></h2>
+<p>The package defines five S3 classes with full print/format/plot/vctrs support:</p>
+<table class="table"><thead><tr><th>Class</th>
+<th>Created by</th>
+<th>Represents</th>
+</tr></thead><tbody><tr><td><code>&lt;mo&gt;</code></td>
+<td><code><a href="reference/as.mo.html">as.mo()</a></code></td>
+<td>Microorganism code</td>
+</tr><tr><td><code>&lt;ab&gt;</code></td>
+<td><code><a href="reference/as.ab.html">as.ab()</a></code></td>
+<td>Antimicrobial drug code</td>
+</tr><tr><td><code>&lt;av&gt;</code></td>
+<td><code><a href="reference/as.av.html">as.av()</a></code></td>
+<td>Antiviral drug code</td>
+</tr><tr><td><code>&lt;sir&gt;</code></td>
+<td><code><a href="reference/as.sir.html">as.sir()</a></code></td>
+<td>SIR value (S/I/R/SDD)</td>
+</tr><tr><td><code>&lt;mic&gt;</code></td>
+<td><code><a href="reference/as.mic.html">as.mic()</a></code></td>
+<td>Minimum inhibitory concentration</td>
+</tr><tr><td><code>&lt;disk&gt;</code></td>
+<td><code><a href="reference/as.disk.html">as.disk()</a></code></td>
+<td>Disk diffusion diameter</td>
+</tr></tbody></table></div>
+<div class="section level2">
+<h2 id="data-files">Data Files<a class="anchor" aria-label="anchor" href="#data-files"></a></h2>
+<p>Pre-compiled in <code>data/</code> (do not edit directly; regenerate via <code>data-raw/</code> scripts):</p>
+<table class="table"><colgroup><col width="50%"><col width="50%"></colgroup><thead><tr><th>File</th>
+<th>Contents</th>
+</tr></thead><tbody><tr><td><code>microorganisms.rda</code></td>
+<td>~79,000 microbial species with full taxonomy</td>
+</tr><tr><td><code>antimicrobials.rda</code></td>
+<td>~620 antimicrobial drugs with ATC codes</td>
+</tr><tr><td><code>antivirals.rda</code></td>
+<td>Antiviral drugs</td>
+</tr><tr><td><code>clinical_breakpoints.rda</code></td>
+<td>EUCAST + CLSI breakpoints (2011–2025)</td>
+</tr><tr><td><code>intrinsic_resistant.rda</code></td>
+<td>Intrinsic resistance patterns</td>
+</tr><tr><td><code>example_isolates.rda</code></td>
+<td>Example AMR dataset for documentation/testing</td>
+</tr><tr><td><code>WHONET.rda</code></td>
+<td>Example WHONET-format dataset</td>
+</tr></tbody></table></div>
+<div class="section level2">
+<h2 id="zero-dependency-design">Zero-Dependency Design<a class="anchor" aria-label="anchor" href="#zero-dependency-design"></a></h2>
+<p>The package has <strong>no <code>Imports</code></strong> in <code>DESCRIPTION</code>. All optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) are listed in <code>Suggests</code> and guarded with:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"pkg"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span> <span class="va">...</span> <span class="op">}</span></span></code></pre></div>
+<p>Never add packages to <code>Imports</code>. If new functionality requires an external package, add it to <code>Suggests</code> and guard usage appropriately.</p>
+</div>
+<div class="section level2">
+<h2 id="testing">Testing<a class="anchor" aria-label="anchor" href="#testing"></a></h2>
+<ul><li>
+<strong>Framework:</strong> <code>testthat</code> (R ≥ 3.1); legacy <code>tinytest</code> used for R 3.0–3.6 CI</li>
+<li>
+<strong>Test files:</strong> <code>tests/testthat/test-*.R</code>
+</li>
+<li>
+<strong>Helpers:</strong> <code>tests/testthat/helper-functions.R</code>
+</li>
+<li>
+<strong>CI matrix:</strong> GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 through oldrel-4</li>
+<li>
+<strong>Coverage:</strong> <code>covr</code> (some files excluded: <code>atc_online.R</code>, <code>mo_source.R</code>, <code>translate.R</code>, <code>resistance_predict.R</code>, <code>zz_deprecated.R</code>, helper files, <code>zzz.R</code>)</li>
+</ul></div>
+<div class="section level2">
+<h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a></h2>
+<ul><li>All exported functions use <strong>roxygen2</strong> blocks (<code>RoxygenNote: 7.3.3</code>, markdown enabled)</li>
+<li>Run <code>devtools::document()</code> after any change to roxygen comments</li>
+<li>Never edit files in <code>man/</code> directly — they are auto-generated</li>
+<li>Vignettes live in <code>vignettes/</code> as <code>.Rmd</code> files</li>
+<li>The pkgdown website is configured in <code>_pkgdown.yml</code>
+</li>
+</ul></div>
+<div class="section level2">
+<h2 id="versioning">Versioning<a class="anchor" aria-label="anchor" href="#versioning"></a></h2>
+<p>Version format: <code>major.minor.patch.dev</code> (e.g., <code>3.0.1.9021</code>)</p>
+<ul><li>Development versions use a <code>.9xxx</code> suffix</li>
+<li>Stable CRAN releases drop the dev suffix (e.g., <code>3.0.1</code>)</li>
+<li>
+<code>NEWS.md</code> uses sections <strong>New</strong>, <strong>Fixes</strong>, <strong>Updates</strong> with GitHub issue references (<code>#NNN</code>)</li>
+</ul><div class="section level3">
+<h3 id="version-and-date-bump-required-for-every-pr">Version and date bump required for every PR<a class="anchor" aria-label="anchor" href="#version-and-date-bump-required-for-every-pr"></a></h3>
+<p>All PRs are <strong>squash-merged</strong>, so each PR lands as exactly <strong>one commit</strong> on the default branch. Version numbers are kept in sync with the cumulative commit count since the last released tag. Therefore <strong>exactly one version bump is allowed per PR</strong>, regardless of how many intermediate commits are made on the branch.</p>
+<div class="section level4">
+<h4 id="computing-the-correct-version-number">Computing the correct version number<a class="anchor" aria-label="anchor" href="#computing-the-correct-version-number"></a></h4>
+<p><strong>First, ensure <code>git</code> and <code>gh</code> are installed</strong> — both are required for the version computation and for pushing changes. Install them if missing before doing anything else:</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">which</span> git <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> git</span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">which</span> gh   <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> gh</span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co"># Also ensure all tags are fetched so git describe works</span></span>
+<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="fu">git</span> fetch <span class="at">--tags</span></span></code></pre></div>
+<p>Then run the following from the repo root to determine the version string to use:</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="va">currenttag</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0 <span class="kw">|</span> <span class="fu">sed</span> <span class="st">'s/v//'</span><span class="va">)</span></span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="va">currenttagfull</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0<span class="va">)</span></span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="va">defaultbranch</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> branch <span class="kw">|</span> <span class="fu">cut</span> <span class="at">-c</span> 3- <span class="kw">|</span> <span class="fu">grep</span> <span class="at">-E</span> <span class="st">'^master$|^main$'</span><span class="va">)</span></span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="fu">git</span> fetch origin <span class="va">${defaultbranch}</span> <span class="at">--quiet</span></span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="va">currentcommit</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> rev-list <span class="at">--count</span> <span class="va">${currenttagfull}</span>..origin/<span class="va">${defaultbranch})</span></span>
+<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="va">currentversion</span><span class="op">=</span><span class="st">"</span><span class="va">${currenttag}</span><span class="st">.</span><span class="va">$((currentcommit</span> <span class="op">+</span> <span class="dv">9001</span> <span class="op">+</span> <span class="dv">1</span><span class="va">))</span><span class="st">"</span></span>
+<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a><span class="bu">echo</span> <span class="st">"</span><span class="va">$currentversion</span><span class="st">"</span></span></code></pre></div>
+<p>The <code>+ 1</code> accounts for the fact that this PR’s squash commit is not yet on the default branch. Set <strong>both</strong> of these files to the resulting version string (and only once per PR, even across multiple commits):</p>
+<ol style="list-style-type: decimal"><li><p><strong><code>DESCRIPTION</code></strong> — the <code>Version:</code> field</p></li>
+<li>
+<p><strong><code>NEWS.md</code></strong> — <strong>only replace line 1</strong> (the <code># AMR &lt;version&gt;</code> heading) with the new version number; do <strong>not</strong> create a new section. <code>NEWS.md</code> is a <strong>continuous log</strong> for the entire current <code>x.y.z.9nnn</code> development series: all changes since the last stable release accumulate under that single heading. After updating line 1, append the new change as a bullet under the appropriate sub-heading (<code>### New</code>, <code>### Fixes</code>, or <code>### Updates</code>).</p>
+<p>Style rules for <code>NEWS.md</code> entries:</p>
+<ul><li>Be <strong>extremely concise</strong> — one short line per item</li>
+<li>Do <strong>not</strong> end with a full stop (period)</li>
+<li>No verbose explanations; just the essential fact</li>
+</ul></li>
+</ol><p>If <code>git describe</code> fails (e.g. no tags exist in the environment), fall back to reading the current version from <code>DESCRIPTION</code> and adding 1 to the last numeric component — but only if no bump has already been made in this PR.</p>
+</div>
+<div class="section level4">
+<h4 id="date-field">Date field<a class="anchor" aria-label="anchor" href="#date-field"></a></h4>
+<p>The <code>Date:</code> field in <code>DESCRIPTION</code> must reflect the date of the <strong>last commit to the PR</strong> (not the first), in ISO format. Update it with every commit so it is always current:</p>
+<pre><code><span><span class="va">Date</span><span class="op">:</span> <span class="fl">2026</span><span class="op">-</span><span class="fl">03</span><span class="op">-</span><span class="fl">07</span></span></code></pre>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="internal-state">Internal State<a class="anchor" aria-label="anchor" href="#internal-state"></a></h2>
+<p>The package uses a private <code>AMR_env</code> environment (created in <code>aa_globals.R</code>) for caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). This avoids re-computation within a session.</p>
+</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><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -0,0 +1,238 @@
+# CLAUDE.md — AMR R Package
+
+This file provides context for Claude Code when working in this
+repository.
+
+## Project Overview
+
+**AMR** is a zero-dependency R package for antimicrobial resistance
+(AMR) data analysis using a One Health approach. It is peer-reviewed,
+used in 175+ countries, and supports 28 languages.
+
+Key capabilities: - SIR (Susceptible/Intermediate/Resistant)
+classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints -
+Antibiogram generation: traditional, combined, syndromic, and WISCA -
+Microorganism taxonomy database (~79,000 species) - Antimicrobial drug
+database (~620 drugs) - Multi-drug resistant organism (MDRO)
+classification - First-isolate identification - Minimum Inhibitory
+Concentration (MIC) and disk diffusion handling - Multilingual output
+(28 languages)
+
+## Common Commands
+
+All commands run inside an R session:
+
+``` r
+
+# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)
+devtools::document()
+
+# Run all tests
+devtools::test()
+
+# Full package check (CRAN-level: docs + tests + checks)
+devtools::check()
+
+# Build pkgdown website locally
+pkgdown::build_site()
+
+# Code coverage report
+covr::package_coverage()
+```
+
+From the shell:
+
+``` bash
+# CRAN check from parent directory
+R CMD check AMR
+```
+
+## Repository Structure
+
+    R/              # All R source files (62 files, ~28,000 lines)
+    man/            # Auto-generated .Rd documentation (do not edit manually)
+    tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
+    data/           # Pre-compiled .rda datasets
+    data-raw/       # Scripts used to generate data/ files
+    vignettes/      # Rmd vignette articles
+    inst/           # Installed files (translations, etc.)
+    _pkgdown.yml    # pkgdown website configuration
+
+## R Source File Conventions
+
+**Naming conventions in `R/`:**
+
+| Prefix/Name       | Purpose                                                |
+|-------------------|--------------------------------------------------------|
+| `aa_*.R`          | Loaded first (helpers, globals, options, package docs) |
+| `zz_deprecated.R` | Deprecated function wrappers                           |
+| `zzz.R`           | `.onLoad` / `.onAttach` initialization                 |
+
+**Key source files:**
+
+- `aa_helper_functions.R` / `aa_helper_pm_functions.R` — internal
+  utility functions (large; ~63 KB and ~37 KB)
+- `aa_globals.R` — global constants and breakpoint lookup structures
+- `aa_options.R` — `amr_options()` / `get_AMR_option()` system
+- `mo.R` / `mo_property.R` — microorganism lookup and properties
+- `ab.R` / `ab_property.R` — antimicrobial drug functions
+- `av.R` / `av_property.R` — antiviral drug functions
+- `sir.R` / `sir_calc.R` / `sir_df.R` — SIR classification engine
+- `mic.R` / `disk.R` — MIC and disk diffusion classes
+- `antibiogram.R` — antibiogram generation (traditional, combined,
+  syndromic, WISCA)
+- `first_isolate.R` — first-isolate identification algorithms
+- `mdro.R` — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)
+- `amr_selectors.R` — tidyselect helpers for selecting AMR columns
+- `interpretive_rules.R` / `custom_eucast_rules.R` — clinical
+  interpretation rules
+- `translate.R` — 28-language translation system
+- `ggplot_sir.R` / `ggplot_pca.R` / `plotting.R` — visualisation
+  functions
+
+## Custom S3 Classes
+
+The package defines five S3 classes with full print/format/plot/vctrs
+support:
+
+| Class | Created by | Represents |
+|----|----|----|
+| `<mo>` | [`as.mo()`](https://amr-for-r.org/reference/as.mo.md) | Microorganism code |
+| `<ab>` | [`as.ab()`](https://amr-for-r.org/reference/as.ab.md) | Antimicrobial drug code |
+| `<av>` | [`as.av()`](https://amr-for-r.org/reference/as.av.md) | Antiviral drug code |
+| `<sir>` | [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) | SIR value (S/I/R/SDD) |
+| `<mic>` | [`as.mic()`](https://amr-for-r.org/reference/as.mic.md) | Minimum inhibitory concentration |
+| `<disk>` | [`as.disk()`](https://amr-for-r.org/reference/as.disk.md) | Disk diffusion diameter |
+
+## Data Files
+
+Pre-compiled in `data/` (do not edit directly; regenerate via
+`data-raw/` scripts):
+
+| File                       | Contents                                      |
+|----------------------------|-----------------------------------------------|
+| `microorganisms.rda`       | ~79,000 microbial species with full taxonomy  |
+| `antimicrobials.rda`       | ~620 antimicrobial drugs with ATC codes       |
+| `antivirals.rda`           | Antiviral drugs                               |
+| `clinical_breakpoints.rda` | EUCAST + CLSI breakpoints (2011–2025)         |
+| `intrinsic_resistant.rda`  | Intrinsic resistance patterns                 |
+| `example_isolates.rda`     | Example AMR dataset for documentation/testing |
+| `WHONET.rda`               | Example WHONET-format dataset                 |
+
+## Zero-Dependency Design
+
+The package has **no `Imports`** in `DESCRIPTION`. All optional
+integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.)
+are listed in `Suggests` and guarded with:
+
+``` r
+
+if (requireNamespace("pkg", quietly = TRUE)) { ... }
+```
+
+Never add packages to `Imports`. If new functionality requires an
+external package, add it to `Suggests` and guard usage appropriately.
+
+## Testing
+
+- **Framework:** `testthat` (R ≥ 3.1); legacy `tinytest` used for R
+  3.0–3.6 CI
+- **Test files:** `tests/testthat/test-*.R`
+- **Helpers:** `tests/testthat/helper-functions.R`
+- **CI matrix:** GitHub Actions across Windows / macOS / Linux × R devel
+  / release / oldrel-1 through oldrel-4
+- **Coverage:** `covr` (some files excluded: `atc_online.R`,
+  `mo_source.R`, `translate.R`, `resistance_predict.R`,
+  `zz_deprecated.R`, helper files, `zzz.R`)
+
+## Documentation
+
+- All exported functions use **roxygen2** blocks (`RoxygenNote: 7.3.3`,
+  markdown enabled)
+- Run `devtools::document()` after any change to roxygen comments
+- Never edit files in `man/` directly — they are auto-generated
+- Vignettes live in `vignettes/` as `.Rmd` files
+- The pkgdown website is configured in `_pkgdown.yml`
+
+## Versioning
+
+Version format: `major.minor.patch.dev` (e.g., `3.0.1.9021`)
+
+- Development versions use a `.9xxx` suffix
+- Stable CRAN releases drop the dev suffix (e.g., `3.0.1`)
+- `NEWS.md` uses sections **New**, **Fixes**, **Updates** with GitHub
+  issue references (`#NNN`)
+
+### Version and date bump required for every PR
+
+All PRs are **squash-merged**, so each PR lands as exactly **one
+commit** on the default branch. Version numbers are kept in sync with
+the cumulative commit count since the last released tag. Therefore
+**exactly one version bump is allowed per PR**, regardless of how many
+intermediate commits are made on the branch.
+
+#### Computing the correct version number
+
+**First, ensure `git` and `gh` are installed** — both are required for
+the version computation and for pushing changes. Install them if missing
+before doing anything else:
+
+``` bash
+which git || apt-get install -y git
+which gh   || apt-get install -y gh
+# Also ensure all tags are fetched so git describe works
+git fetch --tags
+```
+
+Then run the following from the repo root to determine the version
+string to use:
+
+``` bash
+currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
+currenttagfull=$(git describe --tags --abbrev=0)
+defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
+git fetch origin ${defaultbranch} --quiet
+currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch})
+currentversion="${currenttag}.$((currentcommit + 9001 + 1))"
+echo "$currentversion"
+```
+
+The `+ 1` accounts for the fact that this PR’s squash commit is not yet
+on the default branch. Set **both** of these files to the resulting
+version string (and only once per PR, even across multiple commits):
+
+1.  **`DESCRIPTION`** — the `Version:` field
+
+2.  **`NEWS.md`** — **only replace line 1** (the `# AMR <version>`
+    heading) with the new version number; do **not** create a new
+    section. `NEWS.md` is a **continuous log** for the entire current
+    `x.y.z.9nnn` development series: all changes since the last stable
+    release accumulate under that single heading. After updating line 1,
+    append the new change as a bullet under the appropriate sub-heading
+    (`### New`, `### Fixes`, or `### Updates`).
+
+    Style rules for `NEWS.md` entries:
+
+    - Be **extremely concise** — one short line per item
+    - Do **not** end with a full stop (period)
+    - No verbose explanations; just the essential fact
+
+If `git describe` fails (e.g. no tags exist in the environment), fall
+back to reading the current version from `DESCRIPTION` and adding 1 to
+the last numeric component — but only if no bump has already been made
+in this PR.
+
+#### Date field
+
+The `Date:` field in `DESCRIPTION` must reflect the date of the **last
+commit to the PR** (not the first), in ISO format. Update it with every
+commit so it is always current:
+
+    Date: 2026-03-07
+
+## Internal State
+
+The package uses a private `AMR_env` environment (created in
+`aa_globals.R`) for caching expensive lookups (e.g., microorganism
+matching scores, breakpoint tables). This avoids re-computation within a
+session.
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.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/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/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><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></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 • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/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><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>


@@ -7,7 +7,7 @@

    <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
--- a/LICENSE-text.md
+++ b/LICENSE-text.md
@@ -0,0 +1,250 @@
+# License
+
+    GNU GENERAL PUBLIC LICENSE
+    Version 2, June 1991
+
+    Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
+      51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+    Everyone is permitted to copy and distribute verbatim copies
+    of this license document, but changing it is not allowed.
+
+    A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
+
+    This R package, with package name 'AMR':
+    - May be used for commercial purposes
+    - May be used for private purposes
+    - May NOT be used for patent purposes
+    - May be modified, although:
+      - Modifications MUST be released under the same license when distributing the package
+      - Changes made to the code MUST be documented
+    - May be distributed, although:
+      - Source code MUST be made available when the package is distributed
+      - A copy of the license and copyright notice MUST be included with the package.
+    - Comes with a LIMITATION of liability
+    - Comes with NO warranty
+
+    END OF THE SUMMARY
+
+
+    GNU GENERAL PUBLIC LICENSE
+    TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+    0. This License applies to any program or other work which contains
+    a notice placed by the copyright holder saying it may be distributed
+    under the terms of this General Public License. The "Program", below,
+    refers to any such program or work, and a "work based on the Program"
+    means either the Program or any derivative work under copyright law:
+    that is to say, a work containing the Program or a portion of it,
+    either verbatim or with modifications and/or translated into another
+    language. (Hereinafter, translation is included without limitation in
+    the term "modification".) Each licensee is addressed as "you".
+
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--- a/articles/AMR.html
+++ b/articles/AMR.html
--- a/articles/AMR.md
+++ b/articles/AMR.md
--- a/articles/AMR_files/figure-html/autoplot-2.png
+++ b/articles/AMR_files/figure-html/autoplot-2.png
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+++ b/articles/AMR_files/figure-html/unnamed-chunk-10-1.png
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--- a/articles/AMR_files/figure-html/wisca_plots-3.png
+++ b/articles/AMR_files/figure-html/wisca_plots-3.png
--- a/articles/AMR_for_Python.html
+++ b/articles/AMR_for_Python.html
@@ -12,8 +12,8 @@
 <link rel="icon" sizes="any" href="../favicon.ico">
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+<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
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 <link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
@@ -30,7 +30,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
--- a/articles/AMR_for_Python.md
+++ b/articles/AMR_for_Python.md
@@ -0,0 +1,217 @@
+# AMR for Python
+
+## Introduction
+
+The `AMR` package for R is a powerful tool for antimicrobial resistance
+(AMR) analysis. It provides extensive features for handling microbial
+and antimicrobial data. However, for those who work primarily in Python,
+we now have a more intuitive option available: the [`AMR` Python
+package](https://pypi.org/project/AMR/).
+
+This Python package is a wrapper around the `AMR` R package. It uses the
+`rpy2` package internally. Despite the need to have R installed, Python
+users can now easily work with AMR data directly through Python code.
+
+## Prerequisites
+
+This package was only tested with a [virtual environment
+(venv)](https://docs.python.org/3/library/venv.html). You can set up
+such an environment by running:
+
+``` python
+# linux and macOS:
+python -m venv /path/to/new/virtual/environment
+
+# Windows:
+python -m venv C:\path\to\new\virtual\environment
+```
+
+Then you can [activate the
+environment](https://docs.python.org/3/library/venv.html#how-venvs-work),
+after which the venv is ready to work with.
+
+## Install AMR
+
+1.  Since the Python package is available on the official [Python
+    Package Index](https://pypi.org/project/AMR/), you can just run:
+
+    ``` bash
+    pip install AMR
+    ```
+
+2.  Make sure you have R installed. There is **no need to install the
+    `AMR` R package**, as it will be installed automatically.
+
+    For Linux:
+
+    ``` bash
+    # Ubuntu / Debian
+    sudo apt install r-base
+    # Fedora:
+    sudo dnf install R
+    # CentOS/RHEL
+    sudo yum install R
+    ```
+
+    For macOS (using [Homebrew](https://brew.sh)):
+
+    ``` bash
+    brew install r
+    ```
+
+    For Windows, visit the [CRAN download
+    page](https://cran.r-project.org) to download and install R.
+
+## Examples of Usage
+
+### Cleaning Taxonomy
+
+Here’s an example that demonstrates how to clean microorganism and drug
+names using the `AMR` Python package:
+
+``` python
+import pandas as pd
+import AMR
+
+# Sample data
+data = {
+    "MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
+    "Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
+}
+df = pd.DataFrame(data)
+
+# Use AMR functions to clean microorganism and drug names
+df['MO_clean'] = AMR.mo_name(df['MOs'])
+df['Drug_clean'] = AMR.ab_name(df['Drug'])
+
+# Display the results
+print(df)
+```
+
+| MOs        | Drug     | MO_clean         | Drug_clean    |
+|------------|----------|------------------|---------------|
+| E. coli    | Cipro    | Escherichia coli | Ciprofloxacin |
+| ESCCOL     | CIP      | Escherichia coli | Ciprofloxacin |
+| esco       | J01MA02  | Escherichia coli | Ciprofloxacin |
+| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
+
+#### Explanation
+
+- **mo_name:** This function standardises microorganism names. Here,
+  different variations of *Escherichia coli* (such as “E. coli”,
+  “ESCCOL”, “esco”, and “Esche coli”) are all converted into the
+  correct, standardised form, “Escherichia coli”.
+
+- **ab_name**: Similarly, this function standardises antimicrobial
+  names. The different representations of ciprofloxacin (e.g., “Cipro”,
+  “CIP”, “J01MA02”, and “Ciproxin”) are all converted to the standard
+  name, “Ciprofloxacin”.
+
+### Calculating AMR
+
+``` python
+import AMR
+import pandas as pd
+
+df = AMR.example_isolates
+result = AMR.resistance(df["AMX"])
+print(result)
+```
+
+    [0.59555556]
+
+### Generating Antibiograms
+
+One of the core functions of the `AMR` package is generating an
+antibiogram, a table that summarises the antimicrobial susceptibility of
+bacterial isolates. Here’s how you can generate an antibiogram from
+Python:
+
+``` python
+result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
+print(result2a)
+```
+
+| Pathogen       | Amoxicillin    | Ciprofloxacin | Piperacillin/tazobactam |
+|----------------|----------------|---------------|-------------------------|
+| CoNS           | 7% (10/142)    | 73% (183/252) | 30% (10/33)             |
+| E. coli        | 50% (196/392)  | 88% (399/456) | 94% (393/416)           |
+| K. pneumoniae  | 0% (0/58)      | 96% (53/55)   | 89% (47/53)             |
+| P. aeruginosa  | 0% (0/30)      | 100% (30/30)  | None                    |
+| P. mirabilis   | None           | 94% (34/36)   | None                    |
+| S. aureus      | 6% (8/131)     | 90% (171/191) | None                    |
+| S. epidermidis | 1% (1/91)      | 64% (87/136)  | None                    |
+| S. hominis     | None           | 80% (56/70)   | None                    |
+| S. pneumoniae  | 100% (112/112) | None          | 100% (112/112)          |
+
+``` python
+result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
+print(result2b)
+```
+
+| Pathogen      | Amoxicillin   | Ciprofloxacin | Piperacillin/tazobactam |
+|---------------|---------------|---------------|-------------------------|
+| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641)           |
+| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345)           |
+
+In this example, we generate an antibiogram by selecting various
+antibiotics.
+
+### Taxonomic Data Sets Now in Python!
+
+As a Python user, you might like that the most important data sets of
+the `AMR` R package, `microorganisms`, `antimicrobials`,
+`clinical_breakpoints`, and `example_isolates`, are now available as
+regular Python data frames:
+
+``` python
+AMR.microorganisms
+```
+
+| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
+|----|----|----|----|----|----|----|----|
+| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
+| … | … | … | … | … | … | … | … |
+| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
+| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
+| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
+| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
+| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
+
+``` python
+AMR.antimicrobials
+```
+
+| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
+|----|----|----|----|----|----|----|----|
+| AMA | 4649.0 | 4-aminosalicylic acid | Antimycobacterials | 12.00 | g | NaN | None |
+| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
+| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
+| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
+| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
+| … | … | … | … | … | … | … | … |
+| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
+| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
+| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
+| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
+| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
+
+## Conclusion
+
+With the `AMR` Python package, Python users can now effortlessly call R
+functions from the `AMR` R package. This eliminates the need for complex
+`rpy2` configurations and provides a clean, easy-to-use interface for
+antimicrobial resistance analysis. The examples provided above
+demonstrate how this can be applied to typical workflows, such as
+standardising microorganism and antimicrobial names or calculating
+resistance.
+
+By just running `import AMR`, users can seamlessly integrate the robust
+features of the R `AMR` package into Python workflows.
+
+Whether you’re cleaning data or analysing resistance patterns, the `AMR`
+Python package makes it easy to work with AMR data in Python.
--- a/articles/AMR_with_tidymodels.html
+++ b/articles/AMR_with_tidymodels.html
@@ -12,8 +12,8 @@
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@@ -30,7 +30,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>


    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -88,7 +88,7 @@
    
    
 <blockquote>
-<p>This page was entirely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
+<p>This page was almost entirely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
 manually-trained model able to answer any question about the
 <code>AMR</code> package.</p>
 </blockquote>
@@ -138,8 +138,8 @@ train, and evaluate the model.</li>
 package.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Load required 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://amr-for-r.org">AMR</a></span><span class="op">)</span>          <span class="co"># For AMR data analysis</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://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span>   <span class="co"># For machine learning workflows, and data manipulation (dplyr, tidyr, ...)</span></span></code></pre></div>
+<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://amr-for-r.org">AMR</a></span><span class="op">)</span> <span class="co"># For AMR data analysis</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://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span> <span class="co"># For machine learning workflows, and data manipulation (dplyr, tidyr, ...)</span></span></code></pre></div>
 <p>Prepare the data:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Your data could look like this:</span></span>
@@ -147,16 +147,16 @@ package.</p>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
 <span><span class="co">#&gt;    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
@@ -170,23 +170,28 @@ package.</p>
 <span>  <span class="co"># select AB results dynamically</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">mo</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># replace NAs with NI (not-interpretable)</span></span>
-<span>   <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
-<span>                 <span class="op">~</span><span class="fu">replace_na</span><span class="op">(</span><span class="va">.x</span>, <span class="st">"NI"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>          <span class="co"># make factors of SIR columns</span></span>
-<span>          <span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
-<span>                 <span class="va">as.integer</span><span class="op">)</span>,</span>
-<span>          <span class="co"># get Gramstain of microorganisms</span></span>
-<span>          mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
+<span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span></span>
+<span>      <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>      <span class="op">~</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">.x</span>, <span class="st">"NI"</span><span class="op">)</span></span>
+<span>    <span class="op">)</span>,</span>
+<span>    <span class="co"># make factors of SIR columns</span></span>
+<span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span></span>
+<span>      <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>      <span class="va">as.integer</span></span>
+<span>    <span class="op">)</span>,</span>
+<span>    <span class="co"># get Gramstain of microorganisms</span></span>
+<span>    mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># drop NAs - the ones without a Gramstain (fungi, etc.)</span></span>
 <span>  <span class="fu">drop_na</span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
-<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`betalactams()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span></span>
+<span><span class="co">#&gt;   (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> For `betalactams()` using columns <span style="color: #00BB00; font-weight: bold;">PEN</span> (benzylpenicillin), <span style="color: #00BB00; font-weight: bold;">OXA</span> (oxacillin),</span></span>
+<span><span class="co">#&gt;   <span style="color: #00BB00; font-weight: bold;">FLC</span> (flucloxacillin), <span style="color: #00BB00; font-weight: bold;">AMX</span> (amoxicillin), <span style="color: #00BB00; font-weight: bold;">AMC</span> (amoxicillin/clavulanic acid),</span></span>
+<span><span class="co">#&gt;   <span style="color: #00BB00; font-weight: bold;">AMP</span> (ampicillin), <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam), <span style="color: #00BB00; font-weight: bold;">CZO</span> (cefazolin), <span style="color: #00BB00; font-weight: bold;">FEP</span></span></span>
+<span><span class="co">#&gt;   (cefepime), <span style="color: #00BB00; font-weight: bold;">CXM</span> (cefuroxime), <span style="color: #00BB00; font-weight: bold;">FOX</span> (cefoxitin), <span style="color: #00BB00; font-weight: bold;">CTX</span> (cefotaxime), <span style="color: #00BB00; font-weight: bold;">CAZ</span></span></span>
+<span><span class="co">#&gt;   (ceftazidime), <span style="color: #00BB00; font-weight: bold;">CRO</span> (ceftriaxone), <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem), and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
@@ -227,26 +232,36 @@ we have with <code>step_corr()</code>, the necessary parameters can be
 estimated from a training set using <code>prep()</code>:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">prep</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span></span>
-<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
-<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`betalactams()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span></span>
+<span><span class="co">#&gt;   (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> For `betalactams()` using columns <span style="color: #00BB00; font-weight: bold;">PEN</span> (benzylpenicillin), <span style="color: #00BB00; font-weight: bold;">OXA</span> (oxacillin),</span></span>
+<span><span class="co">#&gt;   <span style="color: #00BB00; font-weight: bold;">FLC</span> (flucloxacillin), <span style="color: #00BB00; font-weight: bold;">AMX</span> (amoxicillin), <span style="color: #00BB00; font-weight: bold;">AMC</span> (amoxicillin/clavulanic acid),</span></span>
+<span><span class="co">#&gt;   <span style="color: #00BB00; font-weight: bold;">AMP</span> (ampicillin), <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam), <span style="color: #00BB00; font-weight: bold;">CZO</span> (cefazolin), <span style="color: #00BB00; font-weight: bold;">FEP</span></span></span>
+<span><span class="co">#&gt;   (cefepime), <span style="color: #00BB00; font-weight: bold;">CXM</span> (cefuroxime), <span style="color: #00BB00; font-weight: bold;">FOX</span> (cefoxitin), <span style="color: #00BB00; font-weight: bold;">CTX</span> (cefotaxime), <span style="color: #00BB00; font-weight: bold;">CAZ</span></span></span>
+<span><span class="co">#&gt;   (ceftazidime), <span style="color: #00BB00; font-weight: bold;">CRO</span> (ceftriaxone), <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem), and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span></span>
+<span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; ── Inputs</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Inputs </span></span>
+<span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Number of variables by role</span></span>
+<span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; outcome:    1</span></span>
 <span><span class="co">#&gt; predictor: 20</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; ── Training information</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Training information </span></span>
+<span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Training data contained 1968 data points and no incomplete rows.</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; ── Operations</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Operations </span></span>
+<span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">AMX</span> <span style="color: #0000BB;">CTX</span> | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
@@ -325,7 +340,7 @@ performance.</p>
 <span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span> <span class="co"># For reproducibility</span></span>
 <span><span class="va">data_split</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span> <span class="co"># 80% training, 20% testing</span></span>
 <span><span class="va">training_data</span> <span class="op">&lt;-</span> <span class="fu">training</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span> <span class="co"># Training set</span></span>
-<span><span class="va">testing_data</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span>   <span class="co"># Testing set</span></span>
+<span><span class="va">testing_data</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span> <span class="co"># Testing set</span></span>
 <span></span>
 <span><span class="co"># Fit the workflow to the training data</span></span>
 <span><span class="va">fitted_workflow</span> <span class="op">&lt;-</span> <span class="va">resistance_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
@@ -345,7 +360,7 @@ called since they are stored in the recipe.</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Make predictions on the testing set</span></span>
 <span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span>                <span class="co"># Generate predictions</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span> <span class="co"># Generate predictions</span></span>
 <span><span class="va">probabilities</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span>, type <span class="op">=</span> <span class="st">"prob"</span><span class="op">)</span> <span class="co"># Generate probabilities</span></span>
 <span></span>
@@ -413,13 +428,13 @@ ROC curve looks like this:</p>
 <code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">roc_curve</span><span class="op">(</span><span class="va">mo</span>, <span class="va">`.pred_Gram-negative`</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" width="720"></p>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" alt="" width="720"></p>
 </div>
 <div class="section level3">
 <h3 id="conclusion">
 <strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion"></a>
 </h3>
-<p>In this post, we demonstrated how to build a machine learning
+<p>In this example, we demonstrated how to build a machine learning
 pipeline with the <code>tidymodels</code> framework and the
 <code>AMR</code> package. By combining selector functions like
 <code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code> with
@@ -442,7 +457,7 @@ transformation to <code>log2</code> scale using
 classification models.</p>
 <p>This approach and idea formed the basis for the publication <a href="https://doi.org/10.3389/fmicb.2025.1582703" class="external-link">DOI:
 10.3389/fmicb.2025.1582703</a> to model the presence of
-extended-spectrum beta-lactamases (ESBL).</p>
+extended-spectrum beta-lactamases (ESBL) based on MIC values.</p>
 <div class="section level3">
 <h3 id="objective-1">
 <strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective-1"></a>
@@ -473,16 +488,16 @@ package.</p>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 500 × 19</span></span></span>
 <span><span class="co">#&gt;    esbl  genus   AMC   AMP   TZP   CXM   FOX   CTX   CAZ   GEN   TOB   TMP   SXT</span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> FALSE Esch…    32    32     4    64    64  8<span style="color: #BBBBBB;">.00</span>  8<span style="color: #BBBBBB;">.00</span>     1     1  16<span style="color: #BBBBBB;">.0</span>    20</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> FALSE Esch…    32    32     4    64    64  4<span style="color: #BBBBBB;">.00</span>  8<span style="color: #BBBBBB;">.00</span>     1     1  16<span style="color: #BBBBBB;">.0</span>   320</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> FALSE Esch…     4     2    64     8     4  8<span style="color: #BBBBBB;">.00</span>  0.12    16    16   0.5    20</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> FALSE Kleb…    32    32    16    64    64  8<span style="color: #BBBBBB;">.00</span>  8<span style="color: #BBBBBB;">.00</span>     1     1   0.5    20</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> FALSE Esch…    32    32     4     4     4  0.25  2<span style="color: #BBBBBB;">.00</span>     1     1  16<span style="color: #BBBBBB;">.0</span>   320</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> FALSE Citr…    32    32    16    64    64 64<span style="color: #BBBBBB;">.00</span> 32<span style="color: #BBBBBB;">.00</span>     1     1   0.5    20</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> FALSE Morg…    32    32     4    64    64 16<span style="color: #BBBBBB;">.00</span>  2<span style="color: #BBBBBB;">.00</span>     1     1   0.5    20</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> FALSE Prot…    16    32     4     1     4  8<span style="color: #BBBBBB;">.00</span>  0.12     1     1  16<span style="color: #BBBBBB;">.0</span>   320</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> FALSE Ente…    32    32     8    64    64 32<span style="color: #BBBBBB;">.00</span>  4<span style="color: #BBBBBB;">.00</span>     1     1   0.5    20</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> FALSE Citr…    32    32    32    64    64  8<span style="color: #BBBBBB;">.00</span> 64<span style="color: #BBBBBB;">.00</span>     1     1  16<span style="color: #BBBBBB;">.0</span>   320</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> FALSE Esch…    32    32     4    64    64  8<span style="color: #949494;">.00</span>  8<span style="color: #949494;">.00</span>     1     1  16<span style="color: #949494;">.0</span>    20</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> FALSE Esch…    32    32     4    64    64  4<span style="color: #949494;">.00</span>  8<span style="color: #949494;">.00</span>     1     1  16<span style="color: #949494;">.0</span>   320</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> FALSE Esch…     4     2    64     8     4  8<span style="color: #949494;">.00</span>  0.12    16    16   0.5    20</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> FALSE Kleb…    32    32    16    64    64  8<span style="color: #949494;">.00</span>  8<span style="color: #949494;">.00</span>     1     1   0.5    20</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> FALSE Esch…    32    32     4     4     4  0.25  2<span style="color: #949494;">.00</span>     1     1  16<span style="color: #949494;">.0</span>   320</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> FALSE Citr…    32    32    16    64    64 64<span style="color: #949494;">.00</span> 32<span style="color: #949494;">.00</span>     1     1   0.5    20</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> FALSE Morg…    32    32     4    64    64 16<span style="color: #949494;">.00</span>  2<span style="color: #949494;">.00</span>     1     1   0.5    20</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> FALSE Prot…    16    32     4     1     4  8<span style="color: #949494;">.00</span>  0.12     1     1  16<span style="color: #949494;">.0</span>   320</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> FALSE Ente…    32    32     8    64    64 32<span style="color: #949494;">.00</span>  4<span style="color: #949494;">.00</span>     1     1   0.5    20</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> FALSE Citr…    32    32    32    64    64  8<span style="color: #949494;">.00</span> 64<span style="color: #949494;">.00</span>     1     1  16<span style="color: #949494;">.0</span>   320</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 490 more rows</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 6 more variables: NIT &lt;mic&gt;, FOS &lt;mic&gt;, CIP &lt;mic&gt;, IPM &lt;mic&gt;, MEM &lt;mic&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   COL &lt;mic&gt;</span></span></span>
@@ -520,11 +535,10 @@ predictors can easily and agnostically selected using the new
 <span></span>
 <span><span class="co"># Define the recipe</span></span>
 <span><span class="va">mic_recipe</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">esbl</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">training_data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="fu">remove_role</span><span class="op">(</span><span class="va">genus</span>, old_role <span class="op">=</span> <span class="st">"predictor"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="co"># Remove non-informative variable</span></span>
-<span>  <span class="fu"><a href="../reference/amr-tidymodels.html">step_mic_log2</a></span><span class="op">(</span><span class="fu"><a href="../reference/amr-tidymodels.html">all_mic_predictors</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co">#%&gt;%         # Log2 transform all MIC predictors</span></span>
-<span> <span class="co"># prep()</span></span>
+<span>  <span class="fu">remove_role</span><span class="op">(</span><span class="va">genus</span>, old_role <span class="op">=</span> <span class="st">"predictor"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Remove non-informative variable</span></span>
+<span>  <span class="fu"><a href="../reference/amr-tidymodels.html">step_mic_log2</a></span><span class="op">(</span><span class="fu"><a href="../reference/amr-tidymodels.html">all_mic_predictors</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># Log2 transform all MIC predictors</span></span>
 <span></span>
-<span><span class="va">mic_recipe</span></span>
+<span><span class="fu">prep</span><span class="op">(</span><span class="va">mic_recipe</span><span class="op">)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
 <span><span class="co">#&gt; </span></span>
@@ -534,8 +548,11 @@ predictors can easily and agnostically selected using the new
 <span><span class="co">#&gt; predictor:       17</span></span>
 <span><span class="co">#&gt; undeclared role:  1</span></span>
 <span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Training information</span></span>
+<span><span class="co">#&gt; Training data contained 375 data points and no incomplete rows.</span></span>
+<span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Operations</span></span>
-<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Log2 transformation of MIC columns: <span style="color: #0000BB;">all_mic_predictors()</span></span></span></code></pre></div>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Log2 transformation of MIC columns: <span style="color: #0000BB;">AMC</span>, <span style="color: #0000BB;">AMP</span>, <span style="color: #0000BB;">TZP</span>, <span style="color: #0000BB;">CXM</span>, <span style="color: #0000BB;">FOX</span>, ... | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
@@ -608,20 +625,24 @@ model.</li>
 <span></span>
 <span><span class="co"># Generate predictions</span></span>
 <span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">fitted</span>, <span class="va">testing_data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">fitted</span>, <span class="va">testing_data</span>, type <span class="op">=</span> <span class="st">"prob"</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># add probabilities</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Evaluate model performance</span></span>
-<span><span class="va">our_metrics</span> <span class="op">&lt;-</span> <span class="fu">metric_set</span><span class="op">(</span><span class="va">accuracy</span>, <span class="va">kap</span>, <span class="va">ppv</span>, <span class="va">npv</span><span class="op">)</span></span>
+<span><span class="va">our_metrics</span> <span class="op">&lt;-</span> <span class="fu">metric_set</span><span class="op">(</span><span class="va">accuracy</span>, <span class="va">recall</span>, <span class="va">precision</span>, <span class="va">sensitivity</span>, <span class="va">specificity</span>, <span class="va">ppv</span>, <span class="va">npv</span><span class="op">)</span></span>
 <span><span class="va">metrics</span> <span class="op">&lt;-</span> <span class="fu">our_metrics</span><span class="op">(</span><span class="va">predictions</span>, truth <span class="op">=</span> <span class="va">esbl</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">metrics</span></span>
-<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 3</span></span></span>
-<span><span class="co">#&gt;   .metric  .estimator .estimate</span></span>
-<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary         0.92 </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> kap      binary         0.840</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> ppv      binary         0.921</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> npv      binary         0.919</span></span></code></pre></div>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 7 × 3</span></span></span>
+<span><span class="co">#&gt;   .metric     .estimator .estimate</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy    binary         0.92 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> recall      binary         0.921</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> precision   binary         0.921</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> sensitivity binary         0.921</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> specificity binary         0.919</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span> ppv         binary         0.921</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">7</span> npv         binary         0.919</span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
@@ -632,11 +653,12 @@ data.</li>
 data.</li>
 <li>
 <code>metric_set()</code>: Allows evaluating multiple classification
-metrics.</li>
+metrics. This will make <code>our_metrics</code> to become a function
+that we can use to check the predictions with.</li>
 </ul>
 <p>It appears we can predict ESBL gene presence with a positive
 predictive value (PPV) of 92.1% and a negative predictive value (NPV) of
-91.9 using a simplistic logistic regression model.</p>
+91.9% using a simplistic logistic regression model.</p>
 </div>
 <div class="section level3">
 <h3 id="visualising-predictions">
@@ -649,11 +671,40 @@ status.</p>
 <span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">predictions</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">esbl</span>, fill <span class="op">=</span> <span class="va">.pred_class</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/geom_bar.html" class="external-link">geom_bar</a></span><span class="op">(</span>position <span class="op">=</span> <span class="st">"stack"</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">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Predicted vs Actual ESBL Status"</span>,</span>
-<span>       x <span class="op">=</span> <span class="st">"Actual ESBL"</span>,</span>
-<span>       y <span class="op">=</span> <span class="st">"Count"</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">labs</a></span><span class="op">(</span></span>
+<span>    title <span class="op">=</span> <span class="st">"Predicted vs Actual ESBL Status"</span>,</span>
+<span>    x <span class="op">=</span> <span class="st">"Actual ESBL"</span>,</span>
+<span>    y <span class="op">=</span> <span class="st">"Count"</span></span>
+<span>  <span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" width="720"></p>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" class="r-plt" alt="" width="720"></p>
+<p>And plot the certainties too - how certain were the actual
+predictions?</p>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
+<span>    certainty <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">.pred_class</span> <span class="op">==</span> <span class="st">"FALSE"</span>,</span>
+<span>      <span class="va">.pred_FALSE</span>,</span>
+<span>      <span class="va">.pred_TRUE</span></span>
+<span>    <span class="op">)</span>,</span>
+<span>    correct <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">esbl</span> <span class="op">==</span> <span class="va">.pred_class</span>, <span class="st">"Right"</span>, <span class="st">"Wrong"</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span></span>
+<span>    x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_len</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">predictions</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>    y <span class="op">=</span> <span class="va">certainty</span>,</span>
+<span>    colour <span class="op">=</span> <span class="va">correct</span></span>
+<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/scale_manual.html" class="external-link">scale_colour_manual</a></span><span class="op">(</span></span>
+<span>    values <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>Right <span class="op">=</span> <span class="st">"green3"</span>, Wrong <span class="op">=</span> <span class="st">"red2"</span><span class="op">)</span>,</span>
+<span>    name <span class="op">=</span> <span class="st">"Correct?"</span></span>
+<span>  <span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</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/scale_continuous.html" class="external-link">scale_y_continuous</a></span><span class="op">(</span></span>
+<span>    labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">x</span> <span class="op">*</span> <span class="fl">100</span>, <span class="st">"%"</span><span class="op">)</span>,</span>
+<span>    limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png" class="r-plt" alt="" width="720"></p>
 </div>
 <div class="section level3">
 <h3 id="conclusion-1">
@@ -662,8 +713,8 @@ status.</p>
 <p>In this example, we showcased how the new <code>AMR</code>-specific
 recipe steps simplify working with <code>&lt;mic&gt;</code> columns in
 <code>tidymodels</code>. The <code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code> transformation
-converts ordered MICs to log2-transformed numerics, improving
-compatibility with classification models.</p>
+converts MICs (with or without operators) to log2-transformed numerics,
+improving compatibility with classification models.</p>
 <p>This pipeline enables realistic, reproducible, and interpretable
 modelling of antimicrobial resistance data.</p>
 <hr>
@@ -696,7 +747,7 @@ model.</li>
 </h3>
 <p>We start by transforming the <code>example_isolates</code> dataset
 into a structured time-series format.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Load required 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://amr-for-r.org">AMR</a></span><span class="op">)</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://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span></span>
@@ -704,15 +755,24 @@ into a structured time-series format.</p>
 <span><span class="co"># Transform dataset</span></span>
 <span><span class="va">data_time</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Filter on the top #10 species</span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>year <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/format.html" class="external-link">format</a></span><span class="op">(</span><span class="va">date</span>, <span class="st">"%Y"</span><span class="op">)</span><span class="op">)</span>,  <span class="co"># Extract year from date</span></span>
-<span>         gramstain <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Get taxonomic names</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
+<span>    year <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/format.html" class="external-link">format</a></span><span class="op">(</span><span class="va">date</span>, <span class="st">"%Y"</span><span class="op">)</span><span class="op">)</span>, <span class="co"># Extract year from date</span></span>
+<span>    gramstain <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Get taxonomic names</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">year</span>, <span class="va">gramstain</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">AMX</span>, <span class="va">AMC</span>, <span class="va">CIP</span><span class="op">)</span>, </span>
-<span>                   <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">x</span>, minimum <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>                   .names <span class="op">=</span> <span class="st">"res_{.col}"</span><span class="op">)</span>, </span>
-<span>            .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span></span>
+<span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">AMX</span>, <span class="va">AMC</span>, <span class="va">CIP</span><span class="op">)</span>,</span>
+<span>      <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">x</span>, minimum <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
+<span>      .names <span class="op">=</span> <span class="st">"res_{.col}"</span></span>
+<span>    <span class="op">)</span>,</span>
+<span>    .groups <span class="op">=</span> <span class="st">"drop"</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMX</span><span class="op">)</span> <span class="op">&amp;</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMC</span><span class="op">)</span> <span class="op">&amp;</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_CIP</span><span class="op">)</span><span class="op">)</span> <span class="co"># Drop missing values</span></span>
-<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ Using column '</span><span style="color: #0000BB; font-weight: bold;">mo</span><span style="color: #0000BB;">' as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">`col_mo`</span><span style="color: #0000BB;">.</span></span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `col_mo`.</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> `resistance()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
+<span><span class="co">#&gt;   category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
+<span><span class="co">#&gt;   option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span>
 <span></span>
 <span><span class="va">data_time</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 32 × 5</span></span></span>
@@ -751,12 +811,12 @@ preprocessing step, a model specification, and the fitting process.</p>
 <div class="section level4">
 <h4 id="preprocessing-with-a-recipe-2">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe-2"></a>
 </h4>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Define the recipe</span></span>
 <span><span class="va">resistance_recipe_time</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">res_AMX</span> <span class="op">~</span> <span class="va">year</span> <span class="op">+</span> <span class="va">gramstain</span>, data <span class="op">=</span> <span class="va">data_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="fu">step_dummy</span><span class="op">(</span><span class="va">gramstain</span>, one_hot <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="co"># Convert categorical to numerical</span></span>
-<span>  <span class="fu">step_normalize</span><span class="op">(</span><span class="va">year</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="co"># Normalise year for better model performance</span></span>
-<span>  <span class="fu">step_nzv</span><span class="op">(</span><span class="fu">all_predictors</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>  <span class="co"># Remove near-zero variance predictors</span></span>
+<span>  <span class="fu">step_dummy</span><span class="op">(</span><span class="va">gramstain</span>, one_hot <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Convert categorical to numerical</span></span>
+<span>  <span class="fu">step_normalize</span><span class="op">(</span><span class="va">year</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Normalise year for better model performance</span></span>
+<span>  <span class="fu">step_nzv</span><span class="op">(</span><span class="fu">all_predictors</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># Remove near-zero variance predictors</span></span>
 <span></span>
 <span><span class="va">resistance_recipe_time</span></span>
 <span><span class="co">#&gt; </span></span>
@@ -787,7 +847,7 @@ variable.</li>
 <h4 id="specifying-the-model-2">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model-2"></a>
 </h4>
 <p>We use a linear regression model to predict resistance trends.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Define the linear regression model</span></span>
 <span><span class="va">lm_model</span> <span class="op">&lt;-</span> <span class="fu">linear_reg</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">set_engine</span><span class="op">(</span><span class="st">"lm"</span><span class="op">)</span> <span class="co"># Use linear regression</span></span>
@@ -809,7 +869,7 @@ engine.</li>
 <h4 id="building-the-workflow-2">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow-2"></a>
 </h4>
 <p>We combine the preprocessing recipe and model into a workflow.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Create workflow</span></span>
 <span><span class="va">resistance_workflow_time</span> <span class="op">&lt;-</span> <span class="fu">workflow</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
@@ -839,7 +899,7 @@ engine.</li>
 </h3>
 <p>We split the data into training and testing sets, fit the model, and
 evaluate performance.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Split the data</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
 <span><span class="va">data_split_time</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data_time</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span>
@@ -853,7 +913,7 @@ evaluate performance.</p>
 <span><span class="co"># Make predictions</span></span>
 <span><span class="va">predictions_time</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Evaluate model</span></span>
 <span><span class="va">metrics_time</span> <span class="op">&lt;-</span> <span class="va">predictions_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
@@ -884,32 +944,38 @@ sets.</li>
 <strong>Visualising Predictions</strong><a class="anchor" aria-label="anchor" href="#visualising-predictions-1"></a>
 </h3>
 <p>We plot resistance trends over time for amoxicillin.</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><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://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Plot actual vs predicted resistance over time</span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">predictions_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</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/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="st">"Actual"</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/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>y <span class="op">=</span> <span class="va">.pred</span>, color <span class="op">=</span> <span class="st">"Predicted"</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">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Predicted vs Actual AMX Resistance Over Time"</span>,</span>
-<span>       x <span class="op">=</span> <span class="st">"Year"</span>,</span>
-<span>       y <span class="op">=</span> <span class="st">"Resistance Proportion"</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">labs</a></span><span class="op">(</span></span>
+<span>    title <span class="op">=</span> <span class="st">"Predicted vs Actual AMX Resistance Over Time"</span>,</span>
+<span>    x <span class="op">=</span> <span class="st">"Year"</span>,</span>
+<span>    y <span class="op">=</span> <span class="st">"Resistance Proportion"</span></span>
+<span>  <span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-20-1.png" width="720"></p>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png" class="r-plt" alt="" width="720"></p>
 <p>Additionally, we can visualise resistance trends in
 <code>ggplot2</code> and directly add linear models there:</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">data_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</span>, y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="va">gramstain</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/geom_path.html" class="external-link">geom_line</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">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"AMX Resistance Trends"</span>,</span>
-<span>       x <span class="op">=</span> <span class="st">"Year"</span>,</span>
-<span>       y <span class="op">=</span> <span class="st">"Resistance Proportion"</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">labs</a></span><span class="op">(</span></span>
+<span>    title <span class="op">=</span> <span class="st">"AMX Resistance Trends"</span>,</span>
+<span>    x <span class="op">=</span> <span class="st">"Year"</span>,</span>
+<span>    y <span class="op">=</span> <span class="st">"Resistance Proportion"</span></span>
+<span>  <span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="co"># add a linear model directly in ggplot2:</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_smooth.html" class="external-link">geom_smooth</a></span><span class="op">(</span>method <span class="op">=</span> <span class="st">"lm"</span>,</span>
-<span>              formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span>
-<span>              alpha <span class="op">=</span> <span class="fl">0.25</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_smooth.html" class="external-link">geom_smooth</a></span><span class="op">(</span></span>
+<span>    method <span class="op">=</span> <span class="st">"lm"</span>,</span>
+<span>    formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span>
+<span>    alpha <span class="op">=</span> <span class="fl">0.25</span></span>
+<span>  <span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png" width="720"></p>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png" class="r-plt" alt="" width="720"></p>
 </div>
 <div class="section level3">
 <h3 id="conclusion-2">
--- a/articles/AMR_with_tidymodels.md
+++ b/articles/AMR_with_tidymodels.md
@@ -0,0 +1,903 @@
+# AMR with tidymodels
+
+> This page was almost entirely written by our [AMR for R
+> Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained
+> model able to answer any question about the `AMR` package.
+
+Antimicrobial resistance (AMR) is a global health crisis, and
+understanding resistance patterns is crucial for managing effective
+treatments. The `AMR` R package provides robust tools for analysing AMR
+data, including convenient antimicrobial selector functions like
+[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+and
+[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
+
+In this post, we will explore how to use the `tidymodels` framework to
+predict resistance patterns in the `example_isolates` dataset in two
+examples.
+
+This post contains the following examples:
+
+1.  Using Antimicrobial Selectors
+2.  Predicting ESBL Presence Using Raw MICs
+3.  Predicting AMR Over Time
+
+## Example 1: Using Antimicrobial Selectors
+
+By leveraging the power of `tidymodels` and the `AMR` package, we’ll
+build a reproducible machine learning workflow to predict the Gramstain
+of the microorganism to two important antibiotic classes:
+aminoglycosides and beta-lactams.
+
+### **Objective**
+
+Our goal is to build a predictive model using the `tidymodels` framework
+to determine the Gramstain of the microorganism based on microbial data.
+We will:
+
+1.  Preprocess data using the selector functions
+    [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+    and
+    [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
+2.  Define a logistic regression model for prediction.
+3.  Use a structured `tidymodels` workflow to preprocess, train, and
+    evaluate the model.
+
+### **Data Preparation**
+
+We begin by loading the required libraries and preparing the
+`example_isolates` dataset from the `AMR` package.
+
+``` r
+
+# Load required libraries
+library(AMR) # For AMR data analysis
+library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
+```
+
+Prepare the data:
+
+``` r
+
+# Your data could look like this:
+example_isolates
+#> # A tibble: 2,000 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
+#> # ℹ 1,990 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+
+# Select relevant columns for prediction
+data <- example_isolates %>%
+  # select AB results dynamically
+  select(mo, aminoglycosides(), betalactams()) %>%
+  # replace NAs with NI (not-interpretable)
+  mutate(
+    across(
+      where(is.sir),
+      ~ replace_na(.x, "NI")
+    ),
+    # make factors of SIR columns
+    across(
+      where(is.sir),
+      as.integer
+    ),
+    # get Gramstain of microorganisms
+    mo = as.factor(mo_gramstain(mo))
+  ) %>%
+  # drop NAs - the ones without a Gramstain (fungi, etc.)
+  drop_na()
+#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
+#>   (amikacin), and KAN (kanamycin)
+#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
+#>   FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
+#>   AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
+#>   (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
+#>   (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
+```
+
+**Explanation:**
+
+- [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+  and
+  [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+  dynamically select columns for antimicrobials in these classes.
+- `drop_na()` ensures the model receives complete cases for training.
+
+### **Defining the Workflow**
+
+We now define the `tidymodels` workflow, which consists of three steps:
+preprocessing, model specification, and fitting.
+
+#### 1. Preprocessing with a Recipe
+
+We create a recipe to preprocess the data for modelling.
+
+``` r
+
+# Define the recipe for data preprocessing
+resistance_recipe <- recipe(mo ~ ., data = data) %>%
+  step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
+resistance_recipe
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> ── Inputs
+#> Number of variables by role
+#> outcome:    1
+#> predictor: 20
+#> 
+#> ── Operations
+#> • Correlation filter on: c(aminoglycosides(), betalactams())
+```
+
+For a recipe that includes at least one preprocessing operation, like we
+have with `step_corr()`, the necessary parameters can be estimated from
+a training set using `prep()`:
+
+``` r
+
+prep(resistance_recipe)
+#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
+#>   (amikacin), and KAN (kanamycin)
+#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
+#>   FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
+#>   AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
+#>   (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
+#>   (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
+#> 
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> 
+#> 
+#> ── Inputs 
+#> 
+#> Number of variables by role
+#> 
+#> outcome:    1
+#> predictor: 20
+#> 
+#> 
+#> 
+#> ── Training information 
+#> 
+#> Training data contained 1968 data points and no incomplete rows.
+#> 
+#> 
+#> 
+#> ── Operations 
+#> 
+#> • Correlation filter on: AMX CTX | Trained
+```
+
+**Explanation:**
+
+- `recipe(mo ~ ., data = data)` will take the `mo` column as outcome and
+  all other columns as predictors.
+- `step_corr()` removes predictors (i.e., antibiotic columns) that have
+  a higher correlation than 90%.
+
+Notice how the recipe contains just the antimicrobial selector
+functions - no need to define the columns specifically. In the
+preparation (retrieved with `prep()`) we can see that the columns or
+variables ‘AMX’ and ‘CTX’ were removed as they correlate too much with
+existing, other variables.
+
+#### 2. Specifying the Model
+
+We define a logistic regression model since resistance prediction is a
+binary classification task.
+
+``` r
+
+# Specify a logistic regression model
+logistic_model <- logistic_reg() %>%
+  set_engine("glm") # Use the Generalised Linear Model engine
+logistic_model
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+**Explanation:**
+
+- `logistic_reg()` sets up a logistic regression model.
+- `set_engine("glm")` specifies the use of R’s built-in GLM engine.
+
+#### 3. Building the Workflow
+
+We bundle the recipe and model together into a `workflow`, which
+organises the entire modelling process.
+
+``` r
+
+# Combine the recipe and model into a workflow
+resistance_workflow <- workflow() %>%
+  add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
+  add_model(logistic_model) # Add the logistic regression model
+resistance_workflow
+#> ══ Workflow ════════════════════════════════════════════════════════════════════
+#> Preprocessor: Recipe
+#> Model: logistic_reg()
+#> 
+#> ── Preprocessor ────────────────────────────────────────────────────────────────
+#> 1 Recipe Step
+#> 
+#> • step_corr()
+#> 
+#> ── Model ───────────────────────────────────────────────────────────────────────
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+### **Training and Evaluating the Model**
+
+To train the model, we split the data into training and testing sets.
+Then, we fit the workflow on the training set and evaluate its
+performance.
+
+``` r
+
+# Split data into training and testing sets
+set.seed(123) # For reproducibility
+data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
+training_data <- training(data_split) # Training set
+testing_data <- testing(data_split) # Testing set
+
+# Fit the workflow to the training data
+fitted_workflow <- resistance_workflow %>%
+  fit(training_data) # Train the model
+```
+
+**Explanation:**
+
+- `initial_split()` splits the data into training and testing sets.
+- `fit()` trains the workflow on the training set.
+
+Notice how in `fit()`, the antimicrobial selector functions are
+internally called again. For training, these functions are called since
+they are stored in the recipe.
+
+Next, we evaluate the model on the testing data.
+
+``` r
+
+# Make predictions on the testing set
+predictions <- fitted_workflow %>%
+  predict(testing_data) # Generate predictions
+probabilities <- fitted_workflow %>%
+  predict(testing_data, type = "prob") # Generate probabilities
+
+predictions <- predictions %>%
+  bind_cols(probabilities) %>%
+  bind_cols(testing_data) # Combine with true labels
+
+predictions
+#> # A tibble: 394 × 24
+#>    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB
+#>    <fct>                         <dbl>                 <dbl> <fct>   <int> <int>
+#>  1 Gram-positive              1.07e- 1             8.93 e- 1 Gram-p…     5     5
+#>  2 Gram-positive              3.17e- 8             1.000e+ 0 Gram-p…     5     1
+#>  3 Gram-negative              9.99e- 1             1.42 e- 3 Gram-n…     5     5
+#>  4 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     5     5
+#>  5 Gram-negative              9.46e- 1             5.42 e- 2 Gram-n…     5     5
+#>  6 Gram-positive              1.07e- 1             8.93 e- 1 Gram-p…     5     5
+#>  7 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     1     5
+#>  8 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     4     4
+#>  9 Gram-negative              1   e+ 0             2.22 e-16 Gram-n…     1     1
+#> 10 Gram-positive              6.05e-11             1.000e+ 0 Gram-p…     4     4
+#> # ℹ 384 more rows
+#> # ℹ 18 more variables: AMK <int>, KAN <int>, PEN <int>, OXA <int>, FLC <int>,
+#> #   AMX <int>, AMC <int>, AMP <int>, TZP <int>, CZO <int>, FEP <int>,
+#> #   CXM <int>, FOX <int>, CTX <int>, CAZ <int>, CRO <int>, IPM <int>, MEM <int>
+
+# Evaluate model performance
+metrics <- predictions %>%
+  metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics
+
+metrics
+#> # A tibble: 2 × 3
+#>   .metric  .estimator .estimate
+#>   <chr>    <chr>          <dbl>
+#> 1 accuracy binary         0.995
+#> 2 kap      binary         0.989
+
+
+# To assess some other model properties, you can make our own `metrics()` function
+our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value
+metrics2 <- predictions %>%
+  our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function
+
+metrics2
+#> # A tibble: 4 × 3
+#>   .metric  .estimator .estimate
+#>   <chr>    <chr>          <dbl>
+#> 1 accuracy binary         0.995
+#> 2 kap      binary         0.989
+#> 3 ppv      binary         0.987
+#> 4 npv      binary         1
+```
+
+**Explanation:**
+
+- [`predict()`](https://rdrr.io/r/stats/predict.html) generates
+  predictions on the testing set.
+- `metrics()` computes evaluation metrics like accuracy and kappa.
+
+It appears we can predict the Gram stain with a 99.5% accuracy based on
+AMR results of only aminoglycosides and beta-lactam antibiotics. The ROC
+curve looks like this:
+
+``` r
+
+predictions %>%
+  roc_curve(mo, `.pred_Gram-negative`) %>%
+  autoplot()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png)
+
+### **Conclusion**
+
+In this example, we demonstrated how to build a machine learning
+pipeline with the `tidymodels` framework and the `AMR` package. By
+combining selector functions like
+[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+and
+[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+with `tidymodels`, we efficiently prepared data, trained a model, and
+evaluated its performance.
+
+This workflow is extensible to other antimicrobial classes and
+resistance patterns, empowering users to analyse AMR data systematically
+and reproducibly.
+
+------------------------------------------------------------------------
+
+## Example 2: Predicting ESBL Presence Using Raw MICs
+
+In this second example, we demonstrate how to use `<mic>` columns
+directly in `tidymodels` workflows using AMR-specific recipe steps. This
+includes a transformation to `log2` scale using
+[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md),
+which prepares MIC values for use in classification models.
+
+This approach and idea formed the basis for the publication [DOI:
+10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703)
+to model the presence of extended-spectrum beta-lactamases (ESBL) based
+on MIC values.
+
+### **Objective**
+
+Our goal is to:
+
+1.  Use raw MIC values to predict whether a bacterial isolate produces
+    ESBL.
+2.  Apply AMR-aware preprocessing in a `tidymodels` recipe.
+3.  Train a classification model and evaluate its predictive
+    performance.
+
+### **Data Preparation**
+
+We use the `esbl_isolates` dataset that comes with the AMR package.
+
+``` r
+
+# Load required libraries
+library(AMR)
+library(tidymodels)
+
+# View the esbl_isolates data set
+esbl_isolates
+#> # A tibble: 500 × 19
+#>    esbl  genus   AMC   AMP   TZP   CXM   FOX   CTX   CAZ   GEN   TOB   TMP   SXT
+#>    <lgl> <chr> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic>
+#>  1 FALSE Esch…    32    32     4    64    64  8.00  8.00     1     1  16.0    20
+#>  2 FALSE Esch…    32    32     4    64    64  4.00  8.00     1     1  16.0   320
+#>  3 FALSE Esch…     4     2    64     8     4  8.00  0.12    16    16   0.5    20
+#>  4 FALSE Kleb…    32    32    16    64    64  8.00  8.00     1     1   0.5    20
+#>  5 FALSE Esch…    32    32     4     4     4  0.25  2.00     1     1  16.0   320
+#>  6 FALSE Citr…    32    32    16    64    64 64.00 32.00     1     1   0.5    20
+#>  7 FALSE Morg…    32    32     4    64    64 16.00  2.00     1     1   0.5    20
+#>  8 FALSE Prot…    16    32     4     1     4  8.00  0.12     1     1  16.0   320
+#>  9 FALSE Ente…    32    32     8    64    64 32.00  4.00     1     1   0.5    20
+#> 10 FALSE Citr…    32    32    32    64    64  8.00 64.00     1     1  16.0   320
+#> # ℹ 490 more rows
+#> # ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,
+#> #   COL <mic>
+
+# Prepare a binary outcome and convert to ordered factor
+data <- esbl_isolates %>%
+  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+```
+
+**Explanation:**
+
+- `esbl_isolates`: Contains MIC test results and ESBL status for each
+  isolate.
+- `mutate(esbl = ...)`: Converts the target column to an ordered factor
+  for classification.
+
+### **Defining the Workflow**
+
+#### 1. Preprocessing with a Recipe
+
+We use our
+[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
+function to log2-transform MIC values, ensuring that MICs are numeric
+and properly scaled. All MIC predictors can easily and agnostically
+selected using the new
+[`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md):
+
+``` r
+
+# Split into training and testing sets
+set.seed(123)
+split <- initial_split(data)
+training_data <- training(split)
+testing_data <- testing(split)
+
+# Define the recipe
+mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
+  remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
+  step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
+
+prep(mic_recipe)
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> ── Inputs
+#> Number of variables by role
+#> outcome:          1
+#> predictor:       17
+#> undeclared role:  1
+#> 
+#> ── Training information
+#> Training data contained 375 data points and no incomplete rows.
+#> 
+#> ── Operations
+#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
+```
+
+**Explanation:**
+
+- `remove_role()`: Removes irrelevant variables like genus.
+- [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md):
+  Applies `log2(as.numeric(...))` to all MIC predictors in one go.
+- `prep()`: Finalises the recipe based on training data.
+
+#### 2. Specifying the Model
+
+We use a simple logistic regression to model ESBL presence, though
+recent models such as xgboost ([link to `parsnip`
+manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html))
+could be much more precise.
+
+``` r
+
+# Define the model
+model <- logistic_reg(mode = "classification") %>%
+  set_engine("glm")
+
+model
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+**Explanation:**
+
+- `logistic_reg()`: Specifies a binary classification model.
+- `set_engine("glm")`: Uses the base R GLM engine.
+
+#### 3. Building the Workflow
+
+``` r
+
+# Create workflow
+workflow_model <- workflow() %>%
+  add_recipe(mic_recipe) %>%
+  add_model(model)
+
+workflow_model
+#> ══ Workflow ════════════════════════════════════════════════════════════════════
+#> Preprocessor: Recipe
+#> Model: logistic_reg()
+#> 
+#> ── Preprocessor ────────────────────────────────────────────────────────────────
+#> 1 Recipe Step
+#> 
+#> • step_mic_log2()
+#> 
+#> ── Model ───────────────────────────────────────────────────────────────────────
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+### **Training and Evaluating the Model**
+
+``` r
+
+# Fit the model
+fitted <- fit(workflow_model, training_data)
+
+# Generate predictions
+predictions <- predict(fitted, testing_data) %>%
+  bind_cols(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
+  bind_cols(testing_data)
+
+# Evaluate model performance
+our_metrics <- metric_set(accuracy, recall, precision, sensitivity, specificity, ppv, npv)
+metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+
+metrics
+#> # A tibble: 7 × 3
+#>   .metric     .estimator .estimate
+#>   <chr>       <chr>          <dbl>
+#> 1 accuracy    binary         0.92 
+#> 2 recall      binary         0.921
+#> 3 precision   binary         0.921
+#> 4 sensitivity binary         0.921
+#> 5 specificity binary         0.919
+#> 6 ppv         binary         0.921
+#> 7 npv         binary         0.919
+```
+
+**Explanation:**
+
+- `fit()`: Trains the model on the processed training data.
+- [`predict()`](https://rdrr.io/r/stats/predict.html): Produces
+  predictions for unseen test data.
+- `metric_set()`: Allows evaluating multiple classification metrics.
+  This will make `our_metrics` to become a function that we can use to
+  check the predictions with.
+
+It appears we can predict ESBL gene presence with a positive predictive
+value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
+using a simplistic logistic regression model.
+
+### **Visualising Predictions**
+
+We can visualise predictions by comparing predicted and actual ESBL
+status.
+
+``` r
+
+library(ggplot2)
+
+ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
+  geom_bar(position = "stack") +
+  labs(
+    title = "Predicted vs Actual ESBL Status",
+    x = "Actual ESBL",
+    y = "Count"
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png)
+
+And plot the certainties too - how certain were the actual predictions?
+
+``` r
+
+predictions %>%
+  mutate(
+    certainty = ifelse(.pred_class == "FALSE",
+      .pred_FALSE,
+      .pred_TRUE
+    ),
+    correct = ifelse(esbl == .pred_class, "Right", "Wrong")
+  ) %>%
+  ggplot(aes(
+    x = seq_len(nrow(predictions)),
+    y = certainty,
+    colour = correct
+  )) +
+  scale_colour_manual(
+    values = c(Right = "green3", Wrong = "red2"),
+    name = "Correct?"
+  ) +
+  geom_point() +
+  scale_y_continuous(
+    labels = function(x) paste0(x * 100, "%"),
+    limits = c(0.5, 1)
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png)
+
+### **Conclusion**
+
+In this example, we showcased how the new `AMR`-specific recipe steps
+simplify working with `<mic>` columns in `tidymodels`. The
+[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
+transformation converts MICs (with or without operators) to
+log2-transformed numerics, improving compatibility with classification
+models.
+
+This pipeline enables realistic, reproducible, and interpretable
+modelling of antimicrobial resistance data.
+
+------------------------------------------------------------------------
+
+## Example 3: Predicting AMR Over Time
+
+In this third example, we aim to predict antimicrobial resistance (AMR)
+trends over time using `tidymodels`. We will model resistance to three
+antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and
+ciprofloxacin `CIP`), based on historical data grouped by year and
+hospital ward.
+
+### **Objective**
+
+Our goal is to:
+
+1.  Prepare the dataset by aggregating resistance data over time.
+2.  Define a regression model to predict AMR trends.
+3.  Use `tidymodels` to preprocess, train, and evaluate the model.
+
+### **Data Preparation**
+
+We start by transforming the `example_isolates` dataset into a
+structured time-series format.
+
+``` r
+
+# Load required libraries
+library(AMR)
+library(tidymodels)
+
+# Transform dataset
+data_time <- example_isolates %>%
+  top_n_microorganisms(n = 10) %>% # Filter on the top #10 species
+  mutate(
+    year = as.integer(format(date, "%Y")), # Extract year from date
+    gramstain = mo_gramstain(mo)
+  ) %>% # Get taxonomic names
+  group_by(year, gramstain) %>%
+  summarise(
+    across(c(AMX, AMC, CIP),
+      function(x) resistance(x, minimum = 0),
+      .names = "res_{.col}"
+    ),
+    .groups = "drop"
+  ) %>%
+  filter(!is.na(res_AMX) & !is.na(res_AMC) & !is.na(res_CIP)) # Drop missing values
+#> ℹ Using column mo as input for `col_mo`.
+#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
+#>   category susceptible. Set the `guideline` argument or the `AMR_guideline`
+#>   option to either "CLSI" or "EUCAST", see `?AMR-options`.
+#> ℹ This message will be shown once per session.
+
+data_time
+#> # A tibble: 32 × 5
+#>     year gramstain     res_AMX res_AMC res_CIP
+#>    <int> <chr>           <dbl>   <dbl>   <dbl>
+#>  1  2002 Gram-negative   1      0.105   0.0606
+#>  2  2002 Gram-positive   0.838  0.182   0.162 
+#>  3  2003 Gram-negative   1      0.0714  0     
+#>  4  2003 Gram-positive   0.714  0.244   0.154 
+#>  5  2004 Gram-negative   0.464  0.0938  0     
+#>  6  2004 Gram-positive   0.849  0.299   0.244 
+#>  7  2005 Gram-negative   0.412  0.132   0.0588
+#>  8  2005 Gram-positive   0.882  0.382   0.154 
+#>  9  2006 Gram-negative   0.379  0       0.1   
+#> 10  2006 Gram-positive   0.778  0.333   0.353 
+#> # ℹ 22 more rows
+```
+
+**Explanation:**
+
+- `mo_name(mo)`: Converts microbial codes into proper species names.
+- [`resistance()`](https://amr-for-r.org/reference/proportion.md):
+  Converts AMR results into numeric values (proportion of resistant
+  isolates).
+- `group_by(year, ward, species)`: Aggregates resistance rates by year
+  and ward.
+
+### **Defining the Workflow**
+
+We now define the modelling workflow, which consists of a preprocessing
+step, a model specification, and the fitting process.
+
+#### 1. Preprocessing with a Recipe
+
+``` r
+
+# Define the recipe
+resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>%
+  step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical
+  step_normalize(year) %>% # Normalise year for better model performance
+  step_nzv(all_predictors()) # Remove near-zero variance predictors
+
+resistance_recipe_time
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> ── Inputs
+#> Number of variables by role
+#> outcome:   1
+#> predictor: 2
+#> 
+#> ── Operations
+#> • Dummy variables from: gramstain
+#> • Centering and scaling for: year
+#> • Sparse, unbalanced variable filter on: all_predictors()
+```
+
+**Explanation:**
+
+- `step_dummy()`: Encodes categorical variables (`ward`, `species`) as
+  numerical indicators.
+- `step_normalize()`: Normalises the `year` variable.
+- `step_nzv()`: Removes near-zero variance predictors.
+
+#### 2. Specifying the Model
+
+We use a linear regression model to predict resistance trends.
+
+``` r
+
+# Define the linear regression model
+lm_model <- linear_reg() %>%
+  set_engine("lm") # Use linear regression
+
+lm_model
+#> Linear Regression Model Specification (regression)
+#> 
+#> Computational engine: lm
+```
+
+**Explanation:**
+
+- `linear_reg()`: Defines a linear regression model.
+- `set_engine("lm")`: Uses R’s built-in linear regression engine.
+
+#### 3. Building the Workflow
+
+We combine the preprocessing recipe and model into a workflow.
+
+``` r
+
+# Create workflow
+resistance_workflow_time <- workflow() %>%
+  add_recipe(resistance_recipe_time) %>%
+  add_model(lm_model)
+
+resistance_workflow_time
+#> ══ Workflow ════════════════════════════════════════════════════════════════════
+#> Preprocessor: Recipe
+#> Model: linear_reg()
+#> 
+#> ── Preprocessor ────────────────────────────────────────────────────────────────
+#> 3 Recipe Steps
+#> 
+#> • step_dummy()
+#> • step_normalize()
+#> • step_nzv()
+#> 
+#> ── Model ───────────────────────────────────────────────────────────────────────
+#> Linear Regression Model Specification (regression)
+#> 
+#> Computational engine: lm
+```
+
+### **Training and Evaluating the Model**
+
+We split the data into training and testing sets, fit the model, and
+evaluate performance.
+
+``` r
+
+# Split the data
+set.seed(123)
+data_split_time <- initial_split(data_time, prop = 0.8)
+train_time <- training(data_split_time)
+test_time <- testing(data_split_time)
+
+# Train the model
+fitted_workflow_time <- resistance_workflow_time %>%
+  fit(train_time)
+
+# Make predictions
+predictions_time <- fitted_workflow_time %>%
+  predict(test_time) %>%
+  bind_cols(test_time)
+
+# Evaluate model
+metrics_time <- predictions_time %>%
+  metrics(truth = res_AMX, estimate = .pred)
+
+metrics_time
+#> # A tibble: 3 × 3
+#>   .metric .estimator .estimate
+#>   <chr>   <chr>          <dbl>
+#> 1 rmse    standard      0.0774
+#> 2 rsq     standard      0.711 
+#> 3 mae     standard      0.0704
+```
+
+**Explanation:**
+
+- `initial_split()`: Splits data into training and testing sets.
+- `fit()`: Trains the workflow.
+- [`predict()`](https://rdrr.io/r/stats/predict.html): Generates
+  resistance predictions.
+- `metrics()`: Evaluates model performance.
+
+### **Visualising Predictions**
+
+We plot resistance trends over time for amoxicillin.
+
+``` r
+
+library(ggplot2)
+
+# Plot actual vs predicted resistance over time
+ggplot(predictions_time, aes(x = year)) +
+  geom_point(aes(y = res_AMX, color = "Actual")) +
+  geom_line(aes(y = .pred, color = "Predicted")) +
+  labs(
+    title = "Predicted vs Actual AMX Resistance Over Time",
+    x = "Year",
+    y = "Resistance Proportion"
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png)
+
+Additionally, we can visualise resistance trends in `ggplot2` and
+directly add linear models there:
+
+``` r
+
+ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
+  geom_line() +
+  labs(
+    title = "AMX Resistance Trends",
+    x = "Year",
+    y = "Resistance Proportion"
+  ) +
+  # add a linear model directly in ggplot2:
+  geom_smooth(
+    method = "lm",
+    formula = y ~ x,
+    alpha = 0.25
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png)
+
+### **Conclusion**
+
+In this example, we demonstrated how to analyze AMR trends over time
+using `tidymodels`. By aggregating resistance rates by year and hospital
+ward, we built a predictive model to track changes in resistance to
+amoxicillin (`AMX`), amoxicillin-clavulanic acid (`AMC`), and
+ciprofloxacin (`CIP`).
+
+This method can be extended to other antibiotics and resistance
+patterns, providing valuable insights into AMR dynamics in healthcare
+settings.
--- a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png
+++ b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png
--- a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png
+++ b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png
--- a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-20-1.png
+++ b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-20-1.png
--- a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png
+++ b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png
--- a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png
+++ b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -12,8 +12,8 @@
 <link rel="icon" sizes="any" href="../favicon.ico">
 <link rel="manifest" href="../site.webmanifest">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.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/Lato-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
@@ -30,7 +30,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
@@ -118,7 +118,7 @@ resistant to ampicillin.</p>
 <em>Klebsiella</em> being susceptible to ampicillin. This could be
 because an antibiogram is available before an identification is
 available, and the antibiogram is then not re-interpreted based on the
-identification. The <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function resolves this,
+identification. The <code><a href="../reference/interpretive_rules.html">eucast_rules()</a></code> function resolves this,
 by applying the latest ‘EUCAST Expected Resistant Phenotypes’
 guideline:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
@@ -136,11 +136,11 @@ guideline:</p>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #5FD7AF;">  S  </span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span>
 <span></span>
-<span><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
 <span><span class="co">#&gt;   mo                    ampicillin</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FFAFAF;">  R  </span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FF5F5F;">  R  </span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span></code></pre></div>
 <p>A more convenient function is
 <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
@@ -177,7 +177,7 @@ reading</em>, and is basically a form of imputation:</p>
 <span>  CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span></span>
 <span>  CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span></span>
 <span>  PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span></span>
-<span>  FOX <span class="op">=</span> <span class="st">"S"</span>  <span class="co"># Cefoxitin</span></span>
+<span>  FOX <span class="op">=</span> <span class="st">"S"</span> <span class="co"># Cefoxitin</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span></span></code></pre></div>
@@ -246,7 +246,7 @@ reading</em>, and is basically a form of imputation:</p>
 </tbody>
 </table>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">mo</th>
--- a/articles/EUCAST.md
+++ b/articles/EUCAST.md
@@ -0,0 +1,131 @@
+# Apply EUCAST rules
+
+## Introduction
+
+What are EUCAST rules? The European Committee on Antimicrobial
+Susceptibility Testing (EUCAST) states [on their
+website](https://www.eucast.org/expert_rules_and_expected_phenotypes):
+
+> *EUCAST expert rules (see below) are a tabulated collection of expert
+> knowledge on interpretive rules, expected resistant phenotypes and
+> expected susceptible phenotypes which should be applied to
+> antimicrobial susceptibility testing in order to reduce testing,
+> reduce errors and make appropriate recommendations for reporting
+> particular resistances.*
+
+In Europe, a lot of medical microbiological laboratories already apply
+these rules ([Brown *et al.*,
+2015](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008)).
+Our package features their latest insights on expected resistant
+phenotypes (v1.2, 2023).
+
+## Examples
+
+These rules can be used to discard improbable bug-drug combinations in
+your data. For example, *Klebsiella* produces beta-lactamase that
+prevents ampicillin (or amoxicillin) from working against it. In other
+words, practically every strain of *Klebsiella* is resistant to
+ampicillin.
+
+Sometimes, laboratory data can still contain such strains with
+*Klebsiella* being susceptible to ampicillin. This could be because an
+antibiogram is available before an identification is available, and the
+antibiogram is then not re-interpreted based on the identification. The
+[`eucast_rules()`](https://amr-for-r.org/reference/interpretive_rules.md)
+function resolves this, by applying the latest ‘EUCAST Expected
+Resistant Phenotypes’ guideline:
+
+``` r
+
+oops <- tibble::tibble(
+  mo = c(
+    "Klebsiella pneumoniae",
+    "Escherichia coli"
+  ),
+  ampicillin = as.sir("S")
+)
+oops
+#> # A tibble: 2 × 2
+#>   mo                    ampicillin
+#>   <chr>                 <sir>     
+#> 1 Klebsiella pneumoniae   S       
+#> 2 Escherichia coli        S  
+
+eucast_rules(oops, info = FALSE, overwrite = TRUE)
+#> # A tibble: 2 × 2
+#>   mo                    ampicillin
+#>   <chr>                 <sir>     
+#> 1 Klebsiella pneumoniae   R       
+#> 2 Escherichia coli        S  
+```
+
+A more convenient function is
+[`mo_is_intrinsic_resistant()`](https://amr-for-r.org/reference/mo_property.md)
+that uses the same guideline, but allows to check for one or more
+specific microorganisms or antimicrobials:
+
+``` r
+
+mo_is_intrinsic_resistant(
+  c("Klebsiella pneumoniae", "Escherichia coli"),
+  "ampicillin"
+)
+#> [1]  TRUE FALSE
+
+mo_is_intrinsic_resistant(
+  "Klebsiella pneumoniae",
+  c("ampicillin", "kanamycin")
+)
+#> [1]  TRUE FALSE
+```
+
+EUCAST rules can not only be used for correction, they can also be used
+for filling in known resistance and susceptibility based on results of
+other antimicrobials drugs. This process is called *interpretive
+reading*, and is basically a form of imputation:
+
+``` r
+
+data <- tibble::tibble(
+  mo = c(
+    "Staphylococcus aureus",
+    "Enterococcus faecalis",
+    "Escherichia coli",
+    "Klebsiella pneumoniae",
+    "Pseudomonas aeruginosa"
+  ),
+  VAN = "-", # Vancomycin
+  AMX = "-", # Amoxicillin
+  COL = "-", # Colistin
+  CAZ = "-", # Ceftazidime
+  CXM = "-", # Cefuroxime
+  PEN = "S", # Benzylenicillin
+  FOX = "S" # Cefoxitin
+)
+```
+
+``` r
+
+data
+```
+
+| mo                     | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
+|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| Staphylococcus aureus  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Enterococcus faecalis  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Escherichia coli       | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Klebsiella pneumoniae  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Pseudomonas aeruginosa | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+
+``` r
+
+eucast_rules(data, overwrite = TRUE)
+```
+
+| mo                     | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
+|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| Staphylococcus aureus  | \-  |  S  |  R  |  R  |  S  |  S  |  S  |
+| Enterococcus faecalis  | \-  | \-  |  R  |  R  |  R  |  S  |  R  |
+| Escherichia coli       |  R  | \-  | \-  | \-  | \-  |  R  |  S  |
+| Klebsiella pneumoniae  |  R  |  R  | \-  | \-  | \-  |  R  |  S  |
+| Pseudomonas aeruginosa |  R  |  R  | \-  | \-  |  R  |  R  |  R  |
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -12,8 +12,8 @@
 <link rel="icon" sizes="any" href="../favicon.ico">
 <link rel="manifest" href="../site.webmanifest">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.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/Lato-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
@@ -30,7 +30,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
@@ -163,6 +163,10 @@ per taxonomic order and genus:</p>
 <span>    <span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,</span>
 <span>    <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span></span>
 <span>  <span class="op">)</span> <span class="co"># and select only relevant columns</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> `resistance()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
+<span><span class="co">#&gt;   category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
+<span><span class="co">#&gt;   option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span>
 <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">resistance_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 6 × 10</span></span></span>
@@ -184,8 +188,10 @@ that contain numeric values in all selected variables, so we now only
 need to do:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
-<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ Columns selected for PCA: "</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">SXT</span><span style="color: #0000BB;">",</span></span></span>
-<span><span class="co"><span style="color: #0000BB;">#&gt;   "</span><span style="color: #0000BB; font-weight: bold;">TMP</span><span style="color: #0000BB;">", and "</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">". Total observations available: 7.</span></span></span></code></pre></div>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Columns selected for PCA: <span style="color: #0000BB;">"\033[1mAMC\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCAZ\033[22m"</span>,</span></span>
+<span><span class="co">#&gt;   <span style="color: #0000BB;">"\033[1mCTX\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCXM\033[22m"</span>, <span style="color: #0000BB;">"\033[1mGEN\033[22m"</span>,</span></span>
+<span><span class="co">#&gt;   <span style="color: #0000BB;">"\033[1mSXT\033[22m"</span>, <span style="color: #0000BB;">"\033[1mTMP\033[22m"</span>, and <span style="color: #0000BB;">"\033[1mTOB\033[22m"</span>. Total</span></span>
+<span><span class="co">#&gt;   observations available: 7.</span></span></code></pre></div>
 <p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
 function:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
@@ -210,18 +216,18 @@ per drug explain the difference per microorganism.</p>
 </h2>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
-<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
+<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" alt="" width="750"></p>
 <p>But we can’t see the explanation of the points. Perhaps this works
 better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
 automatically adds the right labels and even groups:</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
-<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
+<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="750"></p>
 <p>You can also print an ellipse per group, and edit the appearance:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
+<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="750"></p>
 </div>
  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
    </nav></aside>
--- a/articles/PCA.md
+++ b/articles/PCA.md
@@ -0,0 +1,170 @@
+# Conduct principal component analysis (PCA) for AMR
+
+**NOTE: This page will be updated soon, as the pca() function is
+currently being developed.**
+
+## Introduction
+
+## Transforming
+
+For PCA, we need to transform our AMR data first. This is what the
+`example_isolates` data set in this package looks like:
+
+``` r
+
+library(AMR)
+library(dplyr)
+glimpse(example_isolates)
+#> Rows: 2,000
+#> Columns: 46
+#> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2…
+#> $ patient <chr> "A77334", "A77334", "067927", "067927", "067927", "067927", "4…
+#> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50…
+#> $ gender  <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M…
+#> $ ward    <chr> "Clinical", "Clinical", "ICU", "ICU", "ICU", "ICU", "Clinical"…
+#> $ mo      <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPHY_EPDR",…
+#> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,…
+#> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R…
+#> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
+#> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N…
+#> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
+#> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
+#> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S…
+#> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
+#> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
+#> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, …
+#> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
+#> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA…
+#> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, …
+#> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N…
+#> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,…
+#> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
+#> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S…
+#> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, …
+#> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
+#> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, …
+#> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
+#> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
+#> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
+#> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA…
+#> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
+#> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
+#> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, …
+#> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
+```
+
+Now to transform this to a data set with only resistance percentages per
+taxonomic order and genus:
+
+``` r
+
+resistance_data <- example_isolates %>%
+  group_by(
+    order = mo_order(mo), # group on anything, like order
+    genus = mo_genus(mo)
+  ) %>% #  and genus as we do here
+  summarise_if(is.sir, resistance) %>% # then get resistance of all drugs
+  select(
+    order, genus, AMC, CXM, CTX,
+    CAZ, GEN, TOB, TMP, SXT
+  ) # and select only relevant columns
+#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
+#>   category susceptible. Set the `guideline` argument or the `AMR_guideline`
+#>   option to either "CLSI" or "EUCAST", see `?AMR-options`.
+#> ℹ This message will be shown once per session.
+
+head(resistance_data)
+#> # A tibble: 6 × 10
+#> # Groups:   order [5]
+#>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT
+#>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA
+#> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA
+#> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA
+#> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA
+#> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA
+#> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA
+```
+
+## Perform principal component analysis
+
+The new [`pca()`](https://amr-for-r.org/reference/pca.md) function will
+automatically filter on rows that contain numeric values in all selected
+variables, so we now only need to do:
+
+``` r
+
+pca_result <- pca(resistance_data)
+#> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m",
+#>   "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m",
+#>   "\033[1mSXT\033[22m", "\033[1mTMP\033[22m", and "\033[1mTOB\033[22m". Total
+#>   observations available: 7.
+```
+
+The result can be reviewed with the good old
+[`summary()`](https://rdrr.io/r/base/summary.html) function:
+
+``` r
+
+summary(pca_result)
+#> Groups (n=4, named as 'order'):
+#> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
+#> Importance of components:
+#>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7
+#> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
+#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
+#> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
+```
+
+    #> Groups (n=4, named as 'order'):
+    #> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
+
+Good news. The first two components explain a total of 93.3% of the
+variance (see the PC1 and PC2 values of the *Proportion of Variance*. We
+can create a so-called biplot with the base R
+[`biplot()`](https://rdrr.io/r/stats/biplot.html) function, to see which
+antimicrobial resistance per drug explain the difference per
+microorganism.
+
+## Plotting the results
+
+``` r
+
+biplot(pca_result)
+```
+
+![](PCA_files/figure-html/unnamed-chunk-5-1.png)
+
+But we can’t see the explanation of the points. Perhaps this works
+better with our new
+[`ggplot_pca()`](https://amr-for-r.org/reference/ggplot_pca.md)
+function, that automatically adds the right labels and even groups:
+
+``` r
+
+ggplot_pca(pca_result)
+```
+
+![](PCA_files/figure-html/unnamed-chunk-6-1.png)
+
+You can also print an ellipse per group, and edit the appearance:
+
+``` r
+
+ggplot_pca(pca_result, ellipse = TRUE) +
+  ggplot2::labs(title = "An AMR/PCA biplot!")
+```
+
+![](PCA_files/figure-html/unnamed-chunk-7-1.png)
--- a/articles/PCA_files/figure-html/unnamed-chunk-6-1.png
+++ b/articles/PCA_files/figure-html/unnamed-chunk-6-1.png
--- a/articles/PCA_files/figure-html/unnamed-chunk-7-1.png
+++ b/articles/PCA_files/figure-html/unnamed-chunk-7-1.png
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -12,8 +12,8 @@
 <link rel="icon" sizes="any" href="../favicon.ico">
 <link rel="manifest" href="../site.webmanifest">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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-<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
@@ -30,7 +30,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
@@ -254,15 +254,20 @@ Longest: 40</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
 <span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
-<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> `susceptibility()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
+<span><span class="co">#&gt;   category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
+<span><span class="co">#&gt;   option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: factor &gt; ordered &gt; sir (numeric)<br>
 Length: 500<br>
-Levels: 5: S &lt; SDD &lt; I &lt; R &lt; NI<br>
+Levels: 8: S &lt; SDD &lt; I &lt; R &lt; NI &lt; WT &lt; NWT &lt;
+NS<br>
 Available: 481 (96.2%, NA: 19 = 3.8%)<br>
 Unique: 3</p>
 <p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)<br>
-Drug group: Beta-lactams/penicillins<br>
+Drug group: Aminopenicillins<br>
 %SI: 78.59%</p>
 <table class="table">
 <thead><tr class="header">
@@ -311,7 +316,7 @@ using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">"ab"</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
+<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
 </div>
  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
    </nav></aside>
--- a/articles/WHONET.md
+++ b/articles/WHONET.md
@@ -0,0 +1,147 @@
+# Work with WHONET data
+
+### Import of data
+
+This tutorial assumes you already imported the WHONET data with e.g. the
+[`readxl` package](https://readxl.tidyverse.org/). In RStudio, this can
+be done using the menu button ‘Import Dataset’ in the tab ‘Environment’.
+Choose the option ‘From Excel’ and select your exported file. Make sure
+date fields are imported correctly.
+
+An example syntax could look like this:
+
+``` r
+
+library(readxl)
+data <- read_excel(path = "path/to/your/file.xlsx")
+```
+
+This package comes with an [example data set
+`WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it
+for this analysis.
+
+### Preparation
+
+First, load the relevant packages if you did not yet did this. I use the
+tidyverse for all of my analyses. All of them. If you don’t know it yet,
+I suggest you read about it on their website:
+<https://www.tidyverse.org/>.
+
+``` r
+
+library(dplyr) # part of tidyverse
+library(ggplot2) # part of tidyverse
+library(AMR) # this package
+library(cleaner) # to create frequency tables
+```
+
+We will have to transform some variables to simplify and automate the
+analysis:
+
+- Microorganisms should be transformed to our own microorganism codes
+  (called an `mo`) using [our Catalogue of Life reference data
+  set](https://amr-for-r.org/reference/catalogue_of_life), which
+  contains all ~70,000 microorganisms from the taxonomic kingdoms
+  Bacteria, Fungi and Protozoa. We do the tranformation with
+  [`as.mo()`](https://amr-for-r.org/reference/as.mo.md). This function
+  also recognises almost all WHONET abbreviations of microorganisms.
+- Antimicrobial results or interpretations have to be clean and valid.
+  In other words, they should only contain values `"S"`, `"I"` or `"R"`.
+  That is exactly where the
+  [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) function is
+  for.
+
+``` r
+
+# transform variables
+data <- WHONET %>%
+  # get microbial ID based on given organism
+  mutate(mo = as.mo(Organism)) %>%
+  # transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class
+  mutate_at(vars(AMP_ND10:CIP_EE), as.sir)
+```
+
+No errors or warnings, so all values are transformed succesfully.
+
+We also created a package dedicated to data cleaning and checking,
+called the `cleaner` package. Its
+[`freq()`](https://msberends.github.io/cleaner/reference/freq.html)
+function can be used to create frequency tables.
+
+So let’s check our data, with a couple of frequency tables:
+
+``` r
+
+# our newly created `mo` variable, put in the mo_name() function
+data %>% freq(mo_name(mo), nmax = 10)
+```
+
+**Frequency table**
+
+Class: character  
+Length: 500  
+Available: 500 (100%, NA: 0 = 0%)  
+Unique: 38
+
+Shortest: 11  
+Longest: 40
+
+|  | Item | Count | Percent | Cum. Count | Cum. Percent |
+|:---|:---|---:|---:|---:|---:|
+| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
+| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
+| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
+| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
+| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
+| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
+| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
+| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
+| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
+| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
+
+(omitted 28 entries, n = 57 \[11.4%\])
+
+``` r
+
+# our transformed antibiotic columns
+# amoxicillin/clavulanic acid (J01CR02) as an example
+data %>% freq(AMC_ND2)
+#> ℹ `susceptibility()` assumes the EUCAST guideline and thus considers the 'I'
+#>   category susceptible. Set the `guideline` argument or the `AMR_guideline`
+#>   option to either "CLSI" or "EUCAST", see `?AMR-options`.
+#> ℹ This message will be shown once per session.
+```
+
+**Frequency table**
+
+Class: factor \> ordered \> sir (numeric)  
+Length: 500  
+Levels: 8: S \< SDD \< I \< R \< NI \< WT \< NWT \< NS  
+Available: 481 (96.2%, NA: 19 = 3.8%)  
+Unique: 3
+
+Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)  
+Drug group: Aminopenicillins  
+%SI: 78.59%
+
+|     | Item | Count | Percent | Cum. Count | Cum. Percent |
+|:----|:-----|------:|--------:|-----------:|-------------:|
+| 1   | S    |   356 |  74.01% |        356 |       74.01% |
+| 2   | R    |   103 |  21.41% |        459 |       95.43% |
+| 3   | I    |    22 |   4.57% |        481 |      100.00% |
+
+### A first glimpse at results
+
+An easy `ggplot` will already give a lot of information, using the
+included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
+function:
+
+``` r
+
+data %>%
+  group_by(Country) %>%
+  select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
+  ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE)
+```
+
+![](WHONET_files/figure-html/unnamed-chunk-7-1.png)
--- a/articles/WHONET_files/figure-html/unnamed-chunk-7-1.png
+++ b/articles/WHONET_files/figure-html/unnamed-chunk-7-1.png
--- a/articles/WISCA.html
+++ b/articles/WISCA.html
@@ -12,8 +12,8 @@
 <link rel="icon" sizes="any" href="../favicon.ico">
 <link rel="manifest" href="../site.webmanifest">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.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/Lato-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
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 <link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
@@ -30,7 +30,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
@@ -87,89 +87,98 @@

    
    
-<blockquote>
-<p>This explainer was largely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
-manually-trained model able to answer any question about the
-<code>AMR</code> package.</p>
-</blockquote>
 <div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+<h2 id="why-wisca">Why WISCA?<a class="anchor" aria-label="anchor" href="#why-wisca"></a>
 </h2>
-<p>Clinical guidelines for empirical antimicrobial therapy require
-<em>probabilistic reasoning</em>: what is the chance that a regimen will
-cover the likely infecting organisms, before culture results are
-available?</p>
-<p>This is the purpose of <strong>WISCA</strong>, or
-<strong>Weighted-Incidence Syndromic Combination
-Antibiogram</strong>.</p>
-<p>WISCA is a Bayesian approach that integrates:</p>
+<p>When a clinician starts empirical antimicrobial therapy, the
+causative pathogen is unknown. The question they need answered is not
+<em>“what proportion of</em> E. coli <em>is susceptible to
+ciprofloxacin?“</em> but rather <em>“what is the probability that this
+regimen will adequately cover whatever pathogen turns out to be causing
+my patient’s infection?”</em></p>
+<p>The traditional cumulative antibiogram, as standardised by CLSI M39,
+cannot answer that question. It presents susceptibility percentages per
+species per antibiotic, but:</p>
 <ul>
 <li>
-<strong>Pathogen prevalence</strong> (how often each species causes
-the syndrome),</li>
+<strong>It fragments information by organism.</strong> The clinician
+must mentally combine susceptibility rates across multiple species,
+weighting by how often each species causes the syndrome, a calculation
+nobody does at the bedside.</li>
 <li>
-<strong>Regimen susceptibility</strong> (how often a regimen works
-<em>if</em> the pathogen is known),</li>
+<strong>It ignores pathogen incidence.</strong> A species that
+causes 2% of infections is given the same visual weight as one that
+causes 60%.</li>
+<li>
+<strong>It does not evaluate combination regimens.</strong> Much
+empirical therapy consists of two or more agents, but the traditional
+antibiogram only shows monotherapy per organism.</li>
+<li>
+<strong>It provides no measure of uncertainty.</strong> A reported
+“90% susceptible” based on 50 isolates has a 95% confidence interval of
+roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
+point estimate without context.</li>
 </ul>
-<p>to estimate the <strong>overall empirical coverage</strong> of
-antimicrobial regimens, with quantified uncertainty.</p>
-<p>This vignette explains how WISCA works, why it is useful, and how to
-apply it using the <code>AMR</code> package.</p>
+<p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
+Antibiogram) resolves all four limitations. It estimates the probability
+that a regimen will provide adequate empirical coverage for a given
+infection syndrome, weighted by local pathogen incidence, with full
+uncertainty quantification via Bayesian inference.</p>
+<p>The concept was introduced by Hebert <em>et al.</em> (2012), who
+demonstrated that traditional antibiogram susceptibility rates could be
+misleading: ciprofloxacin appeared 84% effective against <em>E.
+coli</em> in the traditional antibiogram, but WISCA revealed only 62%
+coverage for UTI and 37% for abdominal infections, because enterococci
+(intrinsically resistant) and other species contribute substantially to
+these syndromes. Randhawa <em>et al.</em> (2014) showed that
+WISCA-guided regimen selection could improve time-to-adequate-coverage
+on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
+Bayesian framework now used in this package, enabling credible intervals
+and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
+globally across 52 hospitals in 23 countries.</p>
 </div>
 <div class="section level2">
-<h2 id="why-traditional-antibiograms-fall-short">Why traditional antibiograms fall short<a class="anchor" aria-label="anchor" href="#why-traditional-antibiograms-fall-short"></a>
-</h2>
-<p>A standard antibiogram gives you:</p>
-<pre><code>Species → Antibiotic → Susceptibility %</code></pre>
-<p>But clinicians don’t know the species <em>a priori</em>. They need to
-choose a regimen that covers the <strong>likely pathogens</strong>,
-without knowing which one is present.</p>
-<p>Traditional antibiograms calculate the susceptibility % as just the
-number of resistant isolates divided by the total number of tested
-isolates. Therefore, traditional antibiograms:</p>
-<ul>
-<li>Fragment information by organism,</li>
-<li>Do not weight by real-world prevalence,</li>
-<li>Do not account for combination therapy or sample size,</li>
-<li>Do not provide uncertainty.</li>
-</ul>
-</div>
-<div class="section level2">
-<h2 id="the-idea-of-wisca">The idea of WISCA<a class="anchor" aria-label="anchor" href="#the-idea-of-wisca"></a>
+<h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
 </h2>
 <p>WISCA asks:</p>
 <blockquote>
 <p>“What is the <strong>probability</strong> that this regimen
 <strong>will cover</strong> the pathogen, given the syndrome?”</p>
 </blockquote>
-<p>This means combining two things:</p>
+<p>This means combining two quantities:</p>
 <ul>
 <li>
-<strong>Incidence</strong> of each pathogen in the syndrome,</li>
+<strong>Pathogen incidence</strong> in the syndrome (how often each
+species causes it),</li>
 <li>
 <strong>Susceptibility</strong> of each pathogen to the
 regimen.</li>
 </ul>
 <p>We can write this as:</p>
-<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mrow><mo stretchy="true" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
-<p>For example, suppose:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
+<p>For example, suppose in your hospital:</p>
 <ul>
 <li>
-<em>E. coli</em> causes 60% of cases, and 90% of <em>E. coli</em>
-are susceptible to a drug.</li>
+<em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
+susceptible to a drug.</li>
 <li>
-<em>Klebsiella</em> causes 40% of cases, and 70% of
+<em>Klebsiella</em> causes 40% of UTIs, and 70% of
 <em>Klebsiella</em> are susceptible.</li>
 </ul>
 <p>Then:</p>
-<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
-<p>But in real data, incidence and susceptibility are <strong>estimated
-from samples</strong>, so they carry uncertainty. WISCA models this
-<strong>probabilistically</strong>, using conjugate Bayesian
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
+<p>That 82% is a far more clinically meaningful number than the
+species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
+reported separately in a traditional antibiogram, because it directly
+answers the question the clinician actually faces.</p>
+<p>But in real data, both incidence and susceptibility are
+<strong>estimated from finite samples</strong>, so they carry
+uncertainty. A sample of 50 isolates is not a census. WISCA models this
+uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
 distributions.</p>
 </div>
 <div class="section level2">
-<h2 id="the-bayesian-engine-behind-wisca">The Bayesian engine behind WISCA<a class="anchor" aria-label="anchor" href="#the-bayesian-engine-behind-wisca"></a>
+<h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
 </h2>
 <div class="section level3">
 <h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
@@ -180,27 +189,38 @@ distributions.</p>
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
 be the number of pathogens,</li>
 <li>
-<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\alpha = (1, 1, \ldots, 1)</annotation></semantics></math>
-be a <strong>Dirichlet</strong> prior (uniform),</li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝛂</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{\alpha} = (1, 1, \ldots, 1)</annotation></semantics></math>
+be a
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="normal">Dirichlet</mtext><annotation encoding="application/x-tex">\text{Dirichlet}</annotation></semantics></math>
+prior (uniform, non-informative),</li>
 <li>
-<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">n = (n_1, \ldots, n_K)</annotation></semantics></math>
-be the observed counts per species.</li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐧</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{n} = (n_1, \ldots, n_K)</annotation></semantics></math>
+be the observed isolate counts per species.</li>
 </ul>
 <p>Then the posterior incidence is:</p>
-<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
 <p>To simulate from this, we use:</p>
-<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
+<p>The Dirichlet is the conjugate prior for multinomial data. With the
+non-informative prior
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Dirichlet}(1, 1, \ldots, 1)</annotation></semantics></math>,
+the posterior is dominated by the data once sample sizes are reasonable.
+With small samples, the posterior is appropriately more diffuse,
+reflecting genuine uncertainty, and the resulting credible intervals
+will be wider.</p>
 </div>
 <div class="section level3">
 <h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
 </h3>
-<p>Each pathogen–regimen pair has a prior and data:</p>
+<p>Each pathogen-regimen pair has a prior and observed data:</p>
 <ul>
-<li>Prior:
-<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>,</mo><msub><mi>β</mi><mn>0</mn></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Beta}(\alpha_0, \beta_0)</annotation></semantics></math>,
-with default
-<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>α</mi><mn>0</mn></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">\alpha_0 = \beta_0 = 1</annotation></semantics></math>
-</li>
+<li>Default prior:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(0.5, 0.5)</annotation></semantics></math>
+(Jeffreys prior)</li>
+<li>Intrinsically resistant pairs:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>9999</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(1, 9999)</annotation></semantics></math>,
+forcing near-zero susceptibility regardless of observed data (based on
+EUCAST Expected Resistant Phenotypes)</li>
 <li>Data:
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
 susceptible out of
@@ -213,7 +233,7 @@ category could also include values SDD (susceptible, dose-dependent) and
 I (intermediate [CLSI], or susceptible, increased exposure
 [EUCAST]).</p>
 <p>Then the posterior is:</p>
-<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
 </div>
 <div class="section level3">
 <h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
@@ -224,27 +244,76 @@ I (intermediate [CLSI], or susceptible, increased exposure
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
 </li>
 <li>Simulate susceptibility:
-<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo>+</mo><msub><mi>R</mi><mi>i</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)</annotation></semantics></math>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>N</mi><mi>i</mi></msub><mo>−</mo><msub><mi>S</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)</annotation></semantics></math>
 </li>
 <li>Combine:</li>
 </ol>
 <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
-<p>Repeat this simulation (e.g. 1000×) and summarise:</p>
+<p>Repeat this simulation (e.g., 1000 times) and summarise:</p>
 <ul>
 <li>
 <strong>Mean</strong> = expected coverage</li>
 <li>
-<strong>Quantiles</strong> = credible interval</li>
+<strong>Quantiles</strong> = credible interval (95% by default)</li>
 </ul>
+<p>Because each simulation draws from the full posterior, the resulting
+distribution of coverage estimates naturally captures the joint
+uncertainty in both pathogen incidence and susceptibility. The credible
+interval tells you how confident you can be in the coverage estimate,
+something a traditional antibiogram never provides.</p>
 </div>
 </div>
 <div class="section level2">
+<h2 id="when-to-use-wisca-vs--traditional-antibiograms">When to use WISCA vs. traditional antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs--traditional-antibiograms"></a>
+</h2>
+<table class="table">
+<thead><tr class="header">
+<th>Goal</th>
+<th>Recommended approach</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>Guide empirical therapy decisions</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="even">
+<td>Compare regimens for a syndrome</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="odd">
+<td>Evaluate combination regimens</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="even">
+<td>Antimicrobial stewardship (A-team)</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="odd">
+<td>Track resistance trends per species</td>
+<td>Traditional / Combination</td>
+</tr>
+<tr class="even">
+<td>AMR surveillance reporting</td>
+<td>Traditional / Syndromic</td>
+</tr>
+<tr class="odd">
+<td>Understand species-level epidemiology</td>
+<td>Traditional</td>
+</tr>
+</tbody>
+</table>
+<p>In short: if the end goal involves a <em>patient</em> who does not
+yet have a culture result, WISCA is the appropriate tool. If the end
+goal is <em>surveillance</em> of resistance at the species level, the
+traditional antibiogram remains fit for purpose.</p>
+</div>
+<div class="section level2">
 <h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
 </h2>
 <div class="section level3">
-<h3 id="prepare-data-and-simulate-synthetic-syndrome">Prepare data and simulate synthetic syndrome<a class="anchor" aria-label="anchor" href="#prepare-data-and-simulate-synthetic-syndrome"></a>
+<h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
 </h3>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><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://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span></span>
 <span></span>
@@ -253,16 +322,16 @@ I (intermediate [CLSI], or susceptible, increased exposure
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
 <span><span class="co">#&gt;    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
@@ -271,15 +340,16 @@ I (intermediate [CLSI], or susceptible, increased exposure
 <span><span class="co">#&gt; <span style="color: #949494;">#   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span></span>
 <span></span>
-<span><span class="co"># Add a fake syndrome column</span></span>
-<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"No UTI"</span><span class="op">)</span></span></code></pre></div>
+<span><span class="co"># Add a synthetic syndrome column for demonstration</span></span>
+<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"Non-UTI"</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level3">
-<h3 id="basic-wisca-antibiogram">Basic WISCA antibiogram<a class="anchor" aria-label="anchor" href="#basic-wisca-antibiogram"></a>
+<h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
 </h3>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
-<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">Amoxicillin/clavulanic acid</th>
@@ -287,18 +357,22 @@ I (intermediate [CLSI], or susceptible, increased exposure
 <th align="left">Gentamicin</th>
 </tr></thead>
 <tbody><tr class="odd">
-<td align="left">73.7% (71.7-75.8%)</td>
-<td align="left">77% (74.3-79.4%)</td>
-<td align="left">72.8% (70.7-74.8%)</td>
+<td align="left">74.2% (72.1-76.1%)</td>
+<td align="left">78.4% (75.6-81.1%)</td>
+<td align="left">72.5% (70.4-74.6%)</td>
 </tr></tbody>
 </table>
 </div>
 <div class="section level3">
 <h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
 </h3>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<p>Combination regimens are specified with a <code>+</code> separator.
+WISCA evaluates whether <em>at least one</em> agent in the combination
+covers the pathogen:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
-<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="24%">
@@ -311,19 +385,22 @@ I (intermediate [CLSI], or susceptible, increased exposure
 <th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
 </tr></thead>
 <tbody><tr class="odd">
-<td align="left">73.8% (71.8-75.7%)</td>
-<td align="left">87.5% (85.9-89%)</td>
-<td align="left">89.7% (88.2-91.1%)</td>
+<td align="left">74.2% (72.2-76.1%)</td>
+<td align="left">88.8% (87.2-90.4%)</td>
+<td align="left">90.8% (89.4-92.2%)</td>
 </tr></tbody>
 </table>
 </div>
 <div class="section level3">
 <h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
 </h3>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<p>Use <code>syndromic_group</code> to produce separate WISCA estimates
+per clinical stratum. You can pass a column name or any expression:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
-<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
-<span>      syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span><span class="op">)</span></span></code></pre></div>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
+<span>  syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="12%">
@@ -339,26 +416,27 @@ I (intermediate [CLSI], or susceptible, increased exposure
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="left">No UTI</td>
-<td align="left">70.1% (67.8-72.3%)</td>
-<td align="left">85.2% (83.1-87.2%)</td>
-<td align="left">87.1% (85.3-88.7%)</td>
+<td align="left">Non-UTI</td>
+<td align="left">70.3% (67.9-72.7%)</td>
+<td align="left">86.8% (84.9-88.7%)</td>
+<td align="left">88.4% (86.4-90.2%)</td>
 </tr>
 <tr class="even">
 <td align="left">UTI</td>
-<td align="left">80.9% (77.7-83.8%)</td>
-<td align="left">88.2% (85.7-90.5%)</td>
-<td align="left">90.9% (88.7-93%)</td>
+<td align="left">80.3% (77-83.3%)</td>
+<td align="left">88.4% (85.7-90.8%)</td>
+<td align="left">91% (88.3-93.3%)</td>
 </tr>
 </tbody>
 </table>
 <p>The <code>AMR</code> package is available in 28 languages, which can
 all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
-<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
-<span>      syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
-<span>      language <span class="op">=</span> <span class="st">"Spanish"</span><span class="op">)</span></span></code></pre></div>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
+<span>  syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
+<span>  language <span class="op">=</span> <span class="st">"Spanish"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="12%">
@@ -374,20 +452,36 @@ all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></c
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="left">No UCI</td>
-<td align="left">70% (67.8-72.4%)</td>
-<td align="left">85.3% (83.3-87.2%)</td>
-<td align="left">87% (85.3-88.8%)</td>
+<td align="left">Non-UCI</td>
+<td align="left">70.4% (68-72.8%)</td>
+<td align="left">86.7% (84.6-88.7%)</td>
+<td align="left">88.5% (86.5-90.2%)</td>
 </tr>
 <tr class="even">
 <td align="left">UCI</td>
-<td align="left">80.9% (77.7-83.9%)</td>
-<td align="left">88.2% (85.5-90.6%)</td>
-<td align="left">90.9% (88.7-93%)</td>
+<td align="left">80.3% (77.2-83.5%)</td>
+<td align="left">88.4% (85.5-90.8%)</td>
+<td align="left">91% (88.4-93.1%)</td>
 </tr>
 </tbody>
 </table>
 </div>
+<div class="section level3">
+<h3 id="interpreting-the-output">Interpreting the output<a class="anchor" aria-label="anchor" href="#interpreting-the-output"></a>
+</h3>
+<p>Each row shows the estimated empirical coverage for a regimen, with a
+95% credible interval. When comparing regimens:</p>
+<ul>
+<li>
+<strong>Overlapping credible intervals</strong> mean there is no
+statistically significant difference in coverage. If a narrower-spectrum
+regimen overlaps with a broader one, the narrower-spectrum option can be
+preferred on stewardship grounds.</li>
+<li>
+<strong>Non-overlapping credible intervals</strong> indicate a
+clinically meaningful difference in coverage.</li>
+</ul>
+</div>
 </div>
 <div class="section level2">
 <h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
@@ -403,14 +497,43 @@ susceptible</li>
 </ul>
 </div>
 <div class="section level2">
+<h2 id="practical-considerations">Practical considerations<a class="anchor" aria-label="anchor" href="#practical-considerations"></a>
+</h2>
+<ul>
+<li>
+<strong>First isolates only</strong>: always deduplicate using
+<code><a href="../reference/first_isolate.html">first_isolate()</a></code> before running WISCA. Repeat isolates
+introduce bias.</li>
+<li>
+<strong>Pathogen selection</strong>: consider filtering with
+<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>. Including rare contaminants
+(e.g. CoNS without clinical context) can distort estimates and may
+artificially lower coverage (Cook <em>et al.</em>, 2022).</li>
+<li>
+<strong>Sample size</strong>: coverage estimates become reliable
+with approximately 100+ isolates. For smaller datasets, consider pooling
+data from multiple sites, but only after verifying that pathogen
+distributions are sufficiently similar (Bielicki <em>et al.</em>,
+2016).</li>
+<li>
+<strong>Culture request bias</strong>: WISCA is only as good as the
+data it is based on. If cultures are selectively requested (e.g. only
+after treatment failure), the dataset will be biased towards resistant
+isolates. A robust culture policy is essential for reliable
+estimates.</li>
+</ul>
+</div>
+<div class="section level2">
 <h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
 </h2>
 <ul>
-<li>It assumes your data are representative</li>
-<li>No adjustment for patient-level covariates, although these could be
-passed onto the <code>syndromic_group</code> argument</li>
-<li>WISCA does not model resistance over time, you might want to use
-<code>tidymodels</code> for that, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
+<li>It assumes your data are representative of the patient population
+you are treating</li>
+<li>No direct adjustment for patient-level covariates, although these
+can be passed onto the <code>syndromic_group</code> argument for
+stratification</li>
+<li>WISCA does not model resistance trends over time; for that, you
+might want to use <code>tidymodels</code>, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
 basic introduction</a>
 </li>
 </ul>
@@ -420,24 +543,48 @@ basic introduction</a>
 </h2>
 <p>WISCA enables:</p>
 <ul>
-<li>Empirical regimen comparison,</li>
-<li>Syndrome-specific coverage estimation,</li>
-<li>Fully probabilistic interpretation.</li>
+<li>
+<strong>Empirical regimen comparison</strong>, answering the
+clinician’s actual question</li>
+<li>
+<strong>Syndrome-specific coverage estimation</strong>, stratifiable
+by any clinical variable</li>
+<li>
+<strong>Fully probabilistic interpretation</strong>, with credible
+intervals that honestly communicate uncertainty</li>
 </ul>
 <p>It is available in the <code>AMR</code> package via either:</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
-<h2 id="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a>
+<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
-<p>Bielicki, JA, et al. (2016). <em>Selecting appropriate empirical
-antibiotic regimens for paediatric bloodstream infections: application
-of a Bayesian decision model to local and pooled antimicrobial
-resistance surveillance data.</em> <strong>J Antimicrob
-Chemother</strong>. 71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a></p>
+<ol style="list-style-type: decimal">
+<li>Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
+Demonstration of the weighted-incidence syndromic combination
+antibiogram: an empiric prescribing decision aid. <em>Infect Control
+Hosp Epidemiol.</em> 2012;33(4):381-388. <a href="https://doi.org/10.1086/664768" class="external-link uri">https://doi.org/10.1086/664768</a>
+</li>
+<li>Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
+Weighted-incidence syndromic combination antibiograms to guide empiric
+treatment of critical care infections: a retrospective cohort study.
+<em>Crit Care.</em> 2014;18(3):R112. <a href="https://doi.org/10.1186/cc13901" class="external-link uri">https://doi.org/10.1186/cc13901</a>
+</li>
+<li>Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
+Selecting appropriate empirical antibiotic regimens for paediatric
+bloodstream infections: application of a Bayesian decision model to
+local and pooled antimicrobial resistance surveillance data. <em>J
+Antimicrob Chemother.</em> 2016;71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a>
+</li>
+<li>Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
+prescribing in pediatric bloodstream infections: a potential application
+of weighted-incidence syndromic combination antibiograms (WISCA).
+<em>Expert Rev Anti Infect Ther.</em> 2022;20(3):445-456. <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link uri">https://doi.org/10.1080/14787210.2021.1967145</a>
+</li>
+</ol>
 </div>
  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
    </nav></aside>
--- a/articles/WISCA.md
+++ b/articles/WISCA.md
@@ -0,0 +1,373 @@
+# Estimating Empirical Coverage with WISCA
+
+## Why WISCA?
+
+When a clinician starts empirical antimicrobial therapy, the causative
+pathogen is unknown. The question they need answered is not *“what
+proportion of* E. coli *is susceptible to ciprofloxacin?“* but rather
+*“what is the probability that this regimen will adequately cover
+whatever pathogen turns out to be causing my patient’s infection?”*
+
+The traditional cumulative antibiogram, as standardised by CLSI M39,
+cannot answer that question. It presents susceptibility percentages per
+species per antibiotic, but:
+
+- **It fragments information by organism.** The clinician must mentally
+  combine susceptibility rates across multiple species, weighting by how
+  often each species causes the syndrome, a calculation nobody does at
+  the bedside.
+- **It ignores pathogen incidence.** A species that causes 2% of
+  infections is given the same visual weight as one that causes 60%.
+- **It does not evaluate combination regimens.** Much empirical therapy
+  consists of two or more agents, but the traditional antibiogram only
+  shows monotherapy per organism.
+- **It provides no measure of uncertainty.** A reported “90%
+  susceptible” based on 50 isolates has a 95% confidence interval of
+  roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
+  point estimate without context.
+
+**WISCA** (Weighted-Incidence Syndromic Combination Antibiogram)
+resolves all four limitations. It estimates the probability that a
+regimen will provide adequate empirical coverage for a given infection
+syndrome, weighted by local pathogen incidence, with full uncertainty
+quantification via Bayesian inference.
+
+The concept was introduced by Hebert *et al.* (2012), who demonstrated
+that traditional antibiogram susceptibility rates could be misleading:
+ciprofloxacin appeared 84% effective against *E. coli* in the
+traditional antibiogram, but WISCA revealed only 62% coverage for UTI
+and 37% for abdominal infections, because enterococci (intrinsically
+resistant) and other species contribute substantially to these
+syndromes. Randhawa *et al.* (2014) showed that WISCA-guided regimen
+selection could improve time-to-adequate-coverage on the ICU by over
+40%. Bielicki *et al.* (2016) introduced the Bayesian framework now used
+in this package, enabling credible intervals and multi-centre pooling.
+Cook *et al.* (2022) applied it globally across 52 hospitals in 23
+countries.
+
+## The idea
+
+WISCA asks:
+
+> “What is the **probability** that this regimen **will cover** the
+> pathogen, given the syndrome?”
+
+This means combining two quantities:
+
+- **Pathogen incidence** in the syndrome (how often each species causes
+  it),
+- **Susceptibility** of each pathogen to the regimen.
+
+We can write this as:
+
+``` math
+\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
+```
+
+For example, suppose in your hospital:
+
+- *E. coli* causes 60% of UTIs, and 90% of *E. coli* are susceptible to
+  a drug.
+- *Klebsiella* causes 40% of UTIs, and 70% of *Klebsiella* are
+  susceptible.
+
+Then:
+
+``` math
+\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
+```
+
+That 82% is a far more clinically meaningful number than the
+species-level “90% of *E. coli*” and “70% of *Klebsiella*” reported
+separately in a traditional antibiogram, because it directly answers the
+question the clinician actually faces.
+
+But in real data, both incidence and susceptibility are **estimated from
+finite samples**, so they carry uncertainty. A sample of 50 isolates is
+not a census. WISCA models this uncertainty **probabilistically**, using
+conjugate Bayesian distributions.
+
+## The Bayesian engine
+
+### Pathogen incidence
+
+Let:
+
+- $`K`$ be the number of pathogens,
+- $`\boldsymbol{\alpha} = (1, 1, \ldots, 1)`$ be a $`\text{Dirichlet}`$
+  prior (uniform, non-informative),
+- $`\boldsymbol{n} = (n_1, \ldots, n_K)`$ be the observed isolate counts
+  per species.
+
+Then the posterior incidence is:
+
+``` math
+\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
+```
+
+To simulate from this, we use:
+
+``` math
+x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
+```
+
+The Dirichlet is the conjugate prior for multinomial data. With the
+non-informative prior $`\text{Dirichlet}(1, 1, \ldots, 1)`$, the
+posterior is dominated by the data once sample sizes are reasonable.
+With small samples, the posterior is appropriately more diffuse,
+reflecting genuine uncertainty, and the resulting credible intervals
+will be wider.
+
+### Susceptibility
+
+Each pathogen-regimen pair has a prior and observed data:
+
+- Default prior: $`\text{Beta}(0.5, 0.5)`$ (Jeffreys prior)
+- Intrinsically resistant pairs: $`\text{Beta}(1, 9999)`$, forcing
+  near-zero susceptibility regardless of observed data (based on EUCAST
+  Expected Resistant Phenotypes)
+- Data: $`S`$ susceptible out of $`N`$ tested
+
+The $`S`$ category could also include values SDD (susceptible,
+dose-dependent) and I (intermediate \[CLSI\], or susceptible, increased
+exposure \[EUCAST\]).
+
+Then the posterior is:
+
+``` math
+\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)
+```
+
+### Final coverage estimate
+
+Putting it together:
+
+1.  Simulate pathogen incidence:
+    $`\boldsymbol{p} \sim \text{Dirichlet}`$
+2.  Simulate susceptibility:
+    $`\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)`$
+3.  Combine:
+
+``` math
+\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
+```
+
+Repeat this simulation (e.g., 1000 times) and summarise:
+
+- **Mean** = expected coverage
+- **Quantiles** = credible interval (95% by default)
+
+Because each simulation draws from the full posterior, the resulting
+distribution of coverage estimates naturally captures the joint
+uncertainty in both pathogen incidence and susceptibility. The credible
+interval tells you how confident you can be in the coverage estimate,
+something a traditional antibiogram never provides.
+
+## When to use WISCA vs. traditional antibiograms
+
+| Goal                                  | Recommended approach      |
+|---------------------------------------|---------------------------|
+| Guide empirical therapy decisions     | **WISCA**                 |
+| Compare regimens for a syndrome       | **WISCA**                 |
+| Evaluate combination regimens         | **WISCA**                 |
+| Antimicrobial stewardship (A-team)    | **WISCA**                 |
+| Track resistance trends per species   | Traditional / Combination |
+| AMR surveillance reporting            | Traditional / Syndromic   |
+| Understand species-level epidemiology | Traditional               |
+
+In short: if the end goal involves a *patient* who does not yet have a
+culture result, WISCA is the appropriate tool. If the end goal is
+*surveillance* of resistance at the species level, the traditional
+antibiogram remains fit for purpose.
+
+## Practical use in the `AMR` package
+
+### Prepare data
+
+``` r
+
+library(AMR)
+data <- example_isolates
+
+# Structure of our data
+data
+#> # A tibble: 2,000 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
+#> # ℹ 1,990 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+
+# Add a synthetic syndrome column for demonstration
+data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI")
+```
+
+### Basic WISCA
+
+``` r
+
+wisca(data,
+  antimicrobials = c("AMC", "CIP", "GEN")
+)
+```
+
+| Amoxicillin/clavulanic acid | Ciprofloxacin      | Gentamicin         |
+|:----------------------------|:-------------------|:-------------------|
+| 74.2% (72.1-76.1%)          | 78.4% (75.6-81.1%) | 72.5% (70.4-74.6%) |
+
+### Use combination regimens
+
+Combination regimens are specified with a `+` separator. WISCA evaluates
+whether *at least one* agent in the combination covers the pathogen:
+
+``` r
+
+wisca(data,
+  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN")
+)
+```
+
+| Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
+|:---|:---|:---|
+| 74.2% (72.2-76.1%) | 88.8% (87.2-90.4%) | 90.8% (89.4-92.2%) |
+
+### Stratify by syndrome
+
+Use `syndromic_group` to produce separate WISCA estimates per clinical
+stratum. You can pass a column name or any expression:
+
+``` r
+
+wisca(data,
+  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
+  syndromic_group = "syndrome"
+)
+```
+
+| Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
+|:---|:---|:---|:---|
+| Non-UTI | 70.3% (67.9-72.7%) | 86.8% (84.9-88.7%) | 88.4% (86.4-90.2%) |
+| UTI | 80.3% (77-83.3%) | 88.4% (85.7-90.8%) | 91% (88.3-93.3%) |
+
+The `AMR` package is available in 28 languages, which can all be used
+for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
+function too:
+
+``` r
+
+wisca(data,
+  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
+  syndromic_group = gsub("UTI", "UCI", data$syndrome),
+  language = "Spanish"
+)
+```
+
+| Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina |
+|:---|:---|:---|:---|
+| Non-UCI | 70.4% (68-72.8%) | 86.7% (84.6-88.7%) | 88.5% (86.5-90.2%) |
+| UCI | 80.3% (77.2-83.5%) | 88.4% (85.5-90.8%) | 91% (88.4-93.1%) |
+
+### Interpreting the output
+
+Each row shows the estimated empirical coverage for a regimen, with a
+95% credible interval. When comparing regimens:
+
+- **Overlapping credible intervals** mean there is no statistically
+  significant difference in coverage. If a narrower-spectrum regimen
+  overlaps with a broader one, the narrower-spectrum option can be
+  preferred on stewardship grounds.
+- **Non-overlapping credible intervals** indicate a clinically
+  meaningful difference in coverage.
+
+## Sensible defaults, which can be customised
+
+- `simulations = 1000`: number of Monte Carlo draws
+- `conf_interval = 0.95`: coverage interval width
+- `combine_SI = TRUE`: count “I” and “SDD” as susceptible
+
+## Practical considerations
+
+- **First isolates only**: always deduplicate using
+  [`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
+  before running WISCA. Repeat isolates introduce bias.
+- **Pathogen selection**: consider filtering with
+  [`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md).
+  Including rare contaminants (e.g. CoNS without clinical context) can
+  distort estimates and may artificially lower coverage (Cook *et al.*,
+  2022).
+- **Sample size**: coverage estimates become reliable with approximately
+  100+ isolates. For smaller datasets, consider pooling data from
+  multiple sites, but only after verifying that pathogen distributions
+  are sufficiently similar (Bielicki *et al.*, 2016).
+- **Culture request bias**: WISCA is only as good as the data it is
+  based on. If cultures are selectively requested (e.g. only after
+  treatment failure), the dataset will be biased towards resistant
+  isolates. A robust culture policy is essential for reliable estimates.
+
+## Limitations
+
+- It assumes your data are representative of the patient population you
+  are treating
+- No direct adjustment for patient-level covariates, although these can
+  be passed onto the `syndromic_group` argument for stratification
+- WISCA does not model resistance trends over time; for that, you might
+  want to use `tidymodels`, for which we [wrote a basic
+  introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
+
+## Summary
+
+WISCA enables:
+
+- **Empirical regimen comparison**, answering the clinician’s actual
+  question
+- **Syndrome-specific coverage estimation**, stratifiable by any
+  clinical variable
+- **Fully probabilistic interpretation**, with credible intervals that
+  honestly communicate uncertainty
+
+It is available in the `AMR` package via either:
+
+``` r
+
+wisca(...)
+
+antibiogram(..., wisca = TRUE)
+```
+
+## References
+
+1.  Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
+    Demonstration of the weighted-incidence syndromic combination
+    antibiogram: an empiric prescribing decision aid. *Infect Control
+    Hosp Epidemiol.* 2012;33(4):381-388.
+    <https://doi.org/10.1086/664768>
+2.  Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
+    Weighted-incidence syndromic combination antibiograms to guide
+    empiric treatment of critical care infections: a retrospective
+    cohort study. *Crit Care.* 2014;18(3):R112.
+    <https://doi.org/10.1186/cc13901>
+3.  Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
+    Selecting appropriate empirical antibiotic regimens for paediatric
+    bloodstream infections: application of a Bayesian decision model to
+    local and pooled antimicrobial resistance surveillance data. *J
+    Antimicrob Chemother.* 2016;71(3):794-802.
+    <https://doi.org/10.1093/jac/dkv397>
+4.  Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
+    prescribing in pediatric bloodstream infections: a potential
+    application of weighted-incidence syndromic combination antibiograms
+    (WISCA). *Expert Rev Anti Infect Ther.* 2022;20(3):445-456.
+    <https://doi.org/10.1080/14787210.2021.1967145>
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -12,8 +12,8 @@
 <link rel="icon" sizes="any" href="../favicon.ico">
 <link rel="manifest" href="../site.webmanifest">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.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/Lato-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
@@ -30,7 +30,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>


    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,7 +80,7 @@
  <main id="main" class="col-md-9"><div class="page-header">
      <img src="../logo.svg" class="logo" alt=""><h1>Download data sets for download / own use</h1>
            
-            <h4 data-toc-skip class="date">17 July 2025</h4>
+            <h4 data-toc-skip class="date">23 June 2026</h4>
      
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
      <div class="d-none name"><code>datasets.Rmd</code></div>
@@ -104,42 +104,43 @@ available in Python.</p>
 <h2 id="microorganisms-full-microbial-taxonomy">
 <code>microorganisms</code>: Full Microbial Taxonomy<a class="anchor" aria-label="anchor" href="#microorganisms-full-microbial-taxonomy"></a>
 </h2>
-<p>A data set with 78 679 rows and 26 columns, containing the following
-column names:<br><em>mo</em>, <em>fullname</em>, <em>status</em>, <em>kingdom</em>,
-<em>phylum</em>, <em>class</em>, <em>order</em>, <em>family</em>,
-<em>genus</em>, <em>species</em>, <em>subspecies</em>, <em>rank</em>,
-<em>ref</em>, <em>oxygen_tolerance</em>, <em>source</em>, <em>lpsn</em>,
+<p>A data set with 96 982 rows and 28 columns, containing the following
+column names:<br><em>mo</em>, <em>fullname</em>, <em>status</em>, <em>domain</em>,
+<em>kingdom</em>, <em>phylum</em>, <em>class</em>, <em>order</em>,
+<em>family</em>, <em>genus</em>, <em>species</em>, <em>subspecies</em>,
+<em>rank</em>, <em>ref</em>, <em>oxygen_tolerance</em>,
+<em>morphology</em>, <em>source</em>, <em>lpsn</em>,
 <em>lpsn_parent</em>, <em>lpsn_renamed_to</em>, <em>mycobank</em>,
 <em>mycobank_parent</em>, <em>mycobank_renamed_to</em>, <em>gbif</em>,
 <em>gbif_parent</em>, <em>gbif_renamed_to</em>, <em>prevalence</em>, and
 <em>snomed</em>.</p>
 <p>This data set is in R available as <code>microorganisms</code>, after
 you load the <code>AMR</code> package.</p>
-<p>It was last updated on 26 March 2025 16:19:17 UTC. Find more info
+<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
 about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.html">data set
 here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds" class="external-link">original
-R Data Structure (RDS) file</a> (1.8 MB)<br>
+R Data Structure (RDS) file</a> (2.2 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt" class="external-link">tab-separated
-text file</a> (17.7 MB)<br>
+text file</a> (23.1 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx" class="external-link">Microsoft
-Excel workbook</a> (8.7 MB)<br>
+Excel workbook</a> (11.4 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather" class="external-link">Apache
-Feather file</a> (8.3 MB)<br>
+Feather file</a> (11 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet" class="external-link">Apache
-Parquet file</a> (3.8 MB)<br>
+Parquet file</a> (4.6 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav" class="external-link">IBM
-SPSS Statistics data file</a> (28.4 MB)<br>
+SPSS Statistics data file</a> (35.2 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta" class="external-link">Stata
-DTA file</a> (89.5 MB)</li>
+DTA file</a> (96.6 MB)</li>
 </ul>
 <p><strong>NOTE: The exported files for SPSS and Stata contain only the
 first 50 SNOMED codes per record, as their file size would otherwise
@@ -157,41 +158,42 @@ all SNOMED codes as comma separated values.</p>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">(unknown kingdom)</td>
-<td align="center">1</td>
+<td align="center"></td>
+<td align="center">20</td>
 </tr>
 <tr class="even">
-<td align="center">Animalia</td>
-<td align="center">1 628</td>
+<td align="center">(unknown kingdom)</td>
+<td align="center">8</td>
 </tr>
 <tr class="odd">
+<td align="center">Animalia</td>
+<td align="center">2 015</td>
+</tr>
+<tr class="even">
 <td align="center">Archaea</td>
-<td align="center">1 419</td>
+<td align="center">150</td>
+</tr>
+<tr class="odd">
+<td align="center">Bacillati</td>
+<td align="center">24 200</td>
 </tr>
 <tr class="even">
 <td align="center">Bacteria</td>
-<td align="center">39 249</td>
-</tr>
-<tr class="odd">
-<td align="center">Chromista</td>
-<td align="center">178</td>
-</tr>
-<tr class="even">
-<td align="center">Fungi</td>
-<td align="center">28 137</td>
+<td align="center">2</td>
 </tr>
 </tbody>
 </table>
 <p>First 6 rows when filtering on genus <em>Escherichia</em>:</p>
-<table style="width:100%;" class="table">
+<table class="table">
 <colgroup>
 <col width="4%">
-<col width="6%">
+<col width="5%">
 <col width="2%">
 <col width="2%">
 <col width="3%">
+<col width="3%">
 <col width="4%">
-<col width="4%">
+<col width="3%">
 <col width="4%">
 <col width="2%">
 <col width="3%">
@@ -199,6 +201,7 @@ all SNOMED codes as comma separated values.</p>
 <col width="2%">
 <col width="5%">
 <col width="6%">
+<col width="2%">
 <col width="1%">
 <col width="1%">
 <col width="2%">
@@ -210,12 +213,13 @@ all SNOMED codes as comma separated values.</p>
 <col width="2%">
 <col width="3%">
 <col width="2%">
-<col width="10%">
+<col width="9%">
 </colgroup>
 <thead><tr class="header">
 <th align="center">mo</th>
 <th align="center">fullname</th>
 <th align="center">status</th>
+<th align="center">domain</th>
 <th align="center">kingdom</th>
 <th align="center">phylum</th>
 <th align="center">class</th>
@@ -227,6 +231,7 @@ all SNOMED codes as comma separated values.</p>
 <th align="center">rank</th>
 <th align="center">ref</th>
 <th align="center">oxygen_tolerance</th>
+<th align="center">morphology</th>
 <th align="center">source</th>
 <th align="center">lpsn</th>
 <th align="center">lpsn_parent</th>
@@ -246,6 +251,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">Escherichia</td>
 <td align="center">accepted</td>
 <td align="center">Bacteria</td>
+<td align="center">Pseudomonadati</td>
 <td align="center">Pseudomonadota</td>
 <td align="center">Gammaproteobacteria</td>
 <td align="center">Enterobacterales</td>
@@ -256,6 +262,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">genus</td>
 <td align="center">Castellani et al., 1919</td>
 <td align="center">facultative anaerobe</td>
+<td align="center">rods</td>
 <td align="center">LPSN</td>
 <td align="center">515602</td>
 <td align="center">482</td>
@@ -263,8 +270,8 @@ all SNOMED codes as comma separated values.</p>
 <td align="center"></td>
 <td align="center"></td>
 <td align="center"></td>
-<td align="center"></td>
-<td align="center">11158430</td>
+<td align="center">CS33H</td>
+<td align="center">CRYWR</td>
 <td align="center"></td>
 <td align="center">1</td>
 <td align="center">407310004, 407251000, 407281008, …</td>
@@ -274,6 +281,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">Escherichia adecarboxylata</td>
 <td align="center">synonym</td>
 <td align="center">Bacteria</td>
+<td align="center">Pseudomonadati</td>
 <td align="center">Pseudomonadota</td>
 <td align="center">Gammaproteobacteria</td>
 <td align="center">Enterobacterales</td>
@@ -284,6 +292,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">species</td>
 <td align="center">Leclerc, 1962</td>
 <td align="center">likely facultative anaerobe</td>
+<td align="center">rods</td>
 <td align="center">LPSN</td>
 <td align="center">776052</td>
 <td align="center">515602</td>
@@ -291,9 +300,9 @@ all SNOMED codes as comma separated values.</p>
 <td align="center"></td>
 <td align="center"></td>
 <td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">CS33J</td>
+<td align="center">CS33H</td>
+<td align="center">3SVX6</td>
 <td align="center">1</td>
 <td align="center"></td>
 </tr>
@@ -302,6 +311,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">Escherichia albertii</td>
 <td align="center">accepted</td>
 <td align="center">Bacteria</td>
+<td align="center">Pseudomonadati</td>
 <td align="center">Pseudomonadota</td>
 <td align="center">Gammaproteobacteria</td>
 <td align="center">Enterobacterales</td>
@@ -312,6 +322,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">species</td>
 <td align="center">Huys et al., 2003</td>
 <td align="center">facultative anaerobe</td>
+<td align="center">rods</td>
 <td align="center">LPSN</td>
 <td align="center">776053</td>
 <td align="center">515602</td>
@@ -319,8 +330,8 @@ all SNOMED codes as comma separated values.</p>
 <td align="center"></td>
 <td align="center"></td>
 <td align="center"></td>
-<td align="center">5427575</td>
-<td align="center"></td>
+<td align="center">3BGTB</td>
+<td align="center">CS33H</td>
 <td align="center"></td>
 <td align="center">1</td>
 <td align="center">419388003</td>
@@ -330,6 +341,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">Escherichia blattae</td>
 <td align="center">synonym</td>
 <td align="center">Bacteria</td>
+<td align="center">Pseudomonadati</td>
 <td align="center">Pseudomonadota</td>
 <td align="center">Gammaproteobacteria</td>
 <td align="center">Enterobacterales</td>
@@ -340,6 +352,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">species</td>
 <td align="center">Burgess et al., 1973</td>
 <td align="center">likely facultative anaerobe</td>
+<td align="center">rods</td>
 <td align="center">LPSN</td>
 <td align="center">776056</td>
 <td align="center">515602</td>
@@ -347,9 +360,9 @@ all SNOMED codes as comma separated values.</p>
 <td align="center"></td>
 <td align="center"></td>
 <td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">CS33K</td>
+<td align="center">CS33H</td>
+<td align="center">4X4P7</td>
 <td align="center">1</td>
 <td align="center"></td>
 </tr>
@@ -358,6 +371,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">Escherichia coli</td>
 <td align="center">accepted</td>
 <td align="center">Bacteria</td>
+<td align="center">Pseudomonadati</td>
 <td align="center">Pseudomonadota</td>
 <td align="center">Gammaproteobacteria</td>
 <td align="center">Enterobacterales</td>
@@ -368,6 +382,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">species</td>
 <td align="center">Castellani et al., 1919</td>
 <td align="center">facultative anaerobe</td>
+<td align="center">rods</td>
 <td align="center">LPSN</td>
 <td align="center">776057</td>
 <td align="center">515602</td>
@@ -375,8 +390,8 @@ all SNOMED codes as comma separated values.</p>
 <td align="center"></td>
 <td align="center"></td>
 <td align="center"></td>
-<td align="center">11286021</td>
-<td align="center"></td>
+<td align="center">NT3L7</td>
+<td align="center">CS33H</td>
 <td align="center"></td>
 <td align="center">1</td>
 <td align="center">1095001000112106, 715307006, 737528008, …</td>
@@ -386,6 +401,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">Escherichia coli coli</td>
 <td align="center">accepted</td>
 <td align="center">Bacteria</td>
+<td align="center">Pseudomonadati</td>
 <td align="center">Pseudomonadota</td>
 <td align="center">Gammaproteobacteria</td>
 <td align="center">Enterobacterales</td>
@@ -396,6 +412,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center">subspecies</td>
 <td align="center"></td>
 <td align="center"></td>
+<td align="center"></td>
 <td align="center">GBIF</td>
 <td align="center"></td>
 <td align="center">776057</td>
@@ -404,7 +421,7 @@ all SNOMED codes as comma separated values.</p>
 <td align="center"></td>
 <td align="center"></td>
 <td align="center">12233256</td>
-<td align="center">11286021</td>
+<td align="center">NT3L7</td>
 <td align="center"></td>
 <td align="center">1</td>
 <td align="center"></td>
@@ -417,32 +434,32 @@ all SNOMED codes as comma separated values.</p>
 <h2 id="antimicrobials-antibiotic-and-antifungal-drugs">
 <code>antimicrobials</code>: Antibiotic and Antifungal Drugs<a class="anchor" aria-label="anchor" href="#antimicrobials-antibiotic-and-antifungal-drugs"></a>
 </h2>
-<p>A data set with 497 rows and 14 columns, containing the following
+<p>A data set with 505 rows and 14 columns, containing the following
 column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc</em>,
 <em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>,
 <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>,
 <em>iv_ddd</em>, <em>iv_units</em>, and <em>loinc</em>.</p>
 <p>This data set is in R available as <code>antimicrobials</code>, after
 you load the <code>AMR</code> package.</p>
-<p>It was last updated on 1 June 2025 09:54:40 UTC. Find more info about
-the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/antimicrobials.html">data set
+<p>It was last updated on 23 June 2026 12:38:59 UTC. Find more info
+about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/antimicrobials.html">data set
 here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.rds" class="external-link">original
-R Data Structure (RDS) file</a> (45 kB)<br>
+R Data Structure (RDS) file</a> (44 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.txt" class="external-link">tab-separated
 text file</a> (0.1 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.xlsx" class="external-link">Microsoft
-Excel workbook</a> (78 kB)<br>
+Excel workbook</a> (79 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.feather" class="external-link">Apache
 Feather file</a> (0.1 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.parquet" class="external-link">Apache
-Parquet file</a> (0.1 MB)<br>
+Parquet file</a> (94 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.sav" class="external-link">IBM
 SPSS Statistics data file</a> (0.4 MB)<br>
@@ -454,22 +471,22 @@ DTA file</a> (10 kB)</li>
 contain the ATC codes, common abbreviations, trade names and LOINC codes
 as comma separated values.</p>
 <p><strong>Example content</strong></p>
-<table style="width:100%;" class="table">
+<table class="table">
 <colgroup>
 <col width="1%">
 <col width="2%">
 <col width="7%">
-<col width="7%">
-<col width="9%">
 <col width="12%">
-<col width="16%">
-<col width="6%">
-<col width="16%">
+<col width="8%">
+<col width="11%">
+<col width="15%">
+<col width="5%">
+<col width="15%">
 <col width="2%">
 <col width="3%">
 <col width="2%">
 <col width="2%">
-<col width="9%">
+<col width="8%">
 </colgroup>
 <thead><tr class="header">
 <th align="center">ab</th>
@@ -508,7 +525,7 @@ as comma separated values.</p>
 <td align="center">AMX</td>
 <td align="center">33613</td>
 <td align="center">Amoxicillin</td>
-<td align="center">Beta-lactams/penicillins</td>
+<td align="center">Aminopenicillins, Penicillins, Beta-lactams</td>
 <td align="center">J01CA04, QG51AA03, QJ01CA04</td>
 <td align="center">Beta-lactam antibacterials, penicillins</td>
 <td align="center">Penicillins with extended spectrum</td>
@@ -524,7 +541,7 @@ as comma separated values.</p>
 <td align="center">AMC</td>
 <td align="center">23665637</td>
 <td align="center">Amoxicillin/clavulanic acid</td>
-<td align="center">Beta-lactams/penicillins</td>
+<td align="center">Aminopenicillins, Penicillins, Beta-lactams, …</td>
 <td align="center">J01CR02, QJ01CR02</td>
 <td align="center">Beta-lactam antibacterials, penicillins</td>
 <td align="center">Combinations of penicillins, incl. beta-lactamase
@@ -541,7 +558,7 @@ inhibitors</td>
 <td align="center">AMP</td>
 <td align="center">6249</td>
 <td align="center">Ampicillin</td>
-<td align="center">Beta-lactams/penicillins</td>
+<td align="center">Aminopenicillins, Penicillins, Beta-lactams</td>
 <td align="center">J01CA01, QJ01CA01, QJ51CA01, …</td>
 <td align="center">Beta-lactam antibacterials, penicillins</td>
 <td align="center">Penicillins with extended spectrum</td>
@@ -557,7 +574,7 @@ inhibitors</td>
 <td align="center">AZM</td>
 <td align="center">447043</td>
 <td align="center">Azithromycin</td>
-<td align="center">Macrolides/lincosamides</td>
+<td align="center">Macrolides</td>
 <td align="center">J01FA10, QJ01FA10, QS01AA26, …</td>
 <td align="center">Macrolides, lincosamides and streptogramins</td>
 <td align="center">Macrolides</td>
@@ -573,7 +590,7 @@ inhibitors</td>
 <td align="center">PEN</td>
 <td align="center">5904</td>
 <td align="center">Benzylpenicillin</td>
-<td align="center">Beta-lactams/penicillins</td>
+<td align="center">Penicillins, Beta-lactams</td>
 <td align="center">J01CE01, QJ01CE01, QJ51CE01, …</td>
 <td align="center">Combinations of antibacterials</td>
 <td align="center">Combinations of antibacterials</td>
@@ -595,38 +612,38 @@ inhibitors</td>
 <code>clinical_breakpoints</code>: Interpretation from MIC values
 &amp; disk diameters to SIR<a class="anchor" aria-label="anchor" href="#clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir"></a>
 </h2>
-<p>A data set with 40 217 rows and 14 columns, containing the following
+<p>A data set with 45 555 rows and 14 columns, containing the following
 column names:<br><em>guideline</em>, <em>type</em>, <em>host</em>, <em>method</em>,
 <em>site</em>, <em>mo</em>, <em>rank_index</em>, <em>ab</em>,
 <em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>,
 <em>breakpoint_R</em>, <em>uti</em>, and <em>is_SDD</em>.</p>
 <p>This data set is in R available as <code>clinical_breakpoints</code>,
 after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 20 April 2025 10:55:31 UTC. Find more info
+<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
 about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/clinical_breakpoints.html">data
 set here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds" class="external-link">original
-R Data Structure (RDS) file</a> (88 kB)<br>
+R Data Structure (RDS) file</a> (92 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt" class="external-link">tab-separated
-text file</a> (3.7 MB)<br>
+text file</a> (4.2 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.xlsx" class="external-link">Microsoft
-Excel workbook</a> (2.4 MB)<br>
+Excel workbook</a> (2.7 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.feather" class="external-link">Apache
-Feather file</a> (1.8 MB)<br>
+Feather file</a> (2 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet" class="external-link">Apache
 Parquet file</a> (0.1 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav" class="external-link">IBM
-SPSS Statistics data file</a> (6.6 MB)<br>
+SPSS Statistics data file</a> (7.5 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.dta" class="external-link">Stata
-DTA file</a> (11.1 MB)</li>
+DTA file</a> (12.6 MB)</li>
 </ul>
 <p><strong>Example content</strong></p>
 <table class="table">
@@ -668,7 +685,7 @@ DTA file</a> (11.1 MB)</li>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">EUCAST 2025</td>
+<td align="center">EUCAST 2026</td>
 <td align="center">human</td>
 <td align="center">human</td>
 <td align="center">DISK</td>
@@ -686,7 +703,7 @@ DTA file</a> (11.1 MB)</li>
 <td align="center">FALSE</td>
 </tr>
 <tr class="even">
-<td align="center">EUCAST 2025</td>
+<td align="center">EUCAST 2026</td>
 <td align="center">human</td>
 <td align="center">human</td>
 <td align="center">MIC</td>
@@ -704,7 +721,7 @@ DTA file</a> (11.1 MB)</li>
 <td align="center">FALSE</td>
 </tr>
 <tr class="odd">
-<td align="center">EUCAST 2025</td>
+<td align="center">EUCAST 2026</td>
 <td align="center">human</td>
 <td align="center">human</td>
 <td align="center">DISK</td>
@@ -722,7 +739,7 @@ DTA file</a> (11.1 MB)</li>
 <td align="center">FALSE</td>
 </tr>
 <tr class="even">
-<td align="center">EUCAST 2025</td>
+<td align="center">EUCAST 2026</td>
 <td align="center">human</td>
 <td align="center">human</td>
 <td align="center">MIC</td>
@@ -740,7 +757,7 @@ DTA file</a> (11.1 MB)</li>
 <td align="center">FALSE</td>
 </tr>
 <tr class="odd">
-<td align="center">EUCAST 2025</td>
+<td align="center">EUCAST 2026</td>
 <td align="center">human</td>
 <td align="center">human</td>
 <td align="center">DISK</td>
@@ -758,7 +775,7 @@ DTA file</a> (11.1 MB)</li>
 <td align="center">FALSE</td>
 </tr>
 <tr class="even">
-<td align="center">EUCAST 2025</td>
+<td align="center">EUCAST 2026</td>
 <td align="center">human</td>
 <td align="center">human</td>
 <td align="center">MIC</td>
@@ -784,13 +801,13 @@ DTA file</a> (11.1 MB)</li>
 <code>microorganisms.groups</code>: Species Groups and
 Microbiological Complexes<a class="anchor" aria-label="anchor" href="#microorganisms-groups-species-groups-and-microbiological-complexes"></a>
 </h2>
-<p>A data set with 534 rows and 4 columns, containing the following
+<p>A data set with 530 rows and 4 columns, containing the following
 column names:<br><em>mo_group</em>, <em>mo</em>, <em>mo_group_name</em>, and
 <em>mo_name</em>.</p>
 <p>This data set is in R available as
 <code>microorganisms.groups</code>, after you load the <code>AMR</code>
 package.</p>
-<p>It was last updated on 26 March 2025 16:19:17 UTC. Find more info
+<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
 about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.groups.html">data
 set here</a>.</p>
 <p><strong>Direct download links:</strong></p>
@@ -802,7 +819,7 @@ R Data Structure (RDS) file</a> (6 kB)<br>
 text file</a> (50 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx" class="external-link">Microsoft
-Excel workbook</a> (20 kB)<br>
+Excel workbook</a> (19 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather" class="external-link">Apache
 Feather file</a> (19 kB)<br>
@@ -811,10 +828,10 @@ Feather file</a> (19 kB)<br>
 Parquet file</a> (13 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav" class="external-link">IBM
-SPSS Statistics data file</a> (65 kB)<br>
+SPSS Statistics data file</a> (64 kB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta" class="external-link">Stata
-DTA file</a> (83 kB)</li>
+DTA file</a> (82 kB)</li>
 </ul>
 <p><strong>Example content</strong></p>
 <table class="table">
@@ -876,11 +893,11 @@ DTA file</a> (83 kB)</li>
 <code>intrinsic_resistant</code>: Intrinsic Bacterial
 Resistance<a class="anchor" aria-label="anchor" href="#intrinsic_resistant-intrinsic-bacterial-resistance"></a>
 </h2>
-<p>A data set with 271 905 rows and 2 columns, containing the following
+<p>A data set with 294 079 rows and 2 columns, containing the following
 column names:<br><em>mo</em> and <em>ab</em>.</p>
 <p>This data set is in R available as <code>intrinsic_resistant</code>,
 after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 28 March 2025 10:17:49 UTC. Find more info
+<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
 about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/intrinsic_resistant.html">data set
 here</a>.</p>
 <p><strong>Direct download links:</strong></p>
@@ -889,22 +906,22 @@ here</a>.</p>
 R Data Structure (RDS) file</a> (0.1 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt" class="external-link">tab-separated
-text file</a> (10.1 MB)<br>
+text file</a> (10.9 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx" class="external-link">Microsoft
-Excel workbook</a> (2.9 MB)<br>
+Excel workbook</a> (3.1 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather" class="external-link">Apache
-Feather file</a> (2.3 MB)<br>
+Feather file</a> (2.5 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.parquet" class="external-link">Apache
 Parquet file</a> (0.3 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav" class="external-link">IBM
-SPSS Statistics data file</a> (14.8 MB)<br>
+SPSS Statistics data file</a> (16 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta" class="external-link">Stata
-DTA file</a> (22.6 MB)</li>
+DTA file</a> (28.6 MB)</li>
 </ul>
 <p><strong>Example content</strong></p>
 <p>Example rows when filtering on <em>Enterobacter cloacae</em>:</p>
@@ -1060,8 +1077,16 @@ DTA file</a> (22.6 MB)</li>
 </tr>
 <tr class="odd">
 <td align="center">Enterobacter cloacae</td>
+<td align="center">Ostreogrycin</td>
+</tr>
+<tr class="even">
+<td align="center">Enterobacter cloacae</td>
 <td align="center">Pirlimycin</td>
 </tr>
+<tr class="odd">
+<td align="center">Enterobacter cloacae</td>
+<td align="center">Primycin</td>
+</tr>
 <tr class="even">
 <td align="center">Enterobacter cloacae</td>
 <td align="center">Pristinamycin</td>
@@ -1142,6 +1167,14 @@ DTA file</a> (22.6 MB)</li>
 <td align="center">Enterobacter cloacae</td>
 <td align="center">Vancomycin</td>
 </tr>
+<tr class="even">
+<td align="center">Enterobacter cloacae</td>
+<td align="center">Virginiamycine</td>
+</tr>
+<tr class="odd">
+<td align="center">Enterobacter cloacae</td>
+<td align="center">Zorbamycin</td>
+</tr>
 </tbody>
 </table>
 <hr>
@@ -1294,7 +1327,7 @@ column names:<br><em>date</em>, <em>patient</em>, <em>age</em>, <em>gender</em>,
 <em>MUP</em>, and <em>RIF</em>.</p>
 <p>This data set is in R available as <code>example_isolates</code>,
 after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 15 June 2024 13:33:49 UTC. Find more info
+<p>It was last updated on 9 February 2026 12:16:36 UTC. Find more info
 about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/example_isolates.html">data set
 here</a>.</p>
 <p><strong>Example content</strong></p>
@@ -1794,12 +1827,12 @@ set here</a>.</p>
 <h2 id="microorganisms-codes-common-laboratory-codes">
 <code>microorganisms.codes</code>: Common Laboratory Codes<a class="anchor" aria-label="anchor" href="#microorganisms-codes-common-laboratory-codes"></a>
 </h2>
-<p>A data set with 6 036 rows and 2 columns, containing the following
+<p>A data set with 6 029 rows and 2 columns, containing the following
 column names:<br><em>code</em> and <em>mo</em>.</p>
 <p>This data set is in R available as <code>microorganisms.codes</code>,
 after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 4 May 2025 16:50:25 UTC. Find more info about
-the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.codes.html">data
+<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
+about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.codes.html">data
 set here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
--- a/articles/datasets.md
+++ b/articles/datasets.md
@@ -0,0 +1,565 @@
+# Download data sets for download / own use
+
+All reference data (about microorganisms, antimicrobials, SIR
+interpretation, EUCAST rules, etc.) in this `AMR` package are reliable,
+up-to-date and freely available. We continually export our data sets to
+formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS,
+and Stata. We also provide tab-separated text files that are
+machine-readable and suitable for input in any software program, such as
+laboratory information systems.
+
+> If you are working in Python, be sure to use our [AMR for
+> Python](https://amr-for-r.org/articles/AMR_for_Python.html) package.
+> It allows all relevant AMR data sets to be natively available in
+> Python.
+
+## `microorganisms`: Full Microbial Taxonomy
+
+A data set with 96 982 rows and 28 columns, containing the following
+column names:  
+*mo*, *fullname*, *status*, *domain*, *kingdom*, *phylum*, *class*,
+*order*, *family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
+*oxygen_tolerance*, *morphology*, *source*, *lpsn*, *lpsn_parent*,
+*lpsn_renamed_to*, *mycobank*, *mycobank_parent*, *mycobank_renamed_to*,
+*gbif*, *gbif_parent*, *gbif_renamed_to*, *prevalence*, and *snomed*.
+
+This data set is in R available as `microorganisms`, after you load the
+`AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/microorganisms.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds)
+  (2.2 MB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt)
+  (23.1 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx)
+  (11.4 MB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather)
+  (11 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet)
+  (4.6 MB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav)
+  (35.2 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta)
+  (96.6 MB)
+
+**NOTE: The exported files for SPSS and Stata contain only the first 50
+SNOMED codes per record, as their file size would otherwise exceed 100
+MB; the file size limit of GitHub.** Their file structures and
+compression techniques are very inefficient. Advice? Use R instead. It’s
+free and much better in many ways.
+
+The tab-separated text file and Microsoft Excel workbook both contain
+all SNOMED codes as comma separated values.
+
+**Example content**
+
+Included (sub)species per taxonomic kingdom:
+
+|      Kingdom      | Number of (sub)species |
+|:-----------------:|:----------------------:|
+|                   |           20           |
+| (unknown kingdom) |           8            |
+|     Animalia      |         2 015          |
+|      Archaea      |          150           |
+|     Bacillati     |         24 200         |
+|     Bacteria      |           2            |
+
+First 6 rows when filtering on genus *Escherichia*:
+
+| mo | fullname | status | domain | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | morphology | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia |  |  | genus | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 515602 | 482 |  |  |  |  | CS33H | CRYWR |  | 1 | 407310004, 407251000, 407281008, … |
+| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata |  | species | Leclerc, 1962 | likely facultative anaerobe | rods | LPSN | 776052 | 515602 | 777447 |  |  |  | CS33J | CS33H | 3SVX6 | 1 |  |
+| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii |  | species | Huys et al., 2003 | facultative anaerobe | rods | LPSN | 776053 | 515602 |  |  |  |  | 3BGTB | CS33H |  | 1 | 419388003 |
+| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae |  | species | Burgess et al., 1973 | likely facultative anaerobe | rods | LPSN | 776056 | 515602 | 788468 |  |  |  | CS33K | CS33H | 4X4P7 | 1 |  |
+| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli |  | species | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 776057 | 515602 |  |  |  |  | NT3L7 | CS33H |  | 1 | 1095001000112106, 715307006, 737528008, … |
+| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies |  |  |  | GBIF |  | 776057 |  |  |  |  | 12233256 | NT3L7 |  | 1 |  |
+
+------------------------------------------------------------------------
+
+## `antimicrobials`: Antibiotic and Antifungal Drugs
+
+A data set with 505 rows and 14 columns, containing the following column
+names:  
+*ab*, *cid*, *name*, *group*, *atc*, *atc_group1*, *atc_group2*,
+*abbreviations*, *synonyms*, *oral_ddd*, *oral_units*, *iv_ddd*,
+*iv_units*, and *loinc*.
+
+This data set is in R available as `antimicrobials`, after you load the
+`AMR` package.
+
+It was last updated on 23 June 2026 12:38:59 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/antimicrobials.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.rds)
+  (44 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.txt)
+  (0.1 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.xlsx)
+  (79 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.feather)
+  (0.1 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.parquet)
+  (94 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.sav)
+  (0.4 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.dta)
+  (10 kB)
+
+The tab-separated text, Microsoft Excel, SPSS, and Stata files all
+contain the ATC codes, common abbreviations, trade names and LOINC codes
+as comma separated values.
+
+**Example content**
+
+| ab | cid | name | group | atc | atc_group1 | atc_group2 | abbreviations | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| AMK | 37768 | Amikacin | Aminoglycosides | D06AX12, J01GB06, QD06AX12, … | Aminoglycoside antibacterials | Other aminoglycosides | ak, ami, amik, … | amikacillin, amikacina, amikacine, … |  |  | 1.0 | g | 101493-5, 11-7, 12-5, … |
+| AMX | 33613 | Amoxicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA04, QG51AA03, QJ01CA04 | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | ac, amox, amoxic, … | acuotricina, alfamox, alfida, … | 1.5 | g | 3.0 | g | 101498-4, 15-8, 16-6, … |
+| AMC | 23665637 | Amoxicillin/clavulanic acid | Aminopenicillins, Penicillins, Beta-lactams, … | J01CR02, QJ01CR02 | Beta-lactam antibacterials, penicillins | Combinations of penicillins, incl. beta-lactamase inhibitors | a/c, amcl, aml, … | amocla, amoclan, amoclav, … | 1.5 | g | 3.0 | g |  |
+| AMP | 6249 | Ampicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA01, QJ01CA01, QJ51CA01, … | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | am, amp, amp100, … | adobacillin, alpen, amblosin, … | 2.0 | g | 6.0 | g | 101477-8, 101478-6, 18864-9, … |
+| AZM | 447043 | Azithromycin | Macrolides | J01FA10, QJ01FA10, QS01AA26, … | Macrolides, lincosamides and streptogramins | Macrolides | az, azi, azit, … | aritromicina, aruzilina, azasite, … | 0.3 | g | 0.5 | g | 100043-9, 16420-2, 16421-0, … |
+| PEN | 5904 | Benzylpenicillin | Penicillins, Beta-lactams | J01CE01, QJ01CE01, QJ51CE01, … | Combinations of antibacterials | Combinations of antibacterials | bepe, pen, peni, … | bencilpenicilina, benzopenicillin, benzylpenicilline, … |  |  | 3.6 | g |  |
+
+------------------------------------------------------------------------
+
+## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
+
+A data set with 45 555 rows and 14 columns, containing the following
+column names:  
+*guideline*, *type*, *host*, *method*, *site*, *mo*, *rank_index*, *ab*,
+*ref_tbl*, *disk_dose*, *breakpoint_S*, *breakpoint_R*, *uti*, and
+*is_SDD*.
+
+This data set is in R available as `clinical_breakpoints`, after you
+load the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/clinical_breakpoints.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds)
+  (92 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt)
+  (4.2 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.xlsx)
+  (2.7 MB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.feather)
+  (2 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet)
+  (0.1 MB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav)
+  (7.5 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.dta)
+  (12.6 MB)
+
+**Example content**
+
+| guideline | type | host | method | site | mo | mo_name | rank_index | ab | ab_name | ref_tbl | disk_dose | breakpoint_S | breakpoint_R | uti | is_SDD |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | 10 mcg | 26.000 | 20.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans |  | 1.000 | 4.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | 1.25/23.75 mcg | 26.000 | 26.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans |  | 0.125 | 0.125 | FALSE | FALSE |
+| EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | 30/6 mcg | 26.000 | 26.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans |  | 4.000 | 4.000 | FALSE | FALSE |
+
+------------------------------------------------------------------------
+
+## `microorganisms.groups`: Species Groups and Microbiological Complexes
+
+A data set with 530 rows and 4 columns, containing the following column
+names:  
+*mo_group*, *mo*, *mo_group_name*, and *mo_name*.
+
+This data set is in R available as `microorganisms.groups`, after you
+load the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/microorganisms.groups.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.rds)
+  (6 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.txt)
+  (50 kB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx)
+  (19 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather)
+  (19 kB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.parquet)
+  (13 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav)
+  (64 kB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta)
+  (82 kB)
+
+**Example content**
+
+| mo_group | mo | mo_group_name | mo_name |
+|:--:|:--:|:--:|:--:|
+| B_ACNTB_BMNN-C | B_ACNTB_BMNN | Acinetobacter baumannii complex | Acinetobacter baumannii |
+| B_ACNTB_BMNN-C | B_ACNTB_CLCC | Acinetobacter baumannii complex | Acinetobacter calcoaceticus |
+| B_ACNTB_BMNN-C | B_ACNTB_LCTC | Acinetobacter baumannii complex | Acinetobacter dijkshoorniae |
+| B_ACNTB_BMNN-C | B_ACNTB_NSCM | Acinetobacter baumannii complex | Acinetobacter nosocomialis |
+| B_ACNTB_BMNN-C | B_ACNTB_PITT | Acinetobacter baumannii complex | Acinetobacter pittii |
+| B_ACNTB_BMNN-C | B_ACNTB_SFRT | Acinetobacter baumannii complex | Acinetobacter seifertii |
+
+------------------------------------------------------------------------
+
+## `intrinsic_resistant`: Intrinsic Bacterial Resistance
+
+A data set with 294 079 rows and 2 columns, containing the following
+column names:  
+*mo* and *ab*.
+
+This data set is in R available as `intrinsic_resistant`, after you load
+the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/intrinsic_resistant.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.rds)
+  (0.1 MB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt)
+  (10.9 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx)
+  (3.1 MB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather)
+  (2.5 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.parquet)
+  (0.3 MB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav)
+  (16 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta)
+  (28.6 MB)
+
+**Example content**
+
+Example rows when filtering on *Enterobacter cloacae*:
+
+|    microorganism     |         antibiotic          |
+|:--------------------:|:---------------------------:|
+| Enterobacter cloacae |      Acetylmidecamycin      |
+| Enterobacter cloacae |      Acetylspiramycin       |
+| Enterobacter cloacae |         Amoxicillin         |
+| Enterobacter cloacae | Amoxicillin/clavulanic acid |
+| Enterobacter cloacae |         Ampicillin          |
+| Enterobacter cloacae |    Ampicillin/sulbactam     |
+| Enterobacter cloacae |          Avoparcin          |
+| Enterobacter cloacae |        Azithromycin         |
+| Enterobacter cloacae |      Benzylpenicillin       |
+| Enterobacter cloacae |          Bleomycin          |
+| Enterobacter cloacae |          Cadazolid          |
+| Enterobacter cloacae |         Cefadroxil          |
+| Enterobacter cloacae |          Cefalexin          |
+| Enterobacter cloacae |          Cefalotin          |
+| Enterobacter cloacae |          Cefazolin          |
+| Enterobacter cloacae |          Cefoxitin          |
+| Enterobacter cloacae |       Clarithromycin        |
+| Enterobacter cloacae |         Clindamycin         |
+| Enterobacter cloacae |         Cycloserine         |
+| Enterobacter cloacae |         Dalbavancin         |
+| Enterobacter cloacae |        Dirithromycin        |
+| Enterobacter cloacae |        Erythromycin         |
+| Enterobacter cloacae |       Flurithromycin        |
+| Enterobacter cloacae |        Fusidic acid         |
+| Enterobacter cloacae |        Gamithromycin        |
+| Enterobacter cloacae |          Josamycin          |
+| Enterobacter cloacae |         Kitasamycin         |
+| Enterobacter cloacae |         Lincomycin          |
+| Enterobacter cloacae |          Linezolid          |
+| Enterobacter cloacae |         Meleumycin          |
+| Enterobacter cloacae |         Midecamycin         |
+| Enterobacter cloacae |         Miocamycin          |
+| Enterobacter cloacae |        Nafithromycin        |
+| Enterobacter cloacae |        Norvancomycin        |
+| Enterobacter cloacae |        Oleandomycin         |
+| Enterobacter cloacae |         Oritavancin         |
+| Enterobacter cloacae |        Ostreogrycin         |
+| Enterobacter cloacae |         Pirlimycin          |
+| Enterobacter cloacae |          Primycin           |
+| Enterobacter cloacae |        Pristinamycin        |
+| Enterobacter cloacae |  Quinupristin/dalfopristin  |
+| Enterobacter cloacae |         Ramoplanin          |
+| Enterobacter cloacae |         Rifampicin          |
+| Enterobacter cloacae |         Rokitamycin         |
+| Enterobacter cloacae |        Roxithromycin        |
+| Enterobacter cloacae |        Solithromycin        |
+| Enterobacter cloacae |         Spiramycin          |
+| Enterobacter cloacae |          Tedizolid          |
+| Enterobacter cloacae |         Teicoplanin         |
+| Enterobacter cloacae |         Telavancin          |
+| Enterobacter cloacae |        Telithromycin        |
+| Enterobacter cloacae |        Thiacetazone         |
+| Enterobacter cloacae |        Tildipirosin         |
+| Enterobacter cloacae |         Tilmicosin          |
+| Enterobacter cloacae |       Troleandomycin        |
+| Enterobacter cloacae |        Tulathromycin        |
+| Enterobacter cloacae |           Tylosin           |
+| Enterobacter cloacae |         Tylvalosin          |
+| Enterobacter cloacae |         Vancomycin          |
+| Enterobacter cloacae |       Virginiamycine        |
+| Enterobacter cloacae |         Zorbamycin          |
+
+------------------------------------------------------------------------
+
+## `dosage`: Dosage Guidelines from EUCAST
+
+A data set with 759 rows and 9 columns, containing the following column
+names:  
+*ab*, *name*, *type*, *dose*, *dose_times*, *administration*, *notes*,
+*original_txt*, and *eucast_version*.
+
+This data set is in R available as `dosage`, after you load the `AMR`
+package.
+
+It was last updated on 20 April 2025 10:55:31 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/dosage.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.rds)
+  (4 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.txt)
+  (66 kB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.xlsx)
+  (37 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.feather)
+  (28 kB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.parquet)
+  (9 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.sav)
+  (97 kB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.dta)
+  (0.2 MB)
+
+**Example content**
+
+| ab | name | type | dose | dose_times | administration | notes | original_txt | eucast_version |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| AMK | Amikacin | standard_dosage | 25-30 mg/kg | 1 | iv |  | 25-30 mg/kg x 1 iv | 15 |
+| AMX | Amoxicillin | high_dosage | 2 g | 6 | iv |  | 2 g x 6 iv | 15 |
+| AMX | Amoxicillin | standard_dosage | 1 g | 3 | iv |  | 1 g x 3-4 iv | 15 |
+| AMX | Amoxicillin | high_dosage | 0.75-1 g | 3 | oral |  | 0.75-1 g x 3 oral | 15 |
+| AMX | Amoxicillin | standard_dosage | 0.5 g | 3 | oral |  | 0.5 g x 3 oral | 15 |
+| AMX | Amoxicillin | uncomplicated_uti | 0.5 g | 3 | oral |  | 0.5 g x 3 oral | 15 |
+
+------------------------------------------------------------------------
+
+## `example_isolates`: Example Data for Practice
+
+A data set with 2 000 rows and 46 columns, containing the following
+column names:  
+*date*, *patient*, *age*, *gender*, *ward*, *mo*, *PEN*, *OXA*, *FLC*,
+*AMX*, *AMC*, *AMP*, *TZP*, *CZO*, *FEP*, *CXM*, *FOX*, *CTX*, *CAZ*,
+*CRO*, *GEN*, *TOB*, *AMK*, *KAN*, *TMP*, *SXT*, *NIT*, *FOS*, *LNZ*,
+*CIP*, *MFX*, *VAN*, *TEC*, *TCY*, *TGC*, *DOX*, *ERY*, *CLI*, *AZM*,
+*IPM*, *MEM*, *MTR*, *CHL*, *COL*, *MUP*, and *RIF*.
+
+This data set is in R available as `example_isolates`, after you load
+the `AMR` package.
+
+It was last updated on 9 February 2026 12:16:36 UTC. Find more info
+about the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/example_isolates.html).
+
+**Example content**
+
+| date | patient | age | gender | ward | mo | PEN | OXA | FLC | AMX | AMC | AMP | TZP | CZO | FEP | CXM | FOX | CTX | CAZ | CRO | GEN | TOB | AMK | KAN | TMP | SXT | NIT | FOS | LNZ | CIP | MFX | VAN | TEC | TCY | TGC | DOX | ERY | CLI | AZM | IPM | MEM | MTR | CHL | COL | MUP | RIF |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| 2002-01-02 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R |  |  |  | I |  |  |  |  | I |  |  |  |  |  |  |  |  | R | R |  |  | R |  |  | R | R | R |  |  | R | R | R |  |  |  |  |  |  | R |
+| 2002-01-03 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R |  |  |  | I |  |  |  |  | I |  |  |  |  |  |  |  |  | R | R |  |  | R |  |  | R | R | R |  |  | R | R | R |  |  |  |  |  |  | R |
+| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | S | S |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
+| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | S | S |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
+| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | R |  |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
+| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | R |  |  |  |  |  |  | S |  | S | S | S | R | R | R |  |  |  |  | R |  |  |
+
+------------------------------------------------------------------------
+
+## `example_isolates_unclean`: Example Data for Practice
+
+A data set with 3 000 rows and 8 columns, containing the following
+column names:  
+*patient_id*, *hospital*, *date*, *bacteria*, *AMX*, *AMC*, *CIP*, and
+*GEN*.
+
+This data set is in R available as `example_isolates_unclean`, after you
+load the `AMR` package.
+
+It was last updated on 27 August 2022 18:49:37 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/example_isolates_unclean.html).
+
+**Example content**
+
+| patient_id | hospital |    date    |   bacteria    | AMX | AMC | CIP | GEN |
+|:----------:|:--------:|:----------:|:-------------:|:---:|:---:|:---:|:---:|
+|     J3     |    A     | 2012-11-21 |    E. coli    |  R  |  I  |  S  |  S  |
+|     R7     |    A     | 2018-04-03 | K. pneumoniae |  R  |  I  |  S  |  S  |
+|     P3     |    A     | 2014-09-19 |    E. coli    |  R  |  S  |  S  |  S  |
+|    P10     |    A     | 2015-12-10 |    E. coli    |  S  |  I  |  S  |  S  |
+|     B7     |    A     | 2015-03-02 |    E. coli    |  S  |  S  |  S  |  S  |
+|     W3     |    A     | 2018-03-31 |   S. aureus   |  R  |  S  |  R  |  S  |
+
+------------------------------------------------------------------------
+
+## `microorganisms.codes`: Common Laboratory Codes
+
+A data set with 6 029 rows and 2 columns, containing the following
+column names:  
+*code* and *mo*.
+
+This data set is in R available as `microorganisms.codes`, after you
+load the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/microorganisms.codes.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds)
+  (27 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt)
+  (0.1 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.xlsx)
+  (98 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.feather)
+  (0.1 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet)
+  (68 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav)
+  (0.2 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.dta)
+  (0.2 MB)
+
+**Example content**
+
+| code |      mo      |
+|:----:|:------------:|
+| 1011 |   B_GRAMP    |
+| 1012 |   B_GRAMP    |
+| 1013 |   B_GRAMN    |
+| 1014 |   B_GRAMN    |
+| 1015 |   F_YEAST    |
+| 103  | B_ESCHR_COLI |
+
+------------------------------------------------------------------------
+
+## `antivirals`: Antiviral Drugs
+
+A data set with 120 rows and 11 columns, containing the following column
+names:  
+*av*, *name*, *atc*, *cid*, *atc_group*, *synonyms*, *oral_ddd*,
+*oral_units*, *iv_ddd*, *iv_units*, and *loinc*.
+
+This data set is in R available as `antivirals`, after you load the
+`AMR` package.
+
+It was last updated on 20 October 2023 12:51:48 UTC. Find more info
+about the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/antimicrobials.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.rds)
+  (6 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.txt)
+  (17 kB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.xlsx)
+  (16 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.feather)
+  (16 kB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.parquet)
+  (13 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.sav)
+  (32 kB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.dta)
+  (78 kB)
+
+The tab-separated text, Microsoft Excel, SPSS, and Stata files all
+contain the trade names and LOINC codes as comma separated values.
+
+**Example content**
+
+| av | name | atc | cid | atc_group | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| ABA | Abacavir | J05AF06 | 441300 | Nucleoside and nucleotide reverse transcriptase inhibitors | abacavir sulfate, avacavir, ziagen | 0.6 | g |  |  | 29113-8, 30273-7, 30287-7, … |
+| ACI | Aciclovir | J05AB01 | 135398513 | Nucleosides and nucleotides excl. reverse transcriptase inhibitors | acicloftal, aciclovier, aciclovirum, … | 4.0 | g | 4 | g |  |
+| ADD | Adefovir dipivoxil | J05AF08 | 60871 | Nucleoside and nucleotide reverse transcriptase inhibitors | adefovir di, adefovir di ester, adefovir dipivoxyl, … | 10.0 | mg |  |  |  |
+| AME | Amenamevir | J05AX26 | 11397521 | Other antivirals | amenalief | 0.4 | g |  |  |  |
+| AMP | Amprenavir | J05AE05 | 65016 | Protease inhibitors | agenerase, carbamate, prozei | 1.2 | g |  |  | 29114-6, 30296-8, 30297-6, … |
+| ASU | Asunaprevir | J05AP06 | 16076883 | Antivirals for treatment of HCV infections | sunvepra, sunvepratrade | 0.2 | g |  |  |  |
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+++ b/articles/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.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/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/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><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></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>Articles • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/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><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>


@@ -7,7 +7,7 @@

    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
--- a/articles/index.md
+++ b/articles/index.md
@@ -0,0 +1,16 @@
+# Articles
+
+### All vignettes
+
+- [AMR for Python](https://amr-for-r.org/articles/AMR_for_Python.md):
+- [AMR with
+  tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.md):
+- [Conduct AMR data analysis](https://amr-for-r.org/articles/AMR.md):
+- [Download data sets for download / own
+  use](https://amr-for-r.org/articles/datasets.md):
+- [Apply EUCAST rules](https://amr-for-r.org/articles/EUCAST.md):
+- [Conduct principal component analysis (PCA) for
+  AMR](https://amr-for-r.org/articles/PCA.md):
+- [Work with WHONET data](https://amr-for-r.org/articles/WHONET.md):
+- [Estimating Empirical Coverage with
+  WISCA](https://amr-for-r.org/articles/WISCA.md):
--- a/authors.html
+++ b/authors.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.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/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/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><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></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>Authors and Citation • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/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><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>


@@ -7,7 +7,7 @@

    <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9011</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</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">
@@ -135,7 +135,7 @@
          </p>
        </li>
        <li>
-          <p><strong>Matthew Saab</strong>. Contributor.
+          <p><strong>Matthew Saab</strong>. Contributor. <a href="https://orcid.org/0009-0008-6626-7919" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
@@ -147,7 +147,7 @@
          </p>
        </li>
        <li>
-          <p><strong>Rogier P. Schade</strong>. Contributor.
+          <p><strong>Rogier P. Schade</strong>. Contributor. <a href="https://orcid.org/0000-0002-9487-4467" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
--- a/authors.md
+++ b/authors.md
@@ -0,0 +1,106 @@
+# Authors and Citation
+
+## Authors
+
+- **Matthijs S. Berends**. Author, maintainer.
+  [](https://orcid.org/0000-0001-7620-1800)
+
+- **Dennis Souverein**. Author, contributor.
+  [](https://orcid.org/0000-0003-0455-0336)
+
+- **Erwin E. A. Hassing**. Author, contributor.
+
+- **Aislinn Cook**. Contributor.
+  [](https://orcid.org/0000-0002-9189-7815)
+
+- **Andrew P. Norgan**. Contributor.
+  [](https://orcid.org/0000-0002-2955-2066)
+
+- **Anita Williams**. Contributor.
+  [](https://orcid.org/0000-0002-5295-8451)
+
+- **Annick Lenglet**. Contributor.
+  [](https://orcid.org/0000-0003-2013-8405)
+
+- **Anthony Underwood**. Contributor.
+  [](https://orcid.org/0000-0002-8547-4277)
+
+- **Anton Mymrikov**. Contributor.
+
+- **Bart C. Meijer**. Contributor.
+
+- **Christian F. Luz**. Contributor.
+  [](https://orcid.org/0000-0001-5809-5995)
+
+- **Dmytro Mykhailenko**. Contributor.
+
+- **Eric H. L. C. M. Hazenberg**. Contributor.
+
+- **Gwen Knight**. Contributor.
+  [](https://orcid.org/0000-0002-7263-9896)
+
+- **Jane Hawkey**. Contributor.
+  [](https://orcid.org/0000-0001-9661-5293)
+
+- **Jason Stull**. Contributor.
+  [](https://orcid.org/0000-0002-9028-8153)
+
+- **Javier Sanchez**. Contributor.
+  [](https://orcid.org/0000-0003-2605-8094)
+
+- **Jonas Salm**. Contributor.
+
+- **Judith M. Fonville**. Contributor.
+
+- **Kathryn Holt**. Contributor.
+  [](https://orcid.org/0000-0003-3949-2471)
+
+- **Larisse Bolton**. Contributor.
+  [](https://orcid.org/0000-0001-7879-2173)
+
+- **Matthew Saab**. Contributor.
+  [](https://orcid.org/0009-0008-6626-7919)
+
+- **Natacha Couto**. Contributor.
+  [](https://orcid.org/0000-0002-9152-5464)
+
+- **Peter Dutey-Magni**. Contributor.
+  [](https://orcid.org/0000-0002-8942-9836)
+
+- **Rogier P. Schade**. Contributor.
+  [](https://orcid.org/0000-0002-9487-4467)
+
+- **Sofia Ny**. Contributor. [](https://orcid.org/0000-0002-2017-1363)
+
+- **Alex W. Friedrich**. Thesis advisor.
+  [](https://orcid.org/0000-0003-4881-038X)
+
+- **Bhanu N. M. Sinha**. Thesis advisor.
+  [](https://orcid.org/0000-0003-1634-0010)
+
+- **Casper J. Albers**. Thesis advisor.
+  [](https://orcid.org/0000-0002-9213-6743)
+
+- **Corinna Glasner**. Thesis advisor.
+  [](https://orcid.org/0000-0003-1241-1328)
+
+## Citation
+
+Source:
+[`inst/CITATION`](https://github.com/msberends/AMR/blob/main/inst/CITATION)
+
+Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C
+(2022). “AMR: An R Package for Working with Antimicrobial Resistance
+Data.” *Journal of Statistical Software*, **104**(3), 1–31.
+[doi:10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03).
+
+    @Article{,
+      title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
+      author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
+      journal = {Journal of Statistical Software},
+      year = {2022},
+      volume = {104},
+      number = {3},
+      pages = {1--31},
+      doi = {10.18637/jss.v104.i03},
+    }
--- a/deps/Fira_Code-0.4.10/uU9eCBsR6Z2vfE9aq3bL0fxyUs4tcw4W_D1sJVD7Ng.woff2
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+++ b/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map
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--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
github-actions	2a7bfb9ffb	Built site for AMR@3.0.1.9061: `0c1709c`	2026-06-23 18:00:14 +00:00
github-actions	9447e0f2c2	Built site for AMR@3.0.1.9057: `0af3f84`	2026-05-02 13:06:13 +00:00
github-actions	6fbd9e4b9f	Built site for AMR@3.0.1.9055: `f7e9294`	2026-04-30 17:52:11 +00:00
github-actions	3a3027f171	Built site for AMR@3.0.1.9053: `23beebc`	2026-04-30 08:07:42 +00:00
github-actions	425f4ad827	Built site for AMR@3.0.1.9052: `3f1b20c`	2026-04-25 14:29:32 +00:00
github-actions	e12355b4a8	Built site for AMR@3.0.1.9050: `8261b91`	2026-04-25 12:49:42 +00:00
github-actions	cc4e9bab4b	Built site for AMR@3.0.1.9050: `19157ce`	2026-04-24 22:45:21 +00:00
github-actions	fdef91486c	Built site for AMR@3.0.1.9048: `e7780b6`	2026-04-22 06:33:22 +00:00
github-actions	5dec3cfd25	Built site for AMR@3.0.1.9047: `e0f8cf0`	2026-04-21 20:32:21 +00:00
github-actions	86b5ebf61f	Built site for AMR@3.0.1.9045: `8ff5d44`	2026-04-21 20:03:58 +00:00
github-actions	f2e56dbc61	Built site for AMR@3.0.1.9044: `fb8758f`	2026-04-05 15:36:36 +00:00
github-actions	6d9934ac04	Built site for AMR@3.0.1.9044: `6c24718`	2026-04-04 10:03:48 +00:00
github-actions	59b2ce5621	Built site for AMR@3.0.1.9043: `2254931`	2026-04-02 10:32:06 +00:00
github-actions	d0fd6aa2cc	Built site for AMR@3.0.1.9042: `26613d7`	2026-04-02 09:54:05 +00:00
github-actions	d6c59896e9	Built site for AMR@3.0.1.9041: `3a736bc`	2026-03-30 08:18:09 +00:00
github-actions	d55d073ae9	Built site for AMR@3.0.1.9040: `9c95aa4`	2026-03-24 12:34:17 +00:00
github-actions	330f1a9dfe	Built site for AMR@3.0.1.9038: `975a690`	2026-03-22 21:31:11 +00:00
github-actions	c95646d39c	Built site for AMR@3.0.1.9037: `3d1412e`	2026-03-22 19:55:49 +00:00
github-actions	3560dfd611	Built site for AMR@3.0.1.9036: `4171d5b`	2026-03-20 16:11:30 +00:00
github-actions	ba003f3e23	Built site for AMR@3.0.1.9035: `8439e9c`	2026-03-18 09:45:05 +00:00
github-actions	33e262ed9b	Built site for AMR@3.0.1.9034: `4dc3ec0`	2026-03-11 15:20:20 +00:00
github-actions	86a53a0901	Built site for AMR@3.0.1.9033: `353eaa3`	2026-03-09 11:08:33 +00:00
github-actions	e7e8aa636e	Built site for AMR@3.0.1.9032: `cba315c`	2026-03-08 19:46:45 +00:00
github-actions	1e81c2a013	Built site for AMR@3.0.1.9031: `b6f8584`	2026-03-08 10:40:48 +00:00
github-actions	ab5ce59112	Built site for AMR@3.0.1.9030: `e2102c0`	2026-03-07 17:15:05 +00:00
github-actions	599302f0bd	Built site for AMR@3.0.1.9026: `60e8f2b`	2026-03-06 11:55:19 +00:00
github-actions	acc807a02c	Built site for AMR@3.0.1.9023: `4e3ea95`	2026-03-04 08:11:02 +00:00
github-actions	3c74d99480	Built site for AMR@3.0.1.9022: `0311834`	2026-03-03 14:52:15 +00:00
github-actions	6f0dc96d0d	Built site for AMR@3.0.1.9021: `2c21eba`	2026-02-27 16:25:41 +00:00
github-actions	f0ec851466	Built site for AMR@3.0.1.9021: `12cf144`	2026-02-12 19:45:10 +00:00
github-actions	c149bb1f65	Built site for AMR@3.0.1.9020: `499c830`	2026-02-09 12:53:52 +00:00
github-actions	8b71d0eb99	Built site for AMR@3.0.1.9019: `ba4c159`	2026-02-08 22:40:23 +00:00
github-actions	0551ea51cb	Built site for AMR@3.0.1.9018: `2df2911`	2026-01-16 10:08:12 +00:00
github-actions	86f19c7576	Built site for AMR@3.0.1.9017: `fd50c51`	2026-01-08 13:12:14 +00:00
github-actions	39894d56c9	Built site for AMR@3.0.1.9016: `cfd1922`	2026-01-08 11:36:31 +00:00
github-actions	a4edc69618	Built site for AMR@3.0.1.9015: `7df28bc`	2026-01-08 09:35:29 +00:00
github-actions	e2ba695c2c	Built site for AMR@3.0.1.9014: `7b9c151`	2026-01-07 14:17:42 +00:00
github-actions	bb2e68890c	Built site for AMR@3.0.1.9013: `85e8e81`	2026-01-07 12:40:00 +00:00
github-actions	70d4d19168	Built site for AMR@3.0.1.9012: `fa827f2`	2026-01-07 10:08:49 +00:00
github-actions	46858ec0a2	Built site for AMR@3.0.1.9008: `9d1b456`	2026-01-06 22:21:25 +00:00
github-actions	01880ee020	Built site for AMR@3.0.1.9009: `35debe2`	2025-12-23 10:12:20 +00:00
github-actions	1227e2c60c	Built site for AMR@3.0.1.9007: `a5c6aa9`	2025-12-22 08:48:41 +00:00
github-actions	7c8d0f82f9	Built site for AMR@3.0.1.9004: `225c73f`	2025-12-15 12:32:31 +00:00
github-actions	141fc468f8	Built site for AMR@3.0.1.9003: `ba30b08`	2025-11-24 10:42:21 +00:00
github-actions	7d16891987	Built site for AMR@3.0.1.9002: `d366949`	2025-10-13 20:22:22 +00:00
github-actions	0d195202fd	Built site for AMR@3.0.1.9001: `0b24967`	2025-09-30 09:09:19 +00:00
github-actions	2d20ac1a7d	Built site for AMR@3.0.1: `adee419`	2025-09-20 16:24:20 +00:00
github-actions	dc9493d2d8	Built site for AMR@3.0.0.9036: `33fb184`	2025-09-19 11:39:04 +00:00
github-actions	a33b95eb3d	Built site for AMR@3.0.0.9035: `13f2a86`	2025-09-18 13:33:09 +00:00
github-actions	f4ce3570ff	Built site for AMR@3.0.0.9034: `10ba368`	2025-09-18 13:08:52 +00:00
github-actions	32361053f1	Built site for AMR@3.0.0.9033: `5796e8f`	2025-09-15 07:19:27 +00:00
github-actions	660bcf00e5	Built site for AMR@3.0.0.9032: `b11866a`	2025-09-13 12:12:30 +00:00
github-actions	cf182a28f1	Built site for AMR@3.0.0.9031: `e8c99f2`	2025-09-12 15:01:39 +00:00
github-actions	ccfe6caf58	Built site for AMR@3.0.0.9030: `5b99888`	2025-09-11 12:48:56 +00:00
github-actions	be9c71b572	Built site for AMR@3.0.0.9029: `c7b2acb`	2025-09-10 14:30:52 +00:00
github-actions	d1f9877a8e	Built site for AMR@3.0.0.9021: `0744c6f`	2025-09-03 10:21:26 +00:00
github-actions	75b8c2485a	Built site for AMR@3.0.0.9019: `eca6385`	2025-09-03 09:56:20 +00:00
github-actions	43285cb936	Built site for AMR@3.0.0.9019: `60bd631`	2025-09-01 15:07:43 +00:00
github-actions	4542802e26	Built site for AMR@3.0.0.9018: `9b07a85`	2025-08-07 14:35:23 +00:00
github-actions	e8a82b62b7	Built site for AMR@3.0.0.9017: `fc72cf9`	2025-07-28 10:32:15 +00:00
github-actions	501395d46e	Built site for AMR@3.0.0.9016: `2f86698`	2025-07-23 20:25:40 +00:00
github-actions	a09b81fb58	Built site for AMR@3.0.0.9015: `6cb724a`	2025-07-19 12:16:34 +00:00
github-actions	3be689a8db	Built site for AMR@3.0.0.9014: `49274f0`	2025-07-18 14:05:50 +00:00
github-actions	7f9aacb56b	Built site for AMR@3.0.0.9012: `8da0f52`	2025-07-17 21:05:56 +00:00
github-actions	5791bfb45d	Built site for AMR@3.0.0.9012: `68442f3`	2025-07-17 17:54:16 +00:00