From 47e776b69c159d991cae5614f8786ddedadda682 Mon Sep 17 00:00:00 2001
From: Tom Palmer <remlapmot@hotmail.com>
Date: Wed, 24 Jul 2024 12:08:46 +0100
Subject: [PATCH] quarto render site

---
 docs/about.html                               |   73 +-
 .../markstat-call-R-example/index.html        |  344 +-
 docs/examples/mrrobust-examples/index.html    |  127 +-
 .../mrrobust-examples/mreggerplot.svg         | 1080 +++--
 .../mrrobust-examples/mreggersimex-plot.svg   |  882 ++--
 docs/examples/mrrobust-examples/mrforest.svg  | 1515 +++++--
 docs/examples/mrrobust-examples/mrfunnel.svg  | 1617 ++++---
 .../mrleaveoneout-plot-01.svg                 |  464 +-
 .../mrleaveoneout-plot-02.svg                 |  464 +-
 .../mrrobust-examples/mrmodalplot.svg         |  319 +-
 docs/examples/mvcommands-example/index.html   |   93 +-
 .../rmarkdown-call-stata-example/index.html   |  252 +-
 docs/examples/save-estimates/index.html       |  149 +-
 .../spiller-ije-2018-examples/index.html      |  117 +-
 .../mreggerplot-bmi.svg                       | 1064 +++--
 .../mreggerplot-height.svg                    | 3999 +++++++++--------
 .../mrmodalplot-bmi.svg                       |  322 +-
 .../mrmodalplot-height.svg                    |  324 +-
 docs/helpfiles/mr-helpfile.html               |   73 +-
 docs/helpfiles/mrdeps-helpfile.html           |   73 +-
 docs/helpfiles/mregger-helpfile.html          |   73 +-
 docs/helpfiles/mreggerplot-helpfile.html      |   73 +-
 docs/helpfiles/mreggersimex-helpfile.html     |   73 +-
 docs/helpfiles/mrforest-helpfile.html         |   73 +-
 docs/helpfiles/mrfunnel-helpfile.html         |   73 +-
 docs/helpfiles/mrivests-helpfile.html         |   73 +-
 docs/helpfiles/mrleaveoneout-helpfile.html    |   73 +-
 docs/helpfiles/mrmedian-helpfile.html         |   73 +-
 docs/helpfiles/mrmedianobs-helpfile.html      |   73 +-
 docs/helpfiles/mrmodal-helpfile.html          |   73 +-
 docs/helpfiles/mrmodalplot-helpfile.html      |   73 +-
 docs/helpfiles/mrmvegger-helpfile.html        |   73 +-
 docs/helpfiles/mrmvivw-helpfile.html          |   73 +-
 docs/helpfiles/mrratio-helpfile.html          |   73 +-
 docs/helpfiles/mrrobust-helpfile.html         |   73 +-
 docs/index.html                               |   75 +-
 docs/search.json                              |  340 +-
 docs/site_libs/bootstrap/bootstrap.min.css    |    4 +-
 .../quarto-syntax-highlighting.css            |    2 +
 docs/site_libs/quarto-html/quarto.js          |   15 +-
 docs/site_libs/quarto-nav/quarto-nav.js       |   37 +
 docs/site_libs/quarto-search/quarto-search.js |   69 +-
 .../index/execute-results/html.json           |   16 -
 .../index/execute-results/html.json           |   16 -
 .../index/execute-results/html.json           |   16 -
 .../index/execute-results/html.json           |   16 -
 .../index/execute-results/html.json           |   14 -
 .../index/execute-results/html.json           |   16 -
 site/_freeze/index/execute-results/html.json  |   17 -
 .../markstat-call-R-example/index.qmd         |    1 +
 .../mrrobust-examples/mreggerplot.svg         | 1080 +++--
 .../mrrobust-examples/mreggersimex-plot.svg   |  882 ++--
 site/examples/mrrobust-examples/mrforest.svg  | 1515 +++++--
 site/examples/mrrobust-examples/mrfunnel.svg  | 1617 ++++---
 .../mrleaveoneout-plot-01.svg                 |  464 +-
 .../mrleaveoneout-plot-02.svg                 |  464 +-
 .../mrrobust-examples/mrmodalplot.svg         |  319 +-
 .../rmarkdown-call-stata-example/index.qmd    |    8 +-
 .../mreggerplot-bmi.svg                       | 1064 +++--
 .../mreggerplot-height.svg                    | 3999 +++++++++--------
 .../mrmodalplot-bmi.svg                       |  322 +-
 .../mrmodalplot-height.svg                    |  324 +-
 62 files changed, 16736 insertions(+), 10422 deletions(-)
 delete mode 100644 site/_freeze/examples/markstat-call-R-example/index/execute-results/html.json
 delete mode 100644 site/_freeze/examples/mrrobust-examples/index/execute-results/html.json
 delete mode 100644 site/_freeze/examples/mvcommands-example/index/execute-results/html.json
 delete mode 100644 site/_freeze/examples/rmarkdown-call-stata-example/index/execute-results/html.json
 delete mode 100644 site/_freeze/examples/save-estimates/index/execute-results/html.json
 delete mode 100644 site/_freeze/examples/spiller-ije-2018-examples/index/execute-results/html.json
 delete mode 100644 site/_freeze/index/execute-results/html.json

diff --git a/docs/about.html b/docs/about.html
index 8d9f8fbc..1a40e5cd 100644
--- a/docs/about.html
+++ b/docs/about.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust - About</title>
+<title>About – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -288,18 +288,7 @@ <h1 class="title">About</h1>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -331,7 +320,47 @@ <h1 class="title">About</h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -366,7 +395,11 @@ <h1 class="title">About</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/examples/markstat-call-R-example/index.html b/docs/examples/markstat-call-R-example/index.html
index 1e7862ed..3cc88104 100644
--- a/docs/examples/markstat-call-R-example/index.html
+++ b/docs/examples/markstat-call-R-example/index.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>index – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -137,7 +137,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -147,7 +147,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -178,7 +178,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -265,7 +265,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -279,7 +279,7 @@
     <h2 id="toc-title">On this page</h2>
    
   <ul>
-  <li><a href="#title" id="toc-title" class="nav-link active" data-scroll-target="#title">Run MR-Base from a Stata do-file or Stata Markdown script</a>
+  <li><a href="#title" id="toc-title" class="nav-link active" data-scroll-target="#title">Access the OpenGWAS API from a Stata do-file or Stata Markdown script</a>
   <ul class="collapse">
   <li><a href="#introduction" id="toc-introduction" class="nav-link" data-scroll-target="#introduction">Introduction</a></li>
   <li><a href="#example-stata-markdown-document" id="toc-example-stata-markdown-document" class="nav-link" data-scroll-target="#example-stata-markdown-document">Example Stata Markdown document</a></li>
@@ -295,11 +295,12 @@ <h2 id="toc-title">On this page</h2>
 
 
 <section id="title" class="level1">
-<h1>Run MR-Base from a Stata do-file or Stata Markdown script</h1>
+<h1>Access the OpenGWAS API from a Stata do-file or Stata Markdown script</h1>
 <section id="introduction" class="level2">
 <h2 class="anchored" data-anchor-id="introduction">Introduction</h2>
-<p>This example shows how to run R and Stata code within the same Stata Markdown (<code>.stmd</code>) script. The general approach is detailed on the Stata Markdown website <a href="https://data.princeton.edu/stata/markdown/quantiles">here</a> and <a href="https://data.princeton.edu/stata/markdown/gettingStarted#R">here</a>.</p>
-<p>This means that we can use the functions provided by the <a href="https://github.com/MRCIEU/TwoSampleMR"><strong>TwoSampleMR</strong></a> package to obtain data from <a href="https://www.mrbase.org/">MR-Base</a> <span class="citation" data-cites="mrbase">(<a href="#ref-mrbase" role="doc-biblioref">Hemani et al. 2018</a>)</span>.</p>
+<p>This example shows how to run R and Stata code within the same Stata Markdown (<code>.stmd</code>) script. The general approach is detailed on the Stata Markdown website <a href="https://grodri.github.io/markstat/quantiles">here</a> and <a href="https://grodri.github.io/markstat/gettingStarted#R">here</a>.</p>
+<p>This means that we can use the functions provided by the <a href="https://github.com/MRCIEU/TwoSampleMR"><strong>TwoSampleMR</strong></a> package to obtain data from the [OpenGWAS API}(https://api.opengwas.io/) which is the successor to <a href="https://www.mrbase.org/">MR-Base</a> <span class="citation" data-cites="mrbase">(<a href="#ref-mrbase" role="doc-biblioref">Hemani et al. 2018</a>)</span>.</p>
+<p>The OpenGWAS API requires authentication. You must place an <code>OPENGWAS_JWT</code> token (by creating a free account on <a href="https://api.opengwas.io/profile/" class="uri">https://api.opengwas.io/profile/</a>) in your <em>.Renviron</em> file before running the code below. See the <a href="https://mrcieu.github.io/ieugwasr/articles/guide.html#authentication"><strong>ieugwasr</strong> website</a> for more information.</p>
 <p>Before you start please install the following two Stata packages from the SSC archive, so in Stata issue the following commands (I have commented them out because I have already installed them).</p>
 <div class="sourceCode" id="cb1"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="kw">ssc</span> install whereis</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="kw">ssc</span> install markstat</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -312,140 +313,152 @@ <h2 class="anchored" data-anchor-id="example-stata-markdown-document">Example St
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
 <span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>We first need to register the R executable with Stata.</span>
-<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="in">if c(os) == "Windows" local rpath "C:\\Program Files\\R\\R-4.3.0\\bin\\x64\\R.exe"</span></span>
-<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="in">else if c(os) == "Unix" local rpath "/usr/bin/R"</span></span>
-<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="in">else local rpath "/usr/local/bin/R"</span></span>
-<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="in">whereis R "`rpath'"</span></span>
-<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
+<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="in">if c(os) == "Windows" local rpath "C:\\Program Files\\R\\R-4.4.1\\bin\\x64\\R.exe"</span></span>
+<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="in">else if c(os) == "Unix" local rpath "/usr/bin/R"</span></span>
+<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="in">else local rpath "/usr/local/bin/R"</span></span>
+<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a><span class="in">whereis R "`rpath'"</span></span>
+<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a>Next we have an R code chunk in which we install the required packages in R.</span>
-<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
-<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"remotes"</span>)</span>
-<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"MRCIEU/TwoSampleMR"</span>)</span>
-<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"MRCIEU/MRInstruments"</span>)</span>
-<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a><span class="fu">## Extracting data from MR-Base</span></span>
-<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a>We will be running the script from the MR-Base paper </span>
-<span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a>(<span class="co">[</span><span class="ot">Hemani et al., 2018</span><span class="co">](https://doi.org/10.7554/eLife.34408)</span>). </span>
-<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a>The R code we will use is from </span>
-<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a><span class="co">[</span><span class="ot">here</span><span class="co">](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R)</span>.</span>
-<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a>First, we load the packages into our R session.</span>
-<span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a>Note that the **foreign** package provides the </span>
-<span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a><span class="in">`write.dta()`</span> function which we will use to save the data in Stata format.</span>
-<span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
-<span id="cb2-32"><a href="#cb2-32" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(TwoSampleMR)</span>
-<span id="cb2-33"><a href="#cb2-33" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(MRInstruments)</span>
-<span id="cb2-34"><a href="#cb2-34" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(foreign)</span>
-<span id="cb2-35"><a href="#cb2-35" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-36"><a href="#cb2-36" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-37"><a href="#cb2-37" aria-hidden="true" tabindex="-1"></a>Our edited version of the code starts by reading in some code</span>
-<span id="cb2-38"><a href="#cb2-38" aria-hidden="true" tabindex="-1"></a>to generate a set of plots in R.</span>
-<span id="cb2-39"><a href="#cb2-39" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
-<span id="cb2-40"><a href="#cb2-40" aria-hidden="true" tabindex="-1"></a><span class="fu">source</span>(<span class="st">"mrplots.R"</span>)</span>
-<span id="cb2-41"><a href="#cb2-41" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-42"><a href="#cb2-42" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-43"><a href="#cb2-43" aria-hidden="true" tabindex="-1"></a>We can access the data using the **MRInstruments** package.</span>
-<span id="cb2-44"><a href="#cb2-44" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
-<span id="cb2-45"><a href="#cb2-45" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(gwas_catalog)</span>
-<span id="cb2-46"><a href="#cb2-46" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-47"><a href="#cb2-47" aria-hidden="true" tabindex="-1"></a><span class="co"># Get published SNPs for LDL cholesterol</span></span>
-<span id="cb2-48"><a href="#cb2-48" aria-hidden="true" tabindex="-1"></a>ldl_snps <span class="ot">&lt;-</span> </span>
-<span id="cb2-49"><a href="#cb2-49" aria-hidden="true" tabindex="-1"></a>  <span class="fu">subset</span>(gwas_catalog, </span>
-<span id="cb2-50"><a href="#cb2-50" aria-hidden="true" tabindex="-1"></a>    <span class="fu">grepl</span>(<span class="st">"LDL choles"</span>, Phenotype) <span class="sc">&amp;</span> Author <span class="sc">==</span> <span class="st">"Willer CJ"</span>)<span class="sc">$</span>SNP</span>
-<span id="cb2-51"><a href="#cb2-51" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-52"><a href="#cb2-52" aria-hidden="true" tabindex="-1"></a><span class="co"># Extract from GLGC dataset</span></span>
-<span id="cb2-53"><a href="#cb2-53" aria-hidden="true" tabindex="-1"></a>exposure <span class="ot">&lt;-</span> </span>
-<span id="cb2-54"><a href="#cb2-54" aria-hidden="true" tabindex="-1"></a>  <span class="fu">convert_outcome_to_exposure</span>(</span>
-<span id="cb2-55"><a href="#cb2-55" aria-hidden="true" tabindex="-1"></a>    <span class="fu">extract_outcome_data</span>(ldl_snps, <span class="st">"ieu-a-300"</span>))</span>
-<span id="cb2-56"><a href="#cb2-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-57"><a href="#cb2-57" aria-hidden="true" tabindex="-1"></a><span class="co"># Get outcome data from Cardiogram 2015</span></span>
-<span id="cb2-58"><a href="#cb2-58" aria-hidden="true" tabindex="-1"></a>outcome <span class="ot">&lt;-</span> <span class="fu">extract_outcome_data</span>(exposure<span class="sc">$</span>SNP, <span class="st">"ieu-a-7"</span>)</span>
-<span id="cb2-59"><a href="#cb2-59" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-60"><a href="#cb2-60" aria-hidden="true" tabindex="-1"></a><span class="co"># Harmonise exposure and outcome datasets</span></span>
-<span id="cb2-61"><a href="#cb2-61" aria-hidden="true" tabindex="-1"></a><span class="co"># Assume alleles are on the forward strand</span></span>
-<span id="cb2-62"><a href="#cb2-62" aria-hidden="true" tabindex="-1"></a>dat <span class="ot">&lt;-</span> <span class="fu">harmonise_data</span>(exposure, outcome, <span class="at">action =</span> <span class="dv">1</span>)</span>
-<span id="cb2-63"><a href="#cb2-63" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-64"><a href="#cb2-64" aria-hidden="true" tabindex="-1"></a>At this point we have our harmonised genotype-exposure and </span>
-<span id="cb2-65"><a href="#cb2-65" aria-hidden="true" tabindex="-1"></a>genotype-outcome association data saved in an object in our </span>
-<span id="cb2-66"><a href="#cb2-66" aria-hidden="true" tabindex="-1"></a>R session called <span class="in">`dat`</span>.</span>
-<span id="cb2-67"><a href="#cb2-67" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-68"><a href="#cb2-68" aria-hidden="true" tabindex="-1"></a>The next two code chunks perform the analysis in R.</span>
-<span id="cb2-69"><a href="#cb2-69" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
-<span id="cb2-70"><a href="#cb2-70" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform MR</span></span>
-<span id="cb2-71"><a href="#cb2-71" aria-hidden="true" tabindex="-1"></a><span class="fu">mr</span>(dat)</span>
-<span id="cb2-72"><a href="#cb2-72" aria-hidden="true" tabindex="-1"></a><span class="fu">mr_heterogeneity</span>(dat)</span>
-<span id="cb2-73"><a href="#cb2-73" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>exposure <span class="ot">&lt;-</span> <span class="st">"LDL cholesterol"</span></span>
-<span id="cb2-74"><a href="#cb2-74" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>outcome <span class="ot">&lt;-</span> <span class="st">"Coronary heart disease"</span></span>
-<span id="cb2-75"><a href="#cb2-75" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-76"><a href="#cb2-76" aria-hidden="true" tabindex="-1"></a><span class="co"># Label outliers and create plots</span></span>
-<span id="cb2-77"><a href="#cb2-77" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>labels <span class="ot">&lt;-</span> dat<span class="sc">$</span>SNP</span>
-<span id="cb2-78"><a href="#cb2-78" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>labels[<span class="sc">!</span> dat<span class="sc">$</span>SNP <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"rs11065987"</span>, <span class="st">"rs1250229"</span>, <span class="st">"rs4530754"</span>)] <span class="ot">&lt;-</span> <span class="cn">NA</span></span>
-<span id="cb2-79"><a href="#cb2-79" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-80"><a href="#cb2-80" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-81"><a href="#cb2-81" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
-<span id="cb2-82"><a href="#cb2-82" aria-hidden="true" tabindex="-1"></a><span class="fu">png</span>(<span class="st">"ldl-chd.png"</span>, <span class="at">width=</span><span class="dv">1000</span>, <span class="at">height=</span><span class="dv">1000</span>)</span>
-<span id="cb2-83"><a href="#cb2-83" aria-hidden="true" tabindex="-1"></a><span class="fu">mr_plots</span>(dat)</span>
-<span id="cb2-84"><a href="#cb2-84" aria-hidden="true" tabindex="-1"></a><span class="fu">dev.off</span>()</span>
+<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
+<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"remotes"</span>)</span>
+<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"MRCIEU/TwoSampleMR"</span>)</span>
+<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"MRCIEU/MRInstruments"</span>)</span>
+<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a><span class="fu">## Extracting data from the OpenGWAS API</span></span>
+<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a>We will be running the script from the MR-Base paper </span>
+<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a>(<span class="co">[</span><span class="ot">Hemani et al., 2018</span><span class="co">](https://doi.org/10.7554/eLife.34408)</span>). </span>
+<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a>The R code we will use is from </span>
+<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a><span class="co">[</span><span class="ot">here</span><span class="co">](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R)</span>.</span>
+<span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a>First, we load the packages into our R session.</span>
+<span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a>Note that the **foreign** package provides the </span>
+<span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a><span class="in">`write.dta()`</span> function which we will use to save the data in Stata format.</span>
+<span id="cb2-32"><a href="#cb2-32" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-33"><a href="#cb2-33" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
+<span id="cb2-34"><a href="#cb2-34" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(TwoSampleMR)</span>
+<span id="cb2-35"><a href="#cb2-35" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(MRInstruments)</span>
+<span id="cb2-36"><a href="#cb2-36" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(foreign)</span>
+<span id="cb2-37"><a href="#cb2-37" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-38"><a href="#cb2-38" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-39"><a href="#cb2-39" aria-hidden="true" tabindex="-1"></a>Our edited version of the code starts by reading in some code</span>
+<span id="cb2-40"><a href="#cb2-40" aria-hidden="true" tabindex="-1"></a>to generate a set of plots in R.</span>
+<span id="cb2-41"><a href="#cb2-41" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-42"><a href="#cb2-42" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
+<span id="cb2-43"><a href="#cb2-43" aria-hidden="true" tabindex="-1"></a><span class="fu">source</span>(<span class="st">"mrplots.R"</span>)</span>
+<span id="cb2-44"><a href="#cb2-44" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-45"><a href="#cb2-45" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-46"><a href="#cb2-46" aria-hidden="true" tabindex="-1"></a>We can access the data using the **MRInstruments** package.</span>
+<span id="cb2-47"><a href="#cb2-47" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-48"><a href="#cb2-48" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
+<span id="cb2-49"><a href="#cb2-49" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(gwas_catalog)</span>
+<span id="cb2-50"><a href="#cb2-50" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-51"><a href="#cb2-51" aria-hidden="true" tabindex="-1"></a><span class="co"># Get published SNPs for LDL cholesterol</span></span>
+<span id="cb2-52"><a href="#cb2-52" aria-hidden="true" tabindex="-1"></a>ldl_snps <span class="ot">&lt;-</span> </span>
+<span id="cb2-53"><a href="#cb2-53" aria-hidden="true" tabindex="-1"></a>  <span class="fu">subset</span>(gwas_catalog, </span>
+<span id="cb2-54"><a href="#cb2-54" aria-hidden="true" tabindex="-1"></a>    <span class="fu">grepl</span>(<span class="st">"LDL choles"</span>, Phenotype) <span class="sc">&amp;</span> Author <span class="sc">==</span> <span class="st">"Willer CJ"</span>)<span class="sc">$</span>SNP</span>
+<span id="cb2-55"><a href="#cb2-55" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-56"><a href="#cb2-56" aria-hidden="true" tabindex="-1"></a><span class="co"># Extract from GLGC dataset</span></span>
+<span id="cb2-57"><a href="#cb2-57" aria-hidden="true" tabindex="-1"></a>exposure <span class="ot">&lt;-</span> </span>
+<span id="cb2-58"><a href="#cb2-58" aria-hidden="true" tabindex="-1"></a>  <span class="fu">convert_outcome_to_exposure</span>(</span>
+<span id="cb2-59"><a href="#cb2-59" aria-hidden="true" tabindex="-1"></a>    <span class="fu">extract_outcome_data</span>(ldl_snps, <span class="st">"ieu-a-300"</span>))</span>
+<span id="cb2-60"><a href="#cb2-60" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-61"><a href="#cb2-61" aria-hidden="true" tabindex="-1"></a><span class="co"># Get outcome data from Cardiogram 2015</span></span>
+<span id="cb2-62"><a href="#cb2-62" aria-hidden="true" tabindex="-1"></a>outcome <span class="ot">&lt;-</span> <span class="fu">extract_outcome_data</span>(exposure<span class="sc">$</span>SNP, <span class="st">"ieu-a-7"</span>)</span>
+<span id="cb2-63"><a href="#cb2-63" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-64"><a href="#cb2-64" aria-hidden="true" tabindex="-1"></a><span class="co"># Harmonise exposure and outcome datasets</span></span>
+<span id="cb2-65"><a href="#cb2-65" aria-hidden="true" tabindex="-1"></a><span class="co"># Assume alleles are on the forward strand</span></span>
+<span id="cb2-66"><a href="#cb2-66" aria-hidden="true" tabindex="-1"></a>dat <span class="ot">&lt;-</span> <span class="fu">harmonise_data</span>(exposure, outcome, <span class="at">action =</span> <span class="dv">1</span>)</span>
+<span id="cb2-67"><a href="#cb2-67" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-68"><a href="#cb2-68" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-69"><a href="#cb2-69" aria-hidden="true" tabindex="-1"></a>At this point we have our harmonised genotype-exposure and </span>
+<span id="cb2-70"><a href="#cb2-70" aria-hidden="true" tabindex="-1"></a>genotype-outcome association data saved in an object in our </span>
+<span id="cb2-71"><a href="#cb2-71" aria-hidden="true" tabindex="-1"></a>R session called <span class="in">`dat`</span>.</span>
+<span id="cb2-72"><a href="#cb2-72" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-73"><a href="#cb2-73" aria-hidden="true" tabindex="-1"></a>The next two code chunks perform the analysis in R.</span>
+<span id="cb2-74"><a href="#cb2-74" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-75"><a href="#cb2-75" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
+<span id="cb2-76"><a href="#cb2-76" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform MR</span></span>
+<span id="cb2-77"><a href="#cb2-77" aria-hidden="true" tabindex="-1"></a><span class="fu">mr</span>(dat)</span>
+<span id="cb2-78"><a href="#cb2-78" aria-hidden="true" tabindex="-1"></a><span class="fu">mr_heterogeneity</span>(dat)</span>
+<span id="cb2-79"><a href="#cb2-79" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>exposure <span class="ot">&lt;-</span> <span class="st">"LDL cholesterol"</span></span>
+<span id="cb2-80"><a href="#cb2-80" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>outcome <span class="ot">&lt;-</span> <span class="st">"Coronary heart disease"</span></span>
+<span id="cb2-81"><a href="#cb2-81" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-82"><a href="#cb2-82" aria-hidden="true" tabindex="-1"></a><span class="co"># Label outliers and create plots</span></span>
+<span id="cb2-83"><a href="#cb2-83" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>labels <span class="ot">&lt;-</span> dat<span class="sc">$</span>SNP</span>
+<span id="cb2-84"><a href="#cb2-84" aria-hidden="true" tabindex="-1"></a>dat<span class="sc">$</span>labels[<span class="sc">!</span> dat<span class="sc">$</span>SNP <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"rs11065987"</span>, <span class="st">"rs1250229"</span>, <span class="st">"rs4530754"</span>)] <span class="ot">&lt;-</span> <span class="cn">NA</span></span>
 <span id="cb2-85"><a href="#cb2-85" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb2-86"><a href="#cb2-86" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-87"><a href="#cb2-87" aria-hidden="true" tabindex="-1"></a><span class="al">![Plots generated by the TwoSampleMR R package.](ldl-chd.png)</span></span>
-<span id="cb2-88"><a href="#cb2-88" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-89"><a href="#cb2-89" aria-hidden="true" tabindex="-1"></a>We now save our <span class="in">`dat`</span> object as a Stata dataset.</span>
-<span id="cb2-90"><a href="#cb2-90" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
-<span id="cb2-91"><a href="#cb2-91" aria-hidden="true" tabindex="-1"></a><span class="fu">write.dta</span>(dat, <span class="at">file =</span> <span class="st">"dat.dta"</span>)</span>
-<span id="cb2-92"><a href="#cb2-92" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-93"><a href="#cb2-93" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-94"><a href="#cb2-94" aria-hidden="true" tabindex="-1"></a><span class="fu">## Performing the analysis using mrrobust in Stata</span></span>
-<span id="cb2-95"><a href="#cb2-95" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-96"><a href="#cb2-96" aria-hidden="true" tabindex="-1"></a>We now switch from using R code chunks to Stata code chunks.</span>
-<span id="cb2-97"><a href="#cb2-97" aria-hidden="true" tabindex="-1"></a>We read the data into Stata and list the variable names</span>
-<span id="cb2-98"><a href="#cb2-98" aria-hidden="true" tabindex="-1"></a>(note any <span class="in">`.`</span> in the colnames of <span class="in">`dat`</span> have been replaced with <span class="in">`_`</span>).</span>
-<span id="cb2-99"><a href="#cb2-99" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-100"><a href="#cb2-100" aria-hidden="true" tabindex="-1"></a><span class="in">use dat, clear</span></span>
-<span id="cb2-101"><a href="#cb2-101" aria-hidden="true" tabindex="-1"></a><span class="in">ds, v(28)</span></span>
-<span id="cb2-102"><a href="#cb2-102" aria-hidden="true" tabindex="-1"></a><span class="in">di _N</span></span>
-<span id="cb2-103"><a href="#cb2-103" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-104"><a href="#cb2-104" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-105"><a href="#cb2-105" aria-hidden="true" tabindex="-1"></a>We can then run the IVW model using <span class="in">`mregger`</span> with multiplicative</span>
-<span id="cb2-106"><a href="#cb2-106" aria-hidden="true" tabindex="-1"></a>standard errors.</span>
+<span id="cb2-87"><a href="#cb2-87" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
+<span id="cb2-88"><a href="#cb2-88" aria-hidden="true" tabindex="-1"></a><span class="fu">png</span>(<span class="st">"ldl-chd.png"</span>, <span class="at">width=</span><span class="dv">1000</span>, <span class="at">height=</span><span class="dv">1000</span>)</span>
+<span id="cb2-89"><a href="#cb2-89" aria-hidden="true" tabindex="-1"></a><span class="fu">mr_plots</span>(dat)</span>
+<span id="cb2-90"><a href="#cb2-90" aria-hidden="true" tabindex="-1"></a><span class="fu">dev.off</span>()</span>
+<span id="cb2-91"><a href="#cb2-91" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-92"><a href="#cb2-92" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-93"><a href="#cb2-93" aria-hidden="true" tabindex="-1"></a><span class="al">![Plots generated by the TwoSampleMR R package.](ldl-chd.png)</span></span>
+<span id="cb2-94"><a href="#cb2-94" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-95"><a href="#cb2-95" aria-hidden="true" tabindex="-1"></a>We now save our <span class="in">`dat`</span> object as a Stata dataset.</span>
+<span id="cb2-96"><a href="#cb2-96" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-97"><a href="#cb2-97" aria-hidden="true" tabindex="-1"></a><span class="in">```r</span></span>
+<span id="cb2-98"><a href="#cb2-98" aria-hidden="true" tabindex="-1"></a><span class="fu">write.dta</span>(dat, <span class="at">file =</span> <span class="st">"dat.dta"</span>)</span>
+<span id="cb2-99"><a href="#cb2-99" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-100"><a href="#cb2-100" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-101"><a href="#cb2-101" aria-hidden="true" tabindex="-1"></a><span class="fu">## Performing the analysis using mrrobust in Stata</span></span>
+<span id="cb2-102"><a href="#cb2-102" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-103"><a href="#cb2-103" aria-hidden="true" tabindex="-1"></a>We now switch from using R code chunks to Stata code chunks.</span>
+<span id="cb2-104"><a href="#cb2-104" aria-hidden="true" tabindex="-1"></a>We read the data into Stata and list the variable names</span>
+<span id="cb2-105"><a href="#cb2-105" aria-hidden="true" tabindex="-1"></a>(note any <span class="in">`.`</span> in the colnames of <span class="in">`dat`</span> have been replaced with <span class="in">`_`</span>).</span>
+<span id="cb2-106"><a href="#cb2-106" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb2-107"><a href="#cb2-107" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-108"><a href="#cb2-108" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw</span></span>
-<span id="cb2-109"><a href="#cb2-109" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-110"><a href="#cb2-110" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-111"><a href="#cb2-111" aria-hidden="true" tabindex="-1"></a>It is helpful to view the forest plot of genotype specific IV estimates.</span>
-<span id="cb2-112"><a href="#cb2-112" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-113"><a href="#cb2-113" aria-hidden="true" tabindex="-1"></a><span class="in">mrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///</span></span>
-<span id="cb2-114"><a href="#cb2-114" aria-hidden="true" tabindex="-1"></a><span class="in">    xlabel(-3,-2,-1,0,1,2,3)</span></span>
-<span id="cb2-115"><a href="#cb2-115" aria-hidden="true" tabindex="-1"></a><span class="in">graph export ldl-chd-mrforest.svg, width(600) replace</span></span>
-<span id="cb2-116"><a href="#cb2-116" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-117"><a href="#cb2-117" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-118"><a href="#cb2-118" aria-hidden="true" tabindex="-1"></a><span class="al">![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)</span></span>
+<span id="cb2-108"><a href="#cb2-108" aria-hidden="true" tabindex="-1"></a><span class="in">use dat, clear</span></span>
+<span id="cb2-109"><a href="#cb2-109" aria-hidden="true" tabindex="-1"></a><span class="in">ds, v(28)</span></span>
+<span id="cb2-110"><a href="#cb2-110" aria-hidden="true" tabindex="-1"></a><span class="in">di _N</span></span>
+<span id="cb2-111"><a href="#cb2-111" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-112"><a href="#cb2-112" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-113"><a href="#cb2-113" aria-hidden="true" tabindex="-1"></a>We can then run the IVW model using <span class="in">`mregger`</span> with multiplicative</span>
+<span id="cb2-114"><a href="#cb2-114" aria-hidden="true" tabindex="-1"></a>standard errors.</span>
+<span id="cb2-115"><a href="#cb2-115" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-116"><a href="#cb2-116" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
+<span id="cb2-117"><a href="#cb2-117" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw</span></span>
+<span id="cb2-118"><a href="#cb2-118" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb2-119"><a href="#cb2-119" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-120"><a href="#cb2-120" aria-hidden="true" tabindex="-1"></a>We can visualise this model with <span class="in">`mreggerplot`</span>.</span>
-<span id="cb2-121"><a href="#cb2-121" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-122"><a href="#cb2-122" aria-hidden="true" tabindex="-1"></a><span class="in">mreggerplot beta_outcome se_outcome beta_exposure se_exposure</span></span>
-<span id="cb2-123"><a href="#cb2-123" aria-hidden="true" tabindex="-1"></a><span class="in">graph export ldl-chd-mreggerplot.svg, width(600) replace</span></span>
-<span id="cb2-124"><a href="#cb2-124" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-125"><a href="#cb2-125" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-126"><a href="#cb2-126" aria-hidden="true" tabindex="-1"></a><span class="al">![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)</span></span>
+<span id="cb2-120"><a href="#cb2-120" aria-hidden="true" tabindex="-1"></a>It is helpful to view the forest plot of genotype specific IV estimates.</span>
+<span id="cb2-121"><a href="#cb2-121" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-122"><a href="#cb2-122" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
+<span id="cb2-123"><a href="#cb2-123" aria-hidden="true" tabindex="-1"></a><span class="in">mrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///</span></span>
+<span id="cb2-124"><a href="#cb2-124" aria-hidden="true" tabindex="-1"></a><span class="in">    xlabel(-3,-2,-1,0,1,2,3)</span></span>
+<span id="cb2-125"><a href="#cb2-125" aria-hidden="true" tabindex="-1"></a><span class="in">graph export ldl-chd-mrforest.svg, width(600) replace</span></span>
+<span id="cb2-126"><a href="#cb2-126" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb2-127"><a href="#cb2-127" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-128"><a href="#cb2-128" aria-hidden="true" tabindex="-1"></a>We then fit the MR-Egger, median, and modal based estimators.</span>
-<span id="cb2-129"><a href="#cb2-129" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-130"><a href="#cb2-130" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]</span></span>
-<span id="cb2-131"><a href="#cb2-131" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb2-132"><a href="#cb2-132" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-133"><a href="#cb2-133" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-134"><a href="#cb2-134" aria-hidden="true" tabindex="-1"></a><span class="in">mrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
+<span id="cb2-128"><a href="#cb2-128" aria-hidden="true" tabindex="-1"></a><span class="al">![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)</span></span>
+<span id="cb2-129"><a href="#cb2-129" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-130"><a href="#cb2-130" aria-hidden="true" tabindex="-1"></a>We can visualise this model with <span class="in">`mreggerplot`</span>.</span>
+<span id="cb2-131"><a href="#cb2-131" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-132"><a href="#cb2-132" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
+<span id="cb2-133"><a href="#cb2-133" aria-hidden="true" tabindex="-1"></a><span class="in">mreggerplot beta_outcome se_outcome beta_exposure se_exposure</span></span>
+<span id="cb2-134"><a href="#cb2-134" aria-hidden="true" tabindex="-1"></a><span class="in">graph export ldl-chd-mreggerplot.svg, width(600) replace</span></span>
 <span id="cb2-135"><a href="#cb2-135" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb2-136"><a href="#cb2-136" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-137"><a href="#cb2-137" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
-<span id="cb2-138"><a href="#cb2-138" aria-hidden="true" tabindex="-1"></a><span class="in">mrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
-<span id="cb2-139"><a href="#cb2-139" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb2-137"><a href="#cb2-137" aria-hidden="true" tabindex="-1"></a><span class="al">![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)</span></span>
+<span id="cb2-138"><a href="#cb2-138" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-139"><a href="#cb2-139" aria-hidden="true" tabindex="-1"></a>We then fit the MR-Egger, median, and modal based estimators.</span>
+<span id="cb2-140"><a href="#cb2-140" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-141"><a href="#cb2-141" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
+<span id="cb2-142"><a href="#cb2-142" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]</span></span>
+<span id="cb2-143"><a href="#cb2-143" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-144"><a href="#cb2-144" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-145"><a href="#cb2-145" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
+<span id="cb2-146"><a href="#cb2-146" aria-hidden="true" tabindex="-1"></a><span class="in">mrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
+<span id="cb2-147"><a href="#cb2-147" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb2-148"><a href="#cb2-148" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-149"><a href="#cb2-149" aria-hidden="true" tabindex="-1"></a><span class="in">```s</span></span>
+<span id="cb2-150"><a href="#cb2-150" aria-hidden="true" tabindex="-1"></a><span class="in">mrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
+<span id="cb2-151"><a href="#cb2-151" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>And we could continue with additional Stata code (or indeed R code) as we liked.</p>
 <p>Note to run this <code>.stmd</code> file in Stata we do so with the following command (specifying additional options as required, see <code>help markstat</code> for more information).</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>markstat <span class="kw">using</span> markstat-call-R-example</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -498,18 +511,7 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -541,7 +543,47 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -576,7 +618,11 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/examples/mrrobust-examples/index.html b/docs/examples/mrrobust-examples/index.html
index 42df7b5b..2d235de7 100644
--- a/docs/examples/mrrobust-examples/index.html
+++ b/docs/examples/mrrobust-examples/index.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust - Examples from helpfiles in the mrrobust package</title>
+<title>Examples from helpfiles in the mrrobust package – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -137,7 +137,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -147,7 +147,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -178,7 +178,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -265,7 +265,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -343,7 +343,7 @@ <h2 class="anchored" data-anchor-id="mrforest-examples">mrforest examples</h2>
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mrforest.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Example forest plot of genotype specific IV estimates."></p>
+<p><img src="mrforest.svg" class="img-fluid figure-img" alt="Example forest plot of genotype specific IV estimates."></p>
 <figcaption>Example forest plot of genotype specific IV estimates.</figcaption>
 </figure>
 </div>
@@ -471,7 +471,7 @@ <h2 class="anchored" data-anchor-id="mreggersimex-examples">mreggersimex example
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mreggersimex-plot.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="SIMEX plots for the MR-Egger model."></p>
+<p><img src="mreggersimex-plot.svg" class="img-fluid figure-img" alt="SIMEX plots for the MR-Egger model."></p>
 <figcaption>SIMEX plots for the MR-Egger model.</figcaption>
 </figure>
 </div>
@@ -483,15 +483,12 @@ <h2 class="anchored" data-anchor-id="mreggerplot-examples">mreggerplot examples<
 <div class="cell" data-layout-align="center">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>mreggerplot chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1</span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mreggerplot.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
 </div>
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mreggerplot.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Scatter plot showing the genotype summary level data points and the fitted MR-Egger model."></p>
+<p><img src="mreggerplot.svg" class="img-fluid figure-img" alt="Scatter plot showing the genotype summary level data points and the fitted MR-Egger model."></p>
 <figcaption>Scatter plot showing the genotype summary level data points and the fitted MR-Egger model.</figcaption>
 </figure>
 </div>
@@ -502,7 +499,7 @@ <h2 class="anchored" data-anchor-id="mreggerplot-examples">mreggerplot examples<
 <h2 class="anchored" data-anchor-id="mrmedian-examples">mrmedian examples</h2>
 <p>Weighted median estimator.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>mrmedian chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>mrmedian chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                            Replications = 1000
@@ -517,8 +514,8 @@ <h2 class="anchored" data-anchor-id="mrmedian-examples">mrmedian examples</h2>
 <section id="mrmodal-examples" class="level2">
 <h2 class="anchored" data-anchor-id="mrmodal-examples">mrmodal examples</h2>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>mrmodalplot chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, <span class="dv">seed</span>(12345)</span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrmodalplot.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>mrmodalplot chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, <span class="dv">seed</span>(12345)</span>
+<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrmodalplot.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                            Replications = 1000
@@ -552,7 +549,7 @@ <h2 class="anchored" data-anchor-id="mrmodal-examples">mrmodal examples</h2>
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mrmodalplot.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Densities of the IV estimates using different values of the phi parameter."></p>
+<p><img src="mrmodalplot.svg" class="img-fluid figure-img" alt="Densities of the IV estimates using different values of the phi parameter."></p>
 <figcaption>Densities of the IV estimates using different values of the phi parameter.</figcaption>
 </figure>
 </div>
@@ -560,7 +557,7 @@ <h2 class="anchored" data-anchor-id="mrmodal-examples">mrmodal examples</h2>
 </div>
 <p>Simple mode estimator.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                            Replications = 1000
@@ -574,7 +571,7 @@ <h2 class="anchored" data-anchor-id="mrmodal-examples">mrmodal examples</h2>
 </div>
 <p>Weighted mode estimator.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                            Replications = 1000
@@ -588,7 +585,7 @@ <h2 class="anchored" data-anchor-id="mrmodal-examples">mrmodal examples</h2>
 </div>
 <p>Simple mode estimator with NOME assumption.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, nome</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, nome</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                            Replications = 1000
@@ -604,17 +601,14 @@ <h2 class="anchored" data-anchor-id="mrmodal-examples">mrmodal examples</h2>
 <section id="mrfunnel-examples" class="level2">
 <h2 class="anchored" data-anchor-id="mrfunnel-examples">mrfunnel examples</h2>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>mrfunnel chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, xlrange(0 10)</span>
-<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrfunnel.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>mrfunnel chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, xlrange(0 10)</span>
+<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrfunnel.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mrfunnel.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Example funnel plot."></p>
+<p><img src="mrfunnel.svg" class="img-fluid figure-img" alt="Example funnel plot."></p>
 <figcaption>Example funnel plot.</figcaption>
 </figure>
 </div>
@@ -625,24 +619,18 @@ <h2 class="anchored" data-anchor-id="mrfunnel-examples">mrfunnel examples</h2>
 <h2 class="anchored" data-anchor-id="leave-one-out-analysis">Leave one out analysis</h2>
 <p>Create a smaller subset of data.</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> <span class="kw">byte</span> sel2 = (ldlcp2 &lt; 1e-25)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
+<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> <span class="kw">byte</span> sel2 = (ldlcp2 &lt; 1e-25)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Perform leave one out analysis using the IVW estimator</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>mrleaveoneout chdbeta ldlcbeta <span class="kw">if</span> sel2==1, gyse(chdse) genotype(rsid) noprint</span>
-<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrleaveoneout-plot-01.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
+<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>mrleaveoneout chdbeta ldlcbeta <span class="kw">if</span> sel2==1, gyse(chdse) genotype(rsid) noprint</span>
+<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrleaveoneout-plot-01.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mrleaveoneout-plot-01.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Plot showing leave one (genotype) out at a time analysis for the IVW estimate."></p>
+<p><img src="mrleaveoneout-plot-01.svg" class="img-fluid figure-img" alt="Plot showing leave one (genotype) out at a time analysis for the IVW estimate."></p>
 <figcaption>Plot showing leave one (genotype) out at a time analysis for the IVW estimate.</figcaption>
 </figure>
 </div>
@@ -650,15 +638,15 @@ <h2 class="anchored" data-anchor-id="leave-one-out-analysis">Leave one out analy
 </div>
 <p>Perform leave one out analysis using MVMR collecting the estimate for LDL-c.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>mrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta <span class="kw">if</span> sel2==1, <span class="co">///</span></span>
-<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>    method(mvmr) gyse(chdse) genotype(rsid) noprint</span>
-<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrleaveoneout-plot-02.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>mrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta <span class="kw">if</span> sel2==1, <span class="co">///</span></span>
+<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>    method(mvmr) gyse(chdse) genotype(rsid) noprint</span>
+<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrleaveoneout-plot-02.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mrleaveoneout-plot-02.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Plot showing leave one (genotype) out at a time analysis for the MVMR estimate."></p>
+<p><img src="mrleaveoneout-plot-02.svg" class="img-fluid figure-img" alt="Plot showing leave one (genotype) out at a time analysis for the MVMR estimate."></p>
 <figcaption>Plot showing leave one (genotype) out at a time analysis for the MVMR estimate.</figcaption>
 </figure>
 </div>
@@ -715,18 +703,7 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -758,7 +735,47 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -793,7 +810,11 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/examples/mrrobust-examples/mreggerplot.svg b/docs/examples/mrrobust-examples/mreggerplot.svg
index a44585d2..0edd61bf 100644
--- a/docs/examples/mrrobust-examples/mreggerplot.svg
+++ b/docs/examples/mrrobust-examples/mreggerplot.svg
@@ -1,435 +1,663 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
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+<!-- This is a Stata 18.5 generated SVG file (http://www.stata.com) -->
 
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+<svg version="1.1" width="600px" height="360px" viewBox="0 0 5400 3240" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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-	<text x="4959.33" y="7227.33" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.96 (-3.36, -0.57)</text>
-	<text x="4959.33" y="7320.21" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-3.35 (-5.11, -1.59)</text>
-	<text x="4959.33" y="7599.66" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.48 (0.41, 0.56)</text>
-	<text x="4959.33" y="7692.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.41, 0.82)</text>
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+	<text x="4962.03" y="1076.73" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.32 (0.18, 2.46)</text>
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+	<text x="4962.03" y="3661.44" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (-0.20, 1.10)</text>
+	<text x="4962.03" y="3753.51" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (0.07, 0.84)</text>
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+	<text x="4962.03" y="5507.43" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.00 (-1.60, 1.60)</text>
+	<text x="4962.03" y="5599.77" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.02 (-0.66, 0.62)</text>
+	<text x="4962.03" y="5691.84" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.12 (-1.05, 0.82)</text>
+	<text x="4962.03" y="5784.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.13 (-1.43, 1.18)</text>
+	<text x="4962.03" y="5876.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.16 (-1.02, 0.69)</text>
+	<text x="4962.03" y="5968.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.18 (-2.01, 1.66)</text>
+	<text x="4962.03" y="6061.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.22 (-1.19, 0.75)</text>
+	<text x="4962.03" y="6153.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.29, 0.78)</text>
+	<text x="4962.03" y="6245.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.24, 0.73)</text>
+	<text x="4962.03" y="6337.95" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.27 (-1.24, 0.71)</text>
+	<text x="4962.03" y="6430.29" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.44, 0.81)</text>
+	<text x="4962.03" y="6522.63" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.15, 0.50)</text>
+	<text x="4962.03" y="6614.97" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.33 (-1.58, 0.91)</text>
+	<text x="4962.03" y="6707.31" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.35 (-0.95, 0.26)</text>
+	<text x="4962.03" y="6799.65" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.43 (-1.68, 0.81)</text>
+	<text x="4962.03" y="6891.99" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.60 (-1.89, 0.69)</text>
+	<text x="4962.03" y="6984.06" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.15 (-2.33, 0.04)</text>
+	<text x="4962.03" y="7076.40" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.42 (-3.01, 0.17)</text>
+	<text x="4962.03" y="7168.74" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.96 (-3.36, -0.57)</text>
+	<text x="4962.03" y="7261.08" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-3.35 (-5.11, -1.59)</text>
+	<text x="4962.03" y="7538.10" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.48 (0.41, 0.56)</text>
+	<text x="4962.03" y="7630.17" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.41, 0.82)</text>
+	<text x="4962.03" y="338.55" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">Estimate (95% CI)</text>
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+	<text x="575.10" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs10790162</text>
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+	<text x="5714.54" y="2364.97" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.56 (0.46, 0.65)</text>
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+	<text x="5714.54" y="3365.86" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.61)</text>
+	<text x="5714.54" y="3615.88" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.62)</text>
+	<text x="5714.54" y="3866.17" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="4116.19" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.50 (0.41, 0.59)</text>
+	<text x="5714.54" y="4366.48" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
+	<text x="5714.54" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.59)</text>
+	<text x="5714.54" y="4866.79" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.43, 0.61)</text>
+	<text x="5714.54" y="5116.81" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.43, 0.61)</text>
+	<text x="5714.54" y="5367.10" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="5617.12" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
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+	<text x="5714.54" y="1614.64" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
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+	<text x="5714.54" y="2865.28" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="3115.57" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.62)</text>
+	<text x="5714.54" y="3365.86" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.61)</text>
+	<text x="5714.54" y="3615.88" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.62)</text>
+	<text x="5714.54" y="3866.17" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="4116.19" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.50 (0.41, 0.59)</text>
+	<text x="5714.54" y="4366.48" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
+	<text x="5714.54" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.59)</text>
+	<text x="5714.54" y="4866.79" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.43, 0.61)</text>
+	<text x="5714.54" y="5116.81" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.43, 0.61)</text>
+	<text x="5714.54" y="5367.10" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="5617.12" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="5867.41" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.45, 0.62)</text>
+	<text x="5714.54" y="6117.70" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
+	<text x="5714.54" y="6367.72" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.61)</text>
+	<text x="5714.54" y="7118.32" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.44, 0.61)</text>
+	<text x="5714.54" y="613.75" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">Estimate (95% CI)</text>
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+	<line x1="4655.06" y1="2540.16" x2="5555.78" y2="2540.16" style="stroke:#000000;stroke-width:24.73"/>
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+	<text x="575.10" y="3615.88" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs4240624</text>
+	<text x="575.10" y="3866.17" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs2954022</text>
+	<text x="575.10" y="4116.19" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs579459</text>
+	<text x="575.10" y="4366.48" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs1535</text>
+	<text x="575.10" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs10790162</text>
+	<text x="575.10" y="4866.79" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs2000999</text>
+	<text x="575.10" y="5116.81" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs6511720</text>
+	<text x="575.10" y="5367.10" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs688</text>
+	<text x="575.10" y="5617.12" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs10401969</text>
+	<text x="575.10" y="5867.41" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs6859</text>
+	<text x="575.10" y="6117.70" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs7254892</text>
+	<text x="575.10" y="6367.72" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs6065311</text>
+	<text x="575.10" y="6868.03" style="font-family:'Helvetica';font-weight:bold;font-size:214.38px;fill:#000000">All genotypes</text>
+	<text x="575.10" y="7118.32" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">MVMR</text>
+	<text x="575.10" y="613.75" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rsid</text>
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+	<text x="5714.54" y="1614.64" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.35, 0.61)</text>
+	<text x="5714.54" y="1864.66" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.29, 0.63)</text>
+	<text x="5714.54" y="2114.95" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.33, 0.63)</text>
+	<text x="5714.54" y="2364.97" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.50 (0.36, 0.65)</text>
+	<text x="5714.54" y="2615.26" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.63)</text>
+	<text x="5714.54" y="2865.28" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.45 (0.30, 0.60)</text>
+	<text x="5714.54" y="3115.57" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.31, 0.62)</text>
+	<text x="5714.54" y="3365.86" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.62)</text>
+	<text x="5714.54" y="3615.88" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.45 (0.30, 0.60)</text>
+	<text x="5714.54" y="3866.17" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.62)</text>
+	<text x="5714.54" y="4116.19" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.42 (0.31, 0.54)</text>
+	<text x="5714.54" y="4366.48" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.31, 0.63)</text>
+	<text x="5714.54" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.33, 0.63)</text>
+	<text x="5714.54" y="4866.79" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.31, 0.61)</text>
+	<text x="5714.54" y="5116.81" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.30, 0.62)</text>
+	<text x="5714.54" y="5367.10" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.45 (0.31, 0.60)</text>
+	<text x="5714.54" y="5617.12" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.31, 0.61)</text>
+	<text x="5714.54" y="5867.41" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.33, 0.63)</text>
+	<text x="5714.54" y="6117.70" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.49 (0.33, 0.64)</text>
+	<text x="5714.54" y="6367.72" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.62)</text>
+	<text x="5714.54" y="7118.32" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.61)</text>
+	<text x="5714.54" y="613.75" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">Estimate (95% CI)</text>
+	<text x="5714.54" y="1364.35" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.62)</text>
+	<text x="5714.54" y="1614.64" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.35, 0.61)</text>
+	<text x="5714.54" y="1864.66" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.29, 0.63)</text>
+	<text x="5714.54" y="2114.95" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.33, 0.63)</text>
+	<text x="5714.54" y="2364.97" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.50 (0.36, 0.65)</text>
+	<text x="5714.54" y="2615.26" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.63)</text>
+	<text x="5714.54" y="2865.28" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.45 (0.30, 0.60)</text>
+	<text x="5714.54" y="3115.57" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.31, 0.62)</text>
+	<text x="5714.54" y="3365.86" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.62)</text>
+	<text x="5714.54" y="3615.88" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.45 (0.30, 0.60)</text>
+	<text x="5714.54" y="3866.17" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.62)</text>
+	<text x="5714.54" y="4116.19" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.42 (0.31, 0.54)</text>
+	<text x="5714.54" y="4366.48" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.31, 0.63)</text>
+	<text x="5714.54" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.33, 0.63)</text>
+	<text x="5714.54" y="4866.79" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.31, 0.61)</text>
+	<text x="5714.54" y="5116.81" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.30, 0.62)</text>
+	<text x="5714.54" y="5367.10" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.45 (0.31, 0.60)</text>
+	<text x="5714.54" y="5617.12" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.46 (0.31, 0.61)</text>
+	<text x="5714.54" y="5867.41" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.48 (0.33, 0.63)</text>
+	<text x="5714.54" y="6117.70" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.49 (0.33, 0.64)</text>
+	<text x="5714.54" y="6367.72" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.62)</text>
+	<text x="5714.54" y="7118.32" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.47 (0.32, 0.61)</text>
+	<text x="5714.54" y="613.75" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">Estimate (95% CI)</text>
+	<polygon points="5049.05,1289.52 5096.78,1241.80 5144.50,1289.52 5096.78,1337.24" style="fill:#000000"/>
+	<polygon points="5066.54,1289.52 5096.78,1259.28 5127.02,1289.52 5096.78,1319.76" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="5081.18,1539.54 5128.91,1491.82 5176.63,1539.54 5128.91,1587.26" style="fill:#000000"/>
+	<polygon points="5098.67,1539.54 5128.91,1509.30 5159.15,1539.54 5128.91,1569.78" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="5017.19,1789.83 5064.92,1742.11 5112.64,1789.83 5064.92,1837.55" style="fill:#000000"/>
+	<polygon points="5034.68,1789.83 5064.92,1759.59 5095.16,1789.83 5064.92,1820.07" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="5064.44,2039.85 5112.17,1992.13 5159.89,2039.85 5112.17,2087.57" style="fill:#000000"/>
+	<polygon points="5081.93,2039.85 5112.17,2009.61 5142.41,2039.85 5112.17,2070.09" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="5143.28,2290.14 5191.01,2242.42 5238.73,2290.14 5191.01,2337.86" style="fill:#000000"/>
+	<polygon points="5160.77,2290.14 5191.01,2259.90 5221.25,2290.14 5191.01,2320.38" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="5057.69,2540.16 5105.42,2492.44 5153.14,2540.16 5105.42,2587.88" style="fill:#000000"/>
+	<polygon points="5075.18,2540.16 5105.42,2509.92 5135.66,2540.16 5105.42,2570.40" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="4998.56,2790.45 5046.29,2742.73 5094.01,2790.45 5046.29,2838.17" style="fill:#000000"/>
+	<polygon points="5016.05,2790.45 5046.29,2760.21 5076.53,2790.45 5046.29,2820.69" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="5039.87,3040.47 5087.60,2992.75 5135.32,3040.47 5087.60,3088.19" style="fill:#000000"/>
+	<polygon points="5057.36,3040.47 5087.60,3010.23 5117.84,3040.47 5087.60,3070.71" style="fill:none;stroke:#000000;stroke-width:24.73"/>
+	<polygon points="5040.68,3290.76 5088.41,3243.04 5136.13,3290.76 5088.41,3338.48" style="fill:#000000"/>
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+	<line x1="557.89" y1="1858.44" x2="513.00" y2="1858.44" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="490.39" y="1897.88" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="end">.5</text>
+	<line x1="557.89" y1="1021.61" x2="513.00" y2="1021.61" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="490.39" y="1061.05" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="end">1</text>
+	<line x1="557.89" y1="184.61" x2="513.00" y2="184.61" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="490.39" y="224.05" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="end">1.5</text>
+	<text x="214.55" y="1439.94" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" transform="rotate(-90 214.55,1439.94)" text-anchor="middle">Density</text>
+	<line x1="557.89" y1="2766.66" x2="2828.42" y2="2766.66" style="stroke:#000000;stroke-width:6.48"/>
+	<line x1="632.98" y1="2766.66" x2="632.98" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="632.98" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">-4</text>
+	<line x1="1164.04" y1="2766.66" x2="1164.04" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="1164.04" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">-2</text>
+	<line x1="1695.09" y1="2766.66" x2="1695.09" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="1695.09" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">0</text>
+	<line x1="2225.98" y1="2766.66" x2="2225.98" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="2225.98" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">2</text>
+	<line x1="2757.04" y1="2766.66" x2="2757.04" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="2757.04" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">4</text>
+	<text x="1693.07" y="3070.31" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">IV estimates</text>
+	<rect x="2867.23" y="1335.15" width="2380.56" height="209.76" style="fill:#FFFFFF"/>
+	<rect x="2870.47" y="1338.39" width="2374.08" height="203.28" style="fill:none;stroke:#FFFFFF;stroke-width:6.48"/>
+	<line x1="2915.83" y1="1439.94" x2="3175.03" y2="1439.94" style="stroke:#1A85FF;stroke-width:9.72"/>
+	<line x1="3784.22" y1="1439.94" x2="4043.42" y2="1439.94" style="stroke:#D41159;stroke-width:9.72"/>
+	<line x1="4586.79" y1="1439.94" x2="4845.99" y2="1439.94" style="stroke:#00BF7F;stroke-width:9.72"/>
+	<text x="3242.53" y="1479.38" style="font-family:'Helvetica';font-size:112.56px;fill:#000000">φ = .25</text>
+	<text x="4110.92" y="1479.38" style="font-family:'Helvetica';font-size:112.56px;fill:#000000">φ = .5</text>
+	<text x="4913.49" y="1479.38" style="font-family:'Helvetica';font-size:112.56px;fill:#000000">φ = 1</text>
 </svg>
diff --git a/docs/examples/mvcommands-example/index.html b/docs/examples/mvcommands-example/index.html
index 3bb6552c..4c20a511 100644
--- a/docs/examples/mvcommands-example/index.html
+++ b/docs/examples/mvcommands-example/index.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>index – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -137,7 +137,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -147,7 +147,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -178,7 +178,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -265,7 +265,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -299,22 +299,16 @@ <h1>Example demonstrating MVMR commands</h1>
 <p>Read in the <span class="citation" data-cites="do-natgen-2013">Do et al. (<a href="#ref-do-natgen-2013" role="doc-biblioref">2013</a>)</span> example dataset.</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="kw">use</span> https:<span class="co">//raw.github.com/remlapmot/mrrobust/master/dodata, clear</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
 </div>
 <p>Select observations (p-value with LDL-C &lt; 10<sup>-8</sup>)</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> <span class="kw">byte</span> sel1 = (ldlcp2 &lt; 1e-8)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> <span class="kw">byte</span> sel1 = (ldlcp2 &lt; 1e-8)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <section id="mv-ivw" class="level2">
 <h2 class="anchored" data-anchor-id="mv-ivw">MV-IVW</h2>
 <p>Fit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable Mendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c <span class="citation" data-cites="burgess-aje-2015">(<a href="#ref-burgess-aje-2015" role="doc-biblioref">Burgess, Dudbridge, and Thompson 2015</a>)</span>.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>mrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>mrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                       Number of phenotypes = 2
@@ -331,7 +325,7 @@ <h2 class="anchored" data-anchor-id="mv-ivw">MV-IVW</h2>
 </div>
 <p>Additionally include a third phenotype – triglycerides.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                       Number of phenotypes = 3
@@ -349,7 +343,7 @@ <h2 class="anchored" data-anchor-id="mv-ivw">MV-IVW</h2>
 </div>
 <p>Report the Q<sub>A</sub> statistic for instrument validity and the conditional F-statistics for instrument strength for each phenotype <span class="citation" data-cites="sanderson-ije-2019 sanderson-statsmed-2021">(<a href="#ref-sanderson-ije-2019" role="doc-biblioref">Sanderson et al. 2019</a>; <a href="#ref-sanderson-statsmed-2021" role="doc-biblioref">Sanderson, Spiller, and Bowden 2021</a>)</span>.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, gxse(ldlcse hdlcse tgse)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, gxse(ldlcse hdlcse tgse)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                       Number of phenotypes = 3
@@ -375,7 +369,7 @@ <h2 class="anchored" data-anchor-id="mv-ivw">MV-IVW</h2>
 <h2 class="anchored" data-anchor-id="mvmr-egger">MVMR-Egger</h2>
 <p>Fit MVMR-Egger regression <span class="citation" data-cites="rees-statsmed-2017">(<a href="#ref-rees-statsmed-2017" role="doc-biblioref">Rees, Wood, and Burgess 2017</a>)</span>, by default orienting the model to the first phenotype in the main varlist.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                       MVMR-Egger model oriented wrt: ldlcbeta
                                                       Number of genotypes = 73
@@ -394,7 +388,7 @@ <h2 class="anchored" data-anchor-id="mvmr-egger">MVMR-Egger</h2>
 </div>
 <p>We can also orient the model with respect to HDL-C instead of LDL-C.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, orient(2)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, orient(2)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                       MVMR-Egger model oriented wrt: hdlcbeta
                                                       Number of genotypes = 73
@@ -413,7 +407,7 @@ <h2 class="anchored" data-anchor-id="mvmr-egger">MVMR-Egger</h2>
 </div>
 <p>Or we can orient the model with respect to triglycerides instead of LDL-C.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, orient(3)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, orient(3)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                         MVMR-Egger model oriented wrt: tgbeta
                                                       Number of genotypes = 73
@@ -491,18 +485,7 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -534,7 +517,47 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -569,7 +592,11 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/examples/rmarkdown-call-stata-example/index.html b/docs/examples/rmarkdown-call-stata-example/index.html
index 8f249785..f9bf15d9 100644
--- a/docs/examples/rmarkdown-call-stata-example/index.html
+++ b/docs/examples/rmarkdown-call-stata-example/index.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>index – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -137,7 +137,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -147,7 +147,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -178,7 +178,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -265,7 +265,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -282,7 +282,7 @@ <h2 id="toc-title">On this page</h2>
   <li><a href="#how-to-include-mrrobust-stata-code-in-an-r-markdown-or-quarto-document-using-the-statamarkdown-r-package" id="toc-how-to-include-mrrobust-stata-code-in-an-r-markdown-or-quarto-document-using-the-statamarkdown-r-package" class="nav-link active" data-scroll-target="#how-to-include-mrrobust-stata-code-in-an-r-markdown-or-quarto-document-using-the-statamarkdown-r-package">How to include mrrobust Stata code in an R Markdown or Quarto document using the Statamarkdown R package</a>
   <ul class="collapse">
   <li><a href="#introduction" id="toc-introduction" class="nav-link" data-scroll-target="#introduction">Introduction</a></li>
-  <li><a href="#example-r-markdown-document" id="toc-example-r-markdown-document" class="nav-link" data-scroll-target="#example-r-markdown-document">Example R Markdown document</a></li>
+  <li><a href="#example-r-markdown-or-quarto-document" id="toc-example-r-markdown-or-quarto-document" class="nav-link" data-scroll-target="#example-r-markdown-or-quarto-document">Example R Markdown or Quarto document</a></li>
   <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
   </ul></li>
   </ul>
@@ -298,18 +298,24 @@ <h2 id="toc-title">On this page</h2>
 <h1>How to include mrrobust Stata code in an R Markdown or Quarto document using the Statamarkdown R package</h1>
 <section id="introduction" class="level2">
 <h2 class="anchored" data-anchor-id="introduction">Introduction</h2>
-<p>This example shows how to run R and Stata code within the same R Markdown (<code>.Rmd</code>) file using the <strong>Statamarkdown</strong> R package. More information about this package is available <a href="https://github.com/Hemken/Statamarkdown">here</a> and <a href="https://www.ssc.wisc.edu/~hemken/Stataworkshops/Statamarkdown/stata-and-r-markdown.html">here</a>. To install this package and load it into your current R session run the following in R.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># install.packages("Statamarkdown") # uncomment on first run</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(Statamarkdown)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>This example shows how to run R and Stata code within the same R Markdown (<em>.Rmd</em>) or Quarto (<em>.qmd</em>) file using the <strong>Statamarkdown</strong> R package. More information about this package is available <a href="https://github.com/Hemken/Statamarkdown">here</a> and <a href="https://www.ssc.wisc.edu/~hemken/Stataworkshops/Statamarkdown/stata-and-r-markdown.html">here</a>. To install this package run the following in R.</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"Statamarkdown"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>Note when writing our Stata code chunks we need to be careful when we specify the <code>collectcode=TRUE</code> code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.</p>
-<p>Using R and Stata code in the same script means that we can use the functions provided by the <a href="https://github.com/MRCIEU/TwoSampleMR"><strong>TwoSampleMR</strong></a> package to obtain data from <a href="https://www.mrbase.org/">MR-Base</a> <span class="citation" data-cites="mrbase">(<a href="#ref-mrbase" role="doc-biblioref">Hemani et al. 2018</a>)</span>.</p>
+<p>Using R and Stata code in the same script means that we can use the functions provided by the <a href="https://github.com/MRCIEU/TwoSampleMR"><strong>TwoSampleMR</strong></a> package to obtain data from the <a href="https://api.opengwas.io/">OpenGWAS API</a>, which is the successor to <a href="https://www.mrbase.org/">MR-Base</a> <span class="citation" data-cites="mrbase">(<a href="#ref-mrbase" role="doc-biblioref">Hemani et al. 2018</a>)</span>.</p>
+<p>The OpenGWAS API requires authentication. You must place an <code>OPENGWAS_JWT</code> token (by creating a free account on <a href="https://api.opengwas.io/profile/" class="uri">https://api.opengwas.io/profile/</a>) in your <em>.Renviron</em> file before running the code below. See the <a href="https://mrcieu.github.io/ieugwasr/articles/guide.html#authentication"><strong>ieugwasr</strong> website</a> for more information.</p>
 <p>Next we install the other required packages in R.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"MRCIEU/TwoSampleMR"</span>)</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"MRCIEU/MRInstruments"</span>)</span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"foreign"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>  <span class="st">"TwoSampleMR"</span>,</span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">repos =</span> <span class="fu">c</span>(</span>
+<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>    <span class="st">"https://mrcieu.r-universe.dev"</span>,</span>
+<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>    <span class="st">"https://cloud.r-project.org"</span></span>
+<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>  )</span>
+<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"foreign"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </section>
-<section id="example-r-markdown-document" class="level2">
-<h2 class="anchored" data-anchor-id="example-r-markdown-document">Example R Markdown document</h2>
+<section id="example-r-markdown-or-quarto-document" class="level2">
+<h2 class="anchored" data-anchor-id="example-r-markdown-or-quarto-document">Example R Markdown or Quarto document</h2>
+<p>Save the following code chunk as an <em>.Rmd</em> or <em>.qmd</em> file, e.g.&nbsp;<em>myanalysis.Rmd</em>.</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> Using mrrobust in an R Markdown or Quarto document</span></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
@@ -324,53 +330,53 @@ <h2 class="anchored" data-anchor-id="example-r-markdown-document">Example R Mark
 <span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>We load the packages into our R session.</span>
 <span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a>Note that the **foreign** package provides the <span class="in">`write.dta()`</span> function</span>
 <span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a>which we will use to save the data in Stata format.</span>
-<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a><span class="in">library(TwoSampleMR)</span></span>
-<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a><span class="in">library(MRInstruments)</span></span>
-<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a><span class="in">library(foreign)</span></span>
-<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>We can access the data using the **MRInstruments** package.</span>
-<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a><span class="in">data(gwas_catalog)</span></span>
-<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a><span class="in"># Get published SNPs for LDL cholesterol</span></span>
-<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a><span class="in">ldl_snps &lt;-</span></span>
-<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a><span class="in">  subset(gwas_catalog,</span></span>
-<span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a><span class="in">    grepl("LDL choles", Phenotype) &amp; Author == "Willer CJ")$SNP</span></span>
-<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a><span class="in"># Extract from GLGC dataset</span></span>
-<span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a><span class="in">exposure &lt;-</span></span>
-<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a><span class="in">  convert_outcome_to_exposure(</span></span>
-<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a><span class="in">    extract_outcome_data(ldl_snps, "ieu-a-300"))</span></span>
-<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a><span class="in"># Get outcome data from Cardiogram 2015</span></span>
-<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a><span class="in">outcome &lt;- extract_outcome_data(exposure$SNP, "ieu-a-7")</span></span>
-<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a><span class="in"># Harmonise exposure and outcome datasets</span></span>
-<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a><span class="in"># Assume alleles are on the forward strand</span></span>
-<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a><span class="in">dat &lt;- harmonise_data(exposure, outcome, action = 1)</span></span>
-<span id="cb3-43"><a href="#cb3-43" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb3-44"><a href="#cb3-44" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-45"><a href="#cb3-45" aria-hidden="true" tabindex="-1"></a>At this point we have our harmonised genotype-exposure and genotype-outcome</span>
-<span id="cb3-46"><a href="#cb3-46" aria-hidden="true" tabindex="-1"></a>association data saved in an object in our R session called <span class="in">`dat`</span>.</span>
-<span id="cb3-47"><a href="#cb3-47" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-48"><a href="#cb3-48" aria-hidden="true" tabindex="-1"></a>The next two code chunks perform the analysis in R.</span>
-<span id="cb3-49"><a href="#cb3-49" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-50"><a href="#cb3-50" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb3-51"><a href="#cb3-51" aria-hidden="true" tabindex="-1"></a><span class="in"># Perform MR analysis</span></span>
-<span id="cb3-52"><a href="#cb3-52" aria-hidden="true" tabindex="-1"></a><span class="in">mr(dat)</span></span>
-<span id="cb3-53"><a href="#cb3-53" aria-hidden="true" tabindex="-1"></a><span class="in">mr_heterogeneity(dat)</span></span>
-<span id="cb3-54"><a href="#cb3-54" aria-hidden="true" tabindex="-1"></a><span class="in">dat$exposure &lt;- "LDL cholesterol"</span></span>
-<span id="cb3-55"><a href="#cb3-55" aria-hidden="true" tabindex="-1"></a><span class="in">dat$outcome &lt;- "Coronary heart disease"</span></span>
-<span id="cb3-56"><a href="#cb3-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-57"><a href="#cb3-57" aria-hidden="true" tabindex="-1"></a><span class="in"># Label outliers and create plots</span></span>
-<span id="cb3-58"><a href="#cb3-58" aria-hidden="true" tabindex="-1"></a><span class="in">dat$labels &lt;- dat$SNP</span></span>
-<span id="cb3-59"><a href="#cb3-59" aria-hidden="true" tabindex="-1"></a><span class="in">dat$labels[! dat$SNP %in% c("rs11065987", "rs1250229", "rs4530754")] &lt;- NA</span></span>
-<span id="cb3-60"><a href="#cb3-60" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb3-61"><a href="#cb3-61" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a><span class="in">library(TwoSampleMR)</span></span>
+<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a><span class="in">library(MRInstruments)</span></span>
+<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a><span class="in">library(foreign)</span></span>
+<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a><span class="in">library(Statamarkdown)</span></span>
+<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a>We can access the data using the **MRInstruments** package.</span>
+<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a><span class="in">data(gwas_catalog)</span></span>
+<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a><span class="in"># Get published SNPs for LDL cholesterol</span></span>
+<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a><span class="in">ldl_snps &lt;-</span></span>
+<span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a><span class="in">  subset(gwas_catalog,</span></span>
+<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a><span class="in">    grepl("LDL choles", Phenotype) &amp; Author == "Willer CJ")$SNP</span></span>
+<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a><span class="in"># Extract from GLGC dataset</span></span>
+<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a><span class="in">exposure &lt;-</span></span>
+<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a><span class="in">  convert_outcome_to_exposure(</span></span>
+<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a><span class="in">    extract_outcome_data(ldl_snps, "ieu-a-300"))</span></span>
+<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a><span class="in"># Get outcome data from Cardiogram 2015</span></span>
+<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a><span class="in">outcome &lt;- extract_outcome_data(exposure$SNP, "ieu-a-7")</span></span>
+<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a><span class="in"># Harmonise exposure and outcome datasets</span></span>
+<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a><span class="in"># Assume alleles are on the forward strand</span></span>
+<span id="cb3-43"><a href="#cb3-43" aria-hidden="true" tabindex="-1"></a><span class="in">dat &lt;- harmonise_data(exposure, outcome, action = 1)</span></span>
+<span id="cb3-44"><a href="#cb3-44" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb3-45"><a href="#cb3-45" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-46"><a href="#cb3-46" aria-hidden="true" tabindex="-1"></a>At this point we have our harmonised genotype-exposure and genotype-outcome</span>
+<span id="cb3-47"><a href="#cb3-47" aria-hidden="true" tabindex="-1"></a>association data saved in an object in our R session called <span class="in">`dat`</span>.</span>
+<span id="cb3-48"><a href="#cb3-48" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-49"><a href="#cb3-49" aria-hidden="true" tabindex="-1"></a>The next two code chunks perform the analysis in R.</span>
+<span id="cb3-50"><a href="#cb3-50" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-51"><a href="#cb3-51" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb3-52"><a href="#cb3-52" aria-hidden="true" tabindex="-1"></a><span class="in"># Perform MR analysis</span></span>
+<span id="cb3-53"><a href="#cb3-53" aria-hidden="true" tabindex="-1"></a><span class="in">mr(dat)</span></span>
+<span id="cb3-54"><a href="#cb3-54" aria-hidden="true" tabindex="-1"></a><span class="in">mr_heterogeneity(dat)</span></span>
+<span id="cb3-55"><a href="#cb3-55" aria-hidden="true" tabindex="-1"></a><span class="in">dat$exposure &lt;- "LDL cholesterol"</span></span>
+<span id="cb3-56"><a href="#cb3-56" aria-hidden="true" tabindex="-1"></a><span class="in">dat$outcome &lt;- "Coronary heart disease"</span></span>
+<span id="cb3-57"><a href="#cb3-57" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-58"><a href="#cb3-58" aria-hidden="true" tabindex="-1"></a><span class="in"># Label outliers and create plots</span></span>
+<span id="cb3-59"><a href="#cb3-59" aria-hidden="true" tabindex="-1"></a><span class="in">dat$labels &lt;- dat$SNP</span></span>
+<span id="cb3-60"><a href="#cb3-60" aria-hidden="true" tabindex="-1"></a><span class="in">dat$labels[! dat$SNP %in% c("rs11065987", "rs1250229", "rs4530754")] &lt;- NA</span></span>
+<span id="cb3-61"><a href="#cb3-61" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb3-62"><a href="#cb3-62" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-63"><a href="#cb3-63" aria-hidden="true" tabindex="-1"></a>To proceed in Stata we can save our <span class="in">`dat`</span> object as a Stata dataset</span>
 <span id="cb3-64"><a href="#cb3-64" aria-hidden="true" tabindex="-1"></a></span>
@@ -378,11 +384,11 @@ <h2 class="anchored" data-anchor-id="example-r-markdown-document">Example R Mark
 <span id="cb3-66"><a href="#cb3-66" aria-hidden="true" tabindex="-1"></a><span class="in">write.dta(dat, file = "dat.dta")</span></span>
 <span id="cb3-67"><a href="#cb3-67" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb3-68"><a href="#cb3-68" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-69"><a href="#cb3-69" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-70"><a href="#cb3-70" aria-hidden="true" tabindex="-1"></a><span class="fu">## Analysis in Stata using the mrrobust package</span></span>
-<span id="cb3-71"><a href="#cb3-71" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-72"><a href="#cb3-72" aria-hidden="true" tabindex="-1"></a>At this point in Stata install the **mrrobust** package and its</span>
-<span id="cb3-73"><a href="#cb3-73" aria-hidden="true" tabindex="-1"></a>dependencies if you have not done so previously.</span>
+<span id="cb3-69"><a href="#cb3-69" aria-hidden="true" tabindex="-1"></a><span class="fu">## Analysis in Stata using the mrrobust package</span></span>
+<span id="cb3-70"><a href="#cb3-70" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-71"><a href="#cb3-71" aria-hidden="true" tabindex="-1"></a>At this point in Stata install the **mrrobust** package and its</span>
+<span id="cb3-72"><a href="#cb3-72" aria-hidden="true" tabindex="-1"></a>dependencies if you have not done so previously.</span>
+<span id="cb3-73"><a href="#cb3-73" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-74"><a href="#cb3-74" aria-hidden="true" tabindex="-1"></a><span class="in">```stata</span></span>
 <span id="cb3-75"><a href="#cb3-75" aria-hidden="true" tabindex="-1"></a><span class="in">net install github, from("https://haghish.github.io/github/")</span></span>
 <span id="cb3-76"><a href="#cb3-76" aria-hidden="true" tabindex="-1"></a><span class="in">gitget mrrobust</span></span>
@@ -403,32 +409,31 @@ <h2 class="anchored" data-anchor-id="example-r-markdown-document">Example R Mark
 <span id="cb3-91"><a href="#cb3-91" aria-hidden="true" tabindex="-1"></a><span class="in">di _N</span></span>
 <span id="cb3-92"><a href="#cb3-92" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb3-93"><a href="#cb3-93" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-94"><a href="#cb3-94" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-95"><a href="#cb3-95" aria-hidden="true" tabindex="-1"></a>We can then run the IVW model using <span class="in">`mregger`</span> with fixed effect standard errors.</span>
-<span id="cb3-96"><a href="#cb3-96" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-97"><a href="#cb3-97" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
-<span id="cb3-98"><a href="#cb3-98" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe</span></span>
-<span id="cb3-99"><a href="#cb3-99" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb3-100"><a href="#cb3-100" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-94"><a href="#cb3-94" aria-hidden="true" tabindex="-1"></a>We can then run the IVW model using <span class="in">`mregger`</span> with fixed effect standard errors.</span>
+<span id="cb3-95"><a href="#cb3-95" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-96"><a href="#cb3-96" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
+<span id="cb3-97"><a href="#cb3-97" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe</span></span>
+<span id="cb3-98"><a href="#cb3-98" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb3-99"><a href="#cb3-99" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-100"><a href="#cb3-100" aria-hidden="true" tabindex="-1"></a>We then fit the MR-Egger, median, and modal based estimators.</span>
 <span id="cb3-101"><a href="#cb3-101" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-102"><a href="#cb3-102" aria-hidden="true" tabindex="-1"></a>We then fit the MR-Egger, median, and modal based estimators.</span>
-<span id="cb3-103"><a href="#cb3-103" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-104"><a href="#cb3-104" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
-<span id="cb3-105"><a href="#cb3-105" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]</span></span>
-<span id="cb3-106"><a href="#cb3-106" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb3-107"><a href="#cb3-107" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-108"><a href="#cb3-108" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
-<span id="cb3-109"><a href="#cb3-109" aria-hidden="true" tabindex="-1"></a><span class="in">mrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
-<span id="cb3-110"><a href="#cb3-110" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb3-111"><a href="#cb3-111" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-112"><a href="#cb3-112" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
-<span id="cb3-113"><a href="#cb3-113" aria-hidden="true" tabindex="-1"></a><span class="in">mrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
-<span id="cb3-114"><a href="#cb3-114" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb3-115"><a href="#cb3-115" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-116"><a href="#cb3-116" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-117"><a href="#cb3-117" aria-hidden="true" tabindex="-1"></a>And we could continue with additional Stata code (or indeed R code) as we like.</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>To run this R Markdown file, either; open it in RStudio and click the <code>Knit</code> button in the toolbar of the Source code window, or run</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>rmarkdown<span class="sc">::</span><span class="fu">render</span>(<span class="st">'rmarkdown-call-stata-example.Rmd'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb3-102"><a href="#cb3-102" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
+<span id="cb3-103"><a href="#cb3-103" aria-hidden="true" tabindex="-1"></a><span class="in">mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]</span></span>
+<span id="cb3-104"><a href="#cb3-104" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb3-105"><a href="#cb3-105" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-106"><a href="#cb3-106" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
+<span id="cb3-107"><a href="#cb3-107" aria-hidden="true" tabindex="-1"></a><span class="in">mrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
+<span id="cb3-108"><a href="#cb3-108" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb3-109"><a href="#cb3-109" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-110"><a href="#cb3-110" aria-hidden="true" tabindex="-1"></a><span class="in">```{stata}</span></span>
+<span id="cb3-111"><a href="#cb3-111" aria-hidden="true" tabindex="-1"></a><span class="in">mrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted</span></span>
+<span id="cb3-112"><a href="#cb3-112" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb3-113"><a href="#cb3-113" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-114"><a href="#cb3-114" aria-hidden="true" tabindex="-1"></a>And we could continue with additional Stata code (or indeed R code) as we like.</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>To render your R Markdown file, either; open it in RStudio and click the <code>Knit</code> button in the toolbar of the Source code window, or run</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>rmarkdown<span class="sc">::</span><span class="fu">render</span>(<span class="st">'myanalysis.Rmd'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>or to render a Quarto file run the following in your Terminal</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode sh code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="ex">quarto</span> render myanalysis.qmd</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </section>
 <section id="references" class="level2">
 <h2 class="anchored" data-anchor-id="references">References</h2>
@@ -478,18 +483,7 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -521,7 +515,47 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -556,7 +590,11 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/examples/save-estimates/index.html b/docs/examples/save-estimates/index.html
index faf2862b..40367414 100644
--- a/docs/examples/save-estimates/index.html
+++ b/docs/examples/save-estimates/index.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>index – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -117,7 +117,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -127,7 +127,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -158,7 +158,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -245,7 +245,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -281,24 +281,18 @@ <h1>How to collect and export results</h1>
 <h2 class="anchored" data-anchor-id="setup-example-data">Setup example data</h2>
 <div class="cell" data-layout-align="center" data-collectcode="true">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="kw">use</span> https:<span class="co">//raw.github.com/remlapmot/mrrobust/master/dodata, clear</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
 </div>
 <p>Select observations (p-value with exposure &lt; 10<sup>-8</sup>)</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> <span class="kw">byte</span> sel1 = (ldlcp2 &lt; 1e-8)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> <span class="kw">byte</span> sel1 = (ldlcp2 &lt; 1e-8)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
 <section id="fit" class="level2">
 <h2 class="anchored" data-anchor-id="fit">Fit estimators - collecting results using <code>r(table)</code> matrix</h2>
 <p>IVW (with fixed effect standard errors)</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>mregger chdbeta ldlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, ivw <span class="kw">fe</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>mat ivw = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>mregger chdbeta ldlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, ivw <span class="kw">fe</span></span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>mat ivw = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                       Residual standard error constrained at 1
@@ -312,8 +306,8 @@ <h2 class="anchored" data-anchor-id="fit">Fit estimators - collecting results us
 </div>
 <p>MR-Egger (with SEs using an unconstrained residual variance)</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>mregger chdbeta ldlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>mat mregger = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>mregger chdbeta ldlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>mat mregger = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                               Residual standard error =  1.548
@@ -328,8 +322,8 @@ <h2 class="anchored" data-anchor-id="fit">Fit estimators - collecting results us
 </div>
 <p>MR-Egger using the radial formulation</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>mregger chdbeta ldlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, radial</span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>mat radial = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>mregger chdbeta ldlcbeta [aw=1/(chdse^2)] <span class="kw">if</span> sel1==1, radial</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>mat radial = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                               Residual standard error =  1.547
@@ -344,8 +338,8 @@ <h2 class="anchored" data-anchor-id="fit">Fit estimators - collecting results us
 </div>
 <p>Weighted mode estimator</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>mat <span class="kw">mode</span> = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>mrmodal chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>mat <span class="kw">mode</span> = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                            Replications = 1000
@@ -359,8 +353,8 @@ <h2 class="anchored" data-anchor-id="fit">Fit estimators - collecting results us
 </div>
 <p>Weighted median estimator</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>mrmedian chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>mat <span class="kw">median</span> = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>mrmedian chdbeta chdse ldlcbeta ldlcse <span class="kw">if</span> sel1==1, weighted</span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>mat <span class="kw">median</span> = <span class="fu">r</span>(<span class="kw">table</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 73
                                                            Replications = 1000
@@ -376,12 +370,12 @@ <h2 class="anchored" data-anchor-id="fit">Fit estimators - collecting results us
 <h2 class="anchored" data-anchor-id="combine-and-export-results">Combine and export results</h2>
 <p>Check our matrices</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">dir</span></span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> ivw</span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> mregger</span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> radial</span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> <span class="kw">mode</span></span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> <span class="kw">median</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">dir</span></span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> ivw</span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> mregger</span>
+<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> radial</span>
+<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> <span class="kw">mode</span></span>
+<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> <span class="kw">median</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>       median[9,1]
          mode[9,1]
@@ -460,12 +454,12 @@ <h2 class="anchored" data-anchor-id="combine-and-export-results">Combine and exp
 </div>
 <p>Combined into single matrix</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>mat output = (ivw, mregger, radial, <span class="kw">mode</span>, <span class="kw">median</span>)</span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">colnames</span> output = ivw_beta mregger_beta mregger_cons <span class="co">///</span></span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>    radial_beta radial_cons mode_beta median_beta</span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">coleq</span> output = <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span></span>
-<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>mat output = output'</span>
-<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> output, <span class="kw">format</span>(%4.3f)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>mat output = (ivw, mregger, radial, <span class="kw">mode</span>, <span class="kw">median</span>)</span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">colnames</span> output = ivw_beta mregger_beta mregger_cons <span class="co">///</span></span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>    radial_beta radial_cons mode_beta median_beta</span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">coleq</span> output = <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span> <span class="st">""</span></span>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>mat output = output'</span>
+<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>mat <span class="ot">list</span> output, <span class="kw">format</span>(%4.3f)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>output[7,9]
                    b      se       z  pvalue      ll      ul      df    crit   eform
@@ -480,19 +474,19 @@ <h2 class="anchored" data-anchor-id="combine-and-export-results">Combine and exp
 </div>
 <p>Export matrix to dataset</p>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="kw">drop</span> <span class="dt">_all</span></span>
-<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="kw">svmat</span> output, names(col)</span>
-<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a><span class="kw">local</span> <span class="ot">rownames</span> : <span class="ot">rownames</span> output</span>
-<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a><span class="kw">di</span> <span class="st">"`rownames'"</span></span>
-<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a><span class="kw">tokenize</span> <span class="ot">`rownames'</span></span>
-<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> str15 estimate = <span class="st">""</span></span>
-<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a><span class="kw">forvalues</span> i = 1/7 {</span>
-<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a>    <span class="kw">replace</span> estimate = <span class="st">"``i''"</span> <span class="kw">in</span> <span class="ot">`i'</span></span>
-<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="kw">drop</span> <span class="dt">_all</span></span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="kw">svmat</span> output, names(col)</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="kw">local</span> <span class="ot">rownames</span> : <span class="ot">rownames</span> output</span>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="kw">di</span> <span class="st">"`rownames'"</span></span>
+<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a><span class="kw">tokenize</span> <span class="ot">`rownames'</span></span>
+<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="kw">gen</span> str15 estimate = <span class="st">""</span></span>
+<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a><span class="kw">forvalues</span> i = 1/7 {</span>
+<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a>    <span class="kw">replace</span> estimate = <span class="st">"``i''"</span> <span class="kw">in</span> <span class="ot">`i'</span></span>
+<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Show dataset</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="ot">list</span> estimate b se z pvalue ll ul, <span class="kw">clean</span> <span class="kw">noobs</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="ot">list</span> estimate b se z pvalue ll ul, <span class="kw">clean</span> <span class="kw">noobs</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>        estimate           b         se          z     pvalue          ll         ul  
         ivw_beta    .4815055    .038221   12.59794   2.17e-36    .4065938   .5564172  
@@ -505,9 +499,9 @@ <h2 class="anchored" data-anchor-id="combine-and-export-results">Combine and exp
 </div>
 </div>
 <p>Save dataset</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="kw">save</span> myestimates, <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb20"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="kw">save</span> myestimates, <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>Export as tab-delimited textfile</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="kw">export</span> delimited <span class="kw">using</span> myestimates.txt, delimiter(<span class="kw">tab</span>) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb21"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="kw">export</span> delimited <span class="kw">using</span> myestimates.txt, delimiter(<span class="kw">tab</span>) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 
 
 </section>
@@ -549,18 +543,7 @@ <h2 class="anchored" data-anchor-id="combine-and-export-results">Combine and exp
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -592,7 +575,47 @@ <h2 class="anchored" data-anchor-id="combine-and-export-results">Combine and exp
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -627,7 +650,11 @@ <h2 class="anchored" data-anchor-id="combine-and-export-results">Combine and exp
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/examples/spiller-ije-2018-examples/index.html b/docs/examples/spiller-ije-2018-examples/index.html
index d89b34f2..95a39978 100644
--- a/docs/examples/spiller-ije-2018-examples/index.html
+++ b/docs/examples/spiller-ije-2018-examples/index.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust - Examples from our IJE paper</title>
+<title>Examples from our IJE paper – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -121,7 +121,7 @@
   }
 }</script>
 
-  <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+  <script src="https://cdnjs.cloudflare.com/polyfill/v3/polyfill.min.js?features=es6"></script>
   <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml-full.js" type="text/javascript"></script>
 
 <script type="text/javascript">
@@ -166,7 +166,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -176,7 +176,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -207,7 +207,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -294,7 +294,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -364,7 +364,7 @@ <h2 class="anchored" data-anchor-id="mrrobust-set-up">mrrobust set-up</h2>
 </section>
 <section id="summary-data-description-and-overview" class="level2">
 <h2 class="anchored" data-anchor-id="summary-data-description-and-overview">Summary data description and overview</h2>
-<p>Accompanying this paper are two sets of data <code>BMI.csv</code>, and <code>Height.csv</code>, containing the set of summary estimates required for performing the BMI-serum glucose and height-serum glucose analyses respectively. Each dataset is organised into 5 columns under the following headings:</p>
+<p>Accompanying this paper are two sets of data <em>BMI.csv</em>, and <em>Height.csv</em>, containing the set of summary estimates required for performing the BMI-serum glucose and height-serum glucose analyses respectively. Each dataset is organised into 5 columns under the following headings:</p>
 <ul>
 <li><code>SNP</code>: A set of identifying numbers (rsids) for each genetic variant</li>
 <li><code>beta.exposure</code>: a set of values representing the coefficient from regressing the exposure upon the genetic variant within a GWAS</li>
@@ -373,8 +373,8 @@ <h2 class="anchored" data-anchor-id="summary-data-description-and-overview">Summ
 <li><code>se.outcome</code>: a set of values representing the standard error corresponding to the coefficient in beta.outcome.</li>
 </ul>
 <p>Note Stata removes the <code>.</code> in the variable names when the data is imported.</p>
-<p>In <code>BMI.csv</code> the exposure is standardised body mass index (BMI), and is therefore interpreted on a standard deviation scale. The summary statistics are reported by <span class="citation" data-cites="locke-nat-2015">Locke et al. (<a href="#ref-locke-nat-2015" role="doc-biblioref">2015</a>)</span>. In <code>Height.csv</code> the exposure is standardised height in meters and also interpreted on a standard deviation scale. The summary statistics are reported by <span class="citation" data-cites="wood-natgen-2014">Wood et al. (<a href="#ref-wood-natgen-2014" role="doc-biblioref">2014</a>)</span>.</p>
-<p>For both analyses log transformed serum glucose was used as an outcome, reported by <span class="citation" data-cites="shin-natgen-2014">Shin et al. (<a href="#ref-shin-natgen-2014" role="doc-biblioref">2014</a>)</span>. All the data was obtained from the MRBase GWAS catalogue available at <a href="https://www.mrbase.org/" class="uri">https://www.mrbase.org/</a> <span class="citation" data-cites="mrbase">(<a href="#ref-mrbase" role="doc-biblioref">Hemani et al. 2018</a>)</span>. Genetic variants were pruned so as to be independent (<span class="math inline">\(R^2\)</span> = 0.0001), and the effect alleles were aligned between the exposure and outcome datasets using the MRBase web application, prior to implementing <code>mrrobust</code>.</p>
+<p>In <em>BMI.csv</em> the exposure is standardised body mass index (BMI), and is therefore interpreted on a standard deviation scale. The summary statistics are reported by <span class="citation" data-cites="locke-nat-2015">Locke et al. (<a href="#ref-locke-nat-2015" role="doc-biblioref">2015</a>)</span>. In <em>Height.csv</em> the exposure is standardised height in meters and also interpreted on a standard deviation scale. The summary statistics are reported by <span class="citation" data-cites="wood-natgen-2014">Wood et al. (<a href="#ref-wood-natgen-2014" role="doc-biblioref">2014</a>)</span>.</p>
+<p>For both analyses log transformed serum glucose was used as an outcome, reported by <span class="citation" data-cites="shin-natgen-2014">Shin et al. (<a href="#ref-shin-natgen-2014" role="doc-biblioref">2014</a>)</span>. All the data was obtained from the MRBase GWAS catalogue available at <a href="https://www.mrbase.org/" class="uri">https://www.mrbase.org/</a> <span class="citation" data-cites="mrbase">(<a href="#ref-mrbase" role="doc-biblioref">Hemani et al. 2018</a>)</span> which has now been superseeded by the <a href="https://api.opengwas.io/">OpenGWAS API</a>. Genetic variants were pruned so as to be independent (<span class="math inline">\(R^2\)</span> = 0.0001), and the effect alleles were aligned between the exposure and outcome datasets using the MRBase web application, prior to implementing <code>mrrobust</code>.</p>
 </section>
 <section id="estimation" class="level2">
 <h2 class="anchored" data-anchor-id="estimation">Stata output for each estimation method using mrrobust: BMI-Serum Glucose</h2>
@@ -422,15 +422,12 @@ <h3 class="anchored" data-anchor-id="plot-of-the-mr-egger-model">Plot of the MR-
 <div class="cell" data-layout-align="center">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>mreggerplot betaoutcome seoutcome betaexposure seexposure</span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mreggerplot-bmi.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
 </div>
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mreggerplot-bmi.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Plot of the MR-Egger model for the BMI data."></p>
+<p><img src="mreggerplot-bmi.svg" class="img-fluid figure-img" alt="Plot of the MR-Egger model for the BMI data."></p>
 <figcaption>Plot of the MR-Egger model for the BMI data.</figcaption>
 </figure>
 </div>
@@ -440,7 +437,7 @@ <h3 class="anchored" data-anchor-id="plot-of-the-mr-egger-model">Plot of the MR-
 <section id="weighted-median" class="level3">
 <h3 class="anchored" data-anchor-id="weighted-median">Weighted median</h3>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>mrmedian betaoutcome seoutcome betaexposure seexposure, weighted <span class="dv">seed</span>(300818)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>mrmedian betaoutcome seoutcome betaexposure seexposure, weighted <span class="dv">seed</span>(300818)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 79
                                                            Replications = 1000
@@ -457,8 +454,8 @@ <h3 class="anchored" data-anchor-id="weighted-median">Weighted median</h3>
 <h2 class="anchored" data-anchor-id="mode">Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose</h2>
 <p>Using the <code>mrmodalplot</code> command, modal estimates are calculated using bandwidths of 0.25, 0.5, and 1 respectively. This command also produces three overlaid density plots for each value, as shown in the Figure.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>mrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) <span class="dv">seed</span>(300818)</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrmodalplot-bmi.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>mrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) <span class="dv">seed</span>(300818)</span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrmodalplot-bmi.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                      Number of genotypes = 79
                                                            Replications = 1000
@@ -492,7 +489,7 @@ <h2 class="anchored" data-anchor-id="mode">Stata output using the mode-based est
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mrmodalplot-bmi.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Densities of the IV estimates using different values of phi."></p>
+<p><img src="mrmodalplot-bmi.svg" class="img-fluid figure-img" alt="Densities of the IV estimates using different values of phi."></p>
 <figcaption>Densities of the IV estimates using different values of phi.</figcaption>
 </figure>
 </div>
@@ -504,13 +501,13 @@ <h2 class="anchored" data-anchor-id="height">Stata output for each estimation me
 <section id="read-in-data-1" class="level3">
 <h3 class="anchored" data-anchor-id="read-in-data-1">Read in data</h3>
 <div class="cell" data-layout-align="center" data-collectcode="true">
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>import delimited <span class="kw">using</span> Height.csv, <span class="kw">clear</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>import delimited <span class="kw">using</span> Height.csv, <span class="kw">clear</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
 <section id="ivw-1" class="level3">
 <h3 class="anchored" data-anchor-id="ivw-1">IVW</h3>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>mregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>mregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                     Number of genotypes = 367
                                               Residual standard error =  1.044
@@ -526,7 +523,7 @@ <h3 class="anchored" data-anchor-id="ivw-1">IVW</h3>
 <section id="mr-egger-1" class="level3">
 <h3 class="anchored" data-anchor-id="mr-egger-1">MR-Egger</h3>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>mregger betaoutcome betaexposure [aw=1/(seoutcome^2)]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>mregger betaoutcome betaexposure [aw=1/(seoutcome^2)]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                     Number of genotypes = 367
                                               Residual standard error =  1.045
@@ -543,17 +540,14 @@ <h3 class="anchored" data-anchor-id="mr-egger-1">MR-Egger</h3>
 <section id="plot-of-the-mr-egger-model-1" class="level3">
 <h3 class="anchored" data-anchor-id="plot-of-the-mr-egger-model-1">Plot of the MR-Egger model</h3>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>mreggerplot betaoutcome seoutcome betaexposure seexposure</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mreggerplot-height.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code></code></pre>
-</div>
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>mreggerplot betaoutcome seoutcome betaexposure seexposure</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mreggerplot-height.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mreggerplot-height.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Plot of the MR-Egger model for the Height data."></p>
+<p><img src="mreggerplot-height.svg" class="img-fluid figure-img" alt="Plot of the MR-Egger model for the Height data."></p>
 <figcaption>Plot of the MR-Egger model for the Height data.</figcaption>
 </figure>
 </div>
@@ -563,7 +557,7 @@ <h3 class="anchored" data-anchor-id="plot-of-the-mr-egger-model-1">Plot of the M
 <section id="weighted-median-1" class="level3">
 <h3 class="anchored" data-anchor-id="weighted-median-1">Weighted median</h3>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>mrmedian betaoutcome seoutcome betaexposure seexposure, weighted <span class="dv">seed</span>(300818)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>mrmedian betaoutcome seoutcome betaexposure seexposure, weighted <span class="dv">seed</span>(300818)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                     Number of genotypes = 367
                                                            Replications = 1000
@@ -579,8 +573,8 @@ <h3 class="anchored" data-anchor-id="weighted-median-1">Weighted median</h3>
 <section id="height-mode" class="level2">
 <h2 class="anchored" data-anchor-id="height-mode">Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose</h2>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>mrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) <span class="dv">seed</span>(300818)</span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrmodalplot-height.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode stata code-with-copy"><code class="sourceCode stata"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>mrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) <span class="dv">seed</span>(300818)</span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="kw">qui</span> <span class="kw">gr</span> <span class="kw">export</span> mrmodalplot-height.svg, <span class="kw">width</span>(600) <span class="kw">replace</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>                                                     Number of genotypes = 367
                                                            Replications = 1000
@@ -614,7 +608,7 @@ <h2 class="anchored" data-anchor-id="height-mode">Stata output using the mode-ba
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="mrmodalplot-height.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" alt="Densities of the IV estimates using different values of phi."></p>
+<p><img src="mrmodalplot-height.svg" class="img-fluid figure-img" alt="Densities of the IV estimates using different values of phi."></p>
 <figcaption>Densities of the IV estimates using different values of phi.</figcaption>
 </figure>
 </div>
@@ -680,18 +674,7 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -723,7 +706,47 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -758,7 +781,11 @@ <h2 class="anchored" data-anchor-id="references">References</h2>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg b/docs/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg
index 66c3dbf0..df539194 100644
--- a/docs/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg
+++ b/docs/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg
@@ -1,461 +1,621 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!-- This is a Stata 17.0 generated SVG file (http://www.stata.com) -->
+<!-- This is a Stata 18.5 generated SVG file (http://www.stata.com) -->
 
-<svg version="1.1" width="600px" height="436px" viewBox="0 0 3960 2880" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<svg version="1.1" width="600px" height="360px" viewBox="0 0 5400 3240" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
 	<desc>Stata Graph - Graph</desc>
-	<rect x="0" y="0" width="3960" height="2880" style="fill:#EAF2F3;stroke:none"/>
-	<rect x="0.00" y="0.00" width="3959.88" height="2880.00" style="fill:#FFFFFF"/>
-	<rect x="2.88" y="2.88" width="3954.12" height="2874.24" style="fill:none;stroke:#FFFFFF;stroke-width:5.76"/>
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-	<rect x="371.90" y="103.74" width="3484.24" height="2127.01" style="fill:none;stroke:#FFFFFF;stroke-width:5.76"/>
-	<line x1="369.02" y1="2170.27" x2="3859.02" y2="2170.27" style="stroke:#EAF2F3;stroke-width:8.64"/>
-	<line x1="369.02" y1="1790.59" x2="3859.02" y2="1790.59" style="stroke:#EAF2F3;stroke-width:8.64"/>
-	<line x1="369.02" y1="1410.79" x2="3859.02" y2="1410.79" style="stroke:#EAF2F3;stroke-width:8.64"/>
-	<line x1="369.02" y1="1030.99" x2="3859.02" y2="1030.99" style="stroke:#EAF2F3;stroke-width:8.64"/>
-	<line x1="369.02" y1="651.19" x2="3859.02" y2="651.19" style="stroke:#EAF2F3;stroke-width:8.64"/>
-	<line x1="369.02" y1="271.39" x2="3859.02" y2="271.39" style="stroke:#EAF2F3;stroke-width:8.64"/>
+	<rect x="0" y="0" width="5400" height="3240" style="fill:#FFFFFF;stroke:none"/>
+	<rect x="0.00" y="0.00" width="5400.00" height="3240.00" style="fill:#FFFFFF"/>
+	<rect x="3.24" y="3.24" width="5393.52" height="3233.52" style="fill:none;stroke:#FFFFFF;stroke-width:6.48"/>
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+	<text x="391.84" y="224.05" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="end">6</text>
+	<text x="214.55" y="1439.94" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" transform="rotate(-90 214.55,1439.94)" text-anchor="middle">Density</text>
+	<line x1="459.34" y1="2766.66" x2="2828.42" y2="2766.66" style="stroke:#000000;stroke-width:6.48"/>
+	<line x1="619.99" y1="2766.66" x2="619.99" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="619.99" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">-.4</text>
+	<line x1="1154.25" y1="2766.66" x2="1154.25" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="1154.25" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">-.2</text>
+	<line x1="1688.51" y1="2766.66" x2="1688.51" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="1688.51" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">0</text>
+	<line x1="2222.78" y1="2766.66" x2="2222.78" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="2222.78" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">.2</text>
+	<line x1="2757.04" y1="2766.66" x2="2757.04" y2="2811.54" style="stroke:#000000;stroke-width:6.48"/>
+	<text x="2757.04" y="2912.86" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">.4</text>
+	<text x="1643.79" y="3070.31" style="font-family:'Helvetica';font-size:112.56px;fill:#000000" text-anchor="middle">IV estimates</text>
+	<rect x="2867.23" y="1335.15" width="2380.56" height="209.76" style="fill:#FFFFFF"/>
+	<rect x="2870.47" y="1338.39" width="2374.08" height="203.28" style="fill:none;stroke:#FFFFFF;stroke-width:6.48"/>
+	<line x1="2915.83" y1="1439.94" x2="3175.03" y2="1439.94" style="stroke:#A0A0A0;stroke-width:9.72"/>
+	<line x1="3784.22" y1="1439.94" x2="4043.42" y2="1439.94" style="stroke:#505050;stroke-width:9.72"/>
+	<line x1="4586.79" y1="1439.94" x2="4845.99" y2="1439.94" style="stroke:#000000;stroke-width:9.72"/>
+	<text x="3242.53" y="1479.38" style="font-family:'Helvetica';font-size:112.56px;fill:#000000">φ = .25</text>
+	<text x="4110.92" y="1479.38" style="font-family:'Helvetica';font-size:112.56px;fill:#000000">φ = .5</text>
+	<text x="4913.49" y="1479.38" style="font-family:'Helvetica';font-size:112.56px;fill:#000000">φ = 1</text>
 </svg>
diff --git a/docs/helpfiles/mr-helpfile.html b/docs/helpfiles/mr-helpfile.html
index 3cbbccce..965f4786 100644
--- a/docs/helpfiles/mr-helpfile.html
+++ b/docs/helpfiles/mr-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mr-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrdeps-helpfile.html b/docs/helpfiles/mrdeps-helpfile.html
index c7adaa01..4bfc8248 100644
--- a/docs/helpfiles/mrdeps-helpfile.html
+++ b/docs/helpfiles/mrdeps-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrdeps-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mregger-helpfile.html b/docs/helpfiles/mregger-helpfile.html
index 3abeb3b7..bd882667 100644
--- a/docs/helpfiles/mregger-helpfile.html
+++ b/docs/helpfiles/mregger-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mregger-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mreggerplot-helpfile.html b/docs/helpfiles/mreggerplot-helpfile.html
index 94d19007..0226b8dc 100644
--- a/docs/helpfiles/mreggerplot-helpfile.html
+++ b/docs/helpfiles/mreggerplot-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mreggerplot-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mreggersimex-helpfile.html b/docs/helpfiles/mreggersimex-helpfile.html
index bae7da47..98c94baf 100644
--- a/docs/helpfiles/mreggersimex-helpfile.html
+++ b/docs/helpfiles/mreggersimex-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mreggersimex-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrforest-helpfile.html b/docs/helpfiles/mrforest-helpfile.html
index 9eb84a39..a49a6ca5 100644
--- a/docs/helpfiles/mrforest-helpfile.html
+++ b/docs/helpfiles/mrforest-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrforest-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrfunnel-helpfile.html b/docs/helpfiles/mrfunnel-helpfile.html
index bc402858..bd8160d6 100644
--- a/docs/helpfiles/mrfunnel-helpfile.html
+++ b/docs/helpfiles/mrfunnel-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrfunnel-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrivests-helpfile.html b/docs/helpfiles/mrivests-helpfile.html
index 0d997577..5bc58171 100644
--- a/docs/helpfiles/mrivests-helpfile.html
+++ b/docs/helpfiles/mrivests-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrivests-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrleaveoneout-helpfile.html b/docs/helpfiles/mrleaveoneout-helpfile.html
index a9baaf77..4d4b7881 100644
--- a/docs/helpfiles/mrleaveoneout-helpfile.html
+++ b/docs/helpfiles/mrleaveoneout-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrleaveoneout-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrmedian-helpfile.html b/docs/helpfiles/mrmedian-helpfile.html
index 00ce8e1a..e95b638e 100644
--- a/docs/helpfiles/mrmedian-helpfile.html
+++ b/docs/helpfiles/mrmedian-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrmedian-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrmedianobs-helpfile.html b/docs/helpfiles/mrmedianobs-helpfile.html
index ee723caa..09e7bb82 100644
--- a/docs/helpfiles/mrmedianobs-helpfile.html
+++ b/docs/helpfiles/mrmedianobs-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrmedianobs-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrmodal-helpfile.html b/docs/helpfiles/mrmodal-helpfile.html
index c2fb471e..5d89a4fe 100644
--- a/docs/helpfiles/mrmodal-helpfile.html
+++ b/docs/helpfiles/mrmodal-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrmodal-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrmodalplot-helpfile.html b/docs/helpfiles/mrmodalplot-helpfile.html
index abf19882..92b1326b 100644
--- a/docs/helpfiles/mrmodalplot-helpfile.html
+++ b/docs/helpfiles/mrmodalplot-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrmodalplot-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrmvegger-helpfile.html b/docs/helpfiles/mrmvegger-helpfile.html
index 0c187f82..333349a5 100644
--- a/docs/helpfiles/mrmvegger-helpfile.html
+++ b/docs/helpfiles/mrmvegger-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrmvegger-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrmvivw-helpfile.html b/docs/helpfiles/mrmvivw-helpfile.html
index aec30193..de0c9ff4 100644
--- a/docs/helpfiles/mrmvivw-helpfile.html
+++ b/docs/helpfiles/mrmvivw-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrmvivw-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -313,7 +302,47 @@
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -348,7 +377,11 @@
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/helpfiles/mrratio-helpfile.html b/docs/helpfiles/mrratio-helpfile.html
index 38eccca5..b7bb618f 100644
--- a/docs/helpfiles/mrratio-helpfile.html
+++ b/docs/helpfiles/mrratio-helpfile.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.5.55">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>mrrobust</title>
+<title>mrratio-helpfile – mrrobust</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -83,7 +83,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -93,7 +93,7 @@
 <span class="menu-text">Home</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Examples</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-examples">    
@@ -124,7 +124,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Helpfiles</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-helpfiles">    
@@ -211,7 +211,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -220,7 +220,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -270,18 +270,7 @@
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
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@@ -313,7 +302,47 @@
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+      const codeEl = trigger.previousElementSibling.cloneNode(true);
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+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
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+      var filterRegex = new RegExp('/' + window.location.host + '/');
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+    }
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@@ -348,7 +377,11 @@
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index c5b4b67d..f91c8710 100644
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+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
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@@ -270,18 +270,7 @@
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@@ -313,7 +302,47 @@
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+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
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+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
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+  }
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+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
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+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
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+        }
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diff --git a/docs/index.html b/docs/index.html
index 4cebdbb3..86a538fb 100644
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+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
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-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-examples" role="link" data-bs-toggle="dropdown" aria-expanded="false">
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-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-helpfiles" role="link" data-bs-toggle="dropdown" aria-expanded="false">
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@@ -306,6 +306,10 @@ <h1>mrrobust: Stata package for two-sample Mendelian randomization analyses</h1>
 <h2 class="anchored" data-anchor-id="latest-updates">Latest updates</h2>
 <p>To obtain the latest update please see the instructions <a href="#installing-and-updating-mrrobust">below</a>.</p>
 <ul>
+<li>July 2024:
+<ul>
+<li>Updated website to mention <a href="https://api.opengwas.io/">OpenGWAS</a> instead of MR-Base</li>
+</ul></li>
 <li>January 2024:
 <ul>
 <li>Reran certification scripts under Stata 18.0</li>
@@ -599,18 +603,7 @@ <h2 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h2>
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-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
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@@ -642,7 +635,47 @@ <h2 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h2>
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+    text: getTextToCopy
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+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
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+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
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+        // undo the damage that might have been done by quarto-nav.js in the case of
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+        if (link.dataset.originalHref !== undefined) {
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+    }
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@@ -677,7 +710,11 @@ <h2 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h2>
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       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
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diff --git a/docs/search.json b/docs/search.json
index 84e015d1..ba11fe2d 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1,129 +1,129 @@
 [
   {
-    "objectID": "examples/markstat-call-R-example/index.html",
-    "href": "examples/markstat-call-R-example/index.html",
-    "title": "Run MR-Base from a Stata do-file or Stata Markdown script",
+    "objectID": "examples/mrrobust-examples/index.html",
+    "href": "examples/mrrobust-examples/index.html",
+    "title": "Examples from helpfiles in the mrrobust package",
     "section": "",
-    "text": "This example shows how to run R and Stata code within the same Stata Markdown (.stmd) script. The general approach is detailed on the Stata Markdown website here and here.\nThis means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base (Hemani et al. 2018).\nBefore you start please install the following two Stata packages from the SSC archive, so in Stata issue the following commands (I have commented them out because I have already installed them).\nssc install whereis\nssc install markstat\n\n\n\nThe following is the example Stata Markdown document.\n---\ntitle: Using mrrobust in a Markstat document example\n---\n\nWe first need to register the R executable with Stata.\n```s\nif c(os) == \"Windows\" local rpath \"C:\\\\Program Files\\\\R\\\\R-4.3.0\\\\bin\\\\x64\\\\R.exe\"\nelse if c(os) == \"Unix\" local rpath \"/usr/bin/R\"\nelse local rpath \"/usr/local/bin/R\"\nwhereis R \"`rpath'\"\n```\n\n\nNext we have an R code chunk in which we install the required packages in R.\n```r\ninstall.packages(\"remotes\")\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\n```\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). \nThe R code we will use is from \n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nFirst, we load the packages into our R session.\nNote that the **foreign** package provides the \n`write.dta()` function which we will use to save the data in Stata format.\n```r\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\nOur edited version of the code starts by reading in some code\nto generate a set of plots in R.\n```r\nsource(\"mrplots.R\")\n```\n\nWe can access the data using the **MRInstruments** package.\n```r\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;- \n  subset(gwas_catalog, \n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;- \n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\nAt this point we have our harmonised genotype-exposure and \ngenotype-outcome association data saved in an object in our \nR session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n```r\n# Perform MR\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\n```r\npng(\"ldl-chd.png\", width=1000, height=1000)\nmr_plots(dat)\ndev.off()\n```\n\n![Plots generated by the TwoSampleMR R package.](ldl-chd.png)\n\nWe now save our `dat` object as a Stata dataset.\n```r\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Performing the analysis using mrrobust in Stata\n\nWe now switch from using R code chunks to Stata code chunks.\nWe read the data into Stata and list the variable names\n(note any `.` in the colnames of `dat` have been replaced with `_`).\n```s\nuse dat, clear\nds, v(28)\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with multiplicative\nstandard errors.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw\n```\n\nIt is helpful to view the forest plot of genotype specific IV estimates.\n```s\nmrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///\n    xlabel(-3,-2,-1,0,1,2,3)\ngraph export ldl-chd-mrforest.svg, width(600) replace\n```\n\n![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)\n\nWe can visualise this model with `mreggerplot`.\n```s\nmreggerplot beta_outcome se_outcome beta_exposure se_exposure\ngraph export ldl-chd-mreggerplot.svg, width(600) replace\n```\n\n![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)\n\nWe then fit the MR-Egger, median, and modal based estimators.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```s\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```s\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\nAnd we could continue with additional Stata code (or indeed R code) as we liked.\nNote to run this .stmd file in Stata we do so with the following command (specifying additional options as required, see help markstat for more information).\nmarkstat using markstat-call-R-example\n\n\n\n\n\nHemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408."
+    "text": "net install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust"
   },
   {
-    "objectID": "examples/markstat-call-R-example/index.html#introduction",
-    "href": "examples/markstat-call-R-example/index.html#introduction",
-    "title": "Run MR-Base from a Stata do-file or Stata Markdown script",
+    "objectID": "examples/mrrobust-examples/index.html#install-the-mrrobust-package",
+    "href": "examples/mrrobust-examples/index.html#install-the-mrrobust-package",
+    "title": "Examples from helpfiles in the mrrobust package",
     "section": "",
-    "text": "This example shows how to run R and Stata code within the same Stata Markdown (.stmd) script. The general approach is detailed on the Stata Markdown website here and here.\nThis means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base (Hemani et al. 2018).\nBefore you start please install the following two Stata packages from the SSC archive, so in Stata issue the following commands (I have commented them out because I have already installed them).\nssc install whereis\nssc install markstat"
+    "text": "net install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust"
   },
   {
-    "objectID": "examples/markstat-call-R-example/index.html#example-stata-markdown-document",
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-    "text": "The following is the example Stata Markdown document.\n---\ntitle: Using mrrobust in a Markstat document example\n---\n\nWe first need to register the R executable with Stata.\n```s\nif c(os) == \"Windows\" local rpath \"C:\\\\Program Files\\\\R\\\\R-4.3.0\\\\bin\\\\x64\\\\R.exe\"\nelse if c(os) == \"Unix\" local rpath \"/usr/bin/R\"\nelse local rpath \"/usr/local/bin/R\"\nwhereis R \"`rpath'\"\n```\n\n\nNext we have an R code chunk in which we install the required packages in R.\n```r\ninstall.packages(\"remotes\")\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\n```\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). \nThe R code we will use is from \n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nFirst, we load the packages into our R session.\nNote that the **foreign** package provides the \n`write.dta()` function which we will use to save the data in Stata format.\n```r\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\nOur edited version of the code starts by reading in some code\nto generate a set of plots in R.\n```r\nsource(\"mrplots.R\")\n```\n\nWe can access the data using the **MRInstruments** package.\n```r\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;- \n  subset(gwas_catalog, \n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;- \n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\nAt this point we have our harmonised genotype-exposure and \ngenotype-outcome association data saved in an object in our \nR session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n```r\n# Perform MR\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\n```r\npng(\"ldl-chd.png\", width=1000, height=1000)\nmr_plots(dat)\ndev.off()\n```\n\n![Plots generated by the TwoSampleMR R package.](ldl-chd.png)\n\nWe now save our `dat` object as a Stata dataset.\n```r\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Performing the analysis using mrrobust in Stata\n\nWe now switch from using R code chunks to Stata code chunks.\nWe read the data into Stata and list the variable names\n(note any `.` in the colnames of `dat` have been replaced with `_`).\n```s\nuse dat, clear\nds, v(28)\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with multiplicative\nstandard errors.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw\n```\n\nIt is helpful to view the forest plot of genotype specific IV estimates.\n```s\nmrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///\n    xlabel(-3,-2,-1,0,1,2,3)\ngraph export ldl-chd-mrforest.svg, width(600) replace\n```\n\n![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)\n\nWe can visualise this model with `mreggerplot`.\n```s\nmreggerplot beta_outcome se_outcome beta_exposure se_exposure\ngraph export ldl-chd-mreggerplot.svg, width(600) replace\n```\n\n![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)\n\nWe then fit the MR-Egger, median, and modal based estimators.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```s\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```s\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\nAnd we could continue with additional Stata code (or indeed R code) as we liked.\nNote to run this .stmd file in Stata we do so with the following command (specifying additional options as required, see help markstat for more information).\nmarkstat using markstat-call-R-example"
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+    "section": "Read in example data",
+    "text": "Read in example data\nRead in example data from Do et al. (2013).\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\nSelect observations (p-value with exposure &lt; 10-8).\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)"
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-    "text": "Hemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408."
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+    "section": "mrforest examples",
+    "text": "mrforest examples\nForest plot of genotype specific IV estimates and IVW and MR-Egger estimates, labelling the genotypes with their RSID.\n\nmrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) ///\n    xlabel(-5,-4,-3,-2,-1,0,1,2,3,4,5)\nqui gr export mrforest.svg, width(600) replace\n\n\n\n\n\n\nExample forest plot of genotype specific IV estimates."
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-    "text": "Read in the Do et al. (2013) example dataset.\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\n\n\n\nSelect observations (p-value with LDL-C &lt; 10-8)\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)\n\n\n\n\n\n\nFit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable Mendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c (Burgess, Dudbridge, and Thompson 2015).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 2\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.514\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4670719   .0581901     8.03   0.000     .3530214    .5811224\n    hdlcbeta |  -.2930048   .1211822    -2.42   0.016    -.5305175   -.0554921\n------------------------------------------------------------------------------\n\n\nAdditionally include a third phenotype – triglycerides.\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n\n\nReport the QA statistic for instrument validity and the conditional F-statistics for instrument strength for each phenotype (Sanderson et al. 2019; Sanderson, Spiller, and Bowden 2021).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse tgse)\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n Q_A statistic for instrument validity; chi2(70) = 152.88 (p =  0.0000)\n Conditional F-statistics for instrument strength:\n F_x1 = 130.31   (ldlcbeta)\n F_x2 = 36.29    (hdlcbeta)\n F_x3 = 40.44    (tgbeta)\n\n\n\n\n\nFit MVMR-Egger regression (Rees, Wood, and Burgess 2017), by default orienting the model to the first phenotype in the main varlist.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                       MVMR-Egger model oriented wrt: ldlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.469\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .5672993   .1002611     5.66   0.000      .370791    .7638075\n    hdlcbeta |  -.1364113   .1332727    -1.02   0.306    -.3976209    .1247983\n      tgbeta |   .2739803   .1246927     2.20   0.028     .0295871    .5183735\n       _cons |  -.0093655   .0054187    -1.73   0.084     -.019986     .001255\n------------------------------------------------------------------------------\n\n\nWe can also orient the model with respect to HDL-C instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)\n\n                                       MVMR-Egger model oriented wrt: hdlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.501\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4286398   .0614056     6.98   0.000      .308287    .5489926\n    hdlcbeta |  -.1989637   .1541909    -1.29   0.197    -.5011723    .1032449\n      tgbeta |   .2256794   .1243221     1.82   0.069    -.0179875    .4693463\n       _cons |   .0002155   .0036218     0.06   0.953     -.006883    .0073141\n------------------------------------------------------------------------------\n\n\nOr we can orient the model with respect to triglycerides instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)\n\n                                         MVMR-Egger model oriented wrt: tgbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.499\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4203073   .0660026     6.37   0.000     .2909447      .54967\n    hdlcbeta |  -.1903089   .1321536    -1.44   0.150    -.4493252    .0687075\n      tgbeta |   .2065651   .1365427     1.51   0.130    -.0610537     .474184\n       _cons |   .0013499    .003951     0.34   0.733    -.0063939    .0090936\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nBurgess, S, F Dudbridge, and SG Thompson. 2015. “Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.” American Journal of Epidemiology 181 (4): 251–60. https://doi.org/10.1093/aje/kwu283.\n\n\nDo, Ron, Cristen J Willer, Ellen M Schmidt, Sebanti Sengupta, Chi Gao, Gina M Peloso, Stefan Gustafsson, et al. 2013. “Common Variants Associated with Plasma Triglycerides and Risk for Coronary Artery Disease.” Nature Genetics 45 (11): 1345–52. https://doi.org/10.1038/ng.2795.\n\n\nRees, J, A Wood, and S Burgess. 2017. “Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy.” Statistics in Medicine 36 (29): 4705–18. https://doi.org/10.1002/sim.7492.\n\n\nSanderson, E, G Davey Smith, F Windmeijer, and J Bowden. 2019. “An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.” International Journal of Epidemiology 48 (3): 713–27. https://doi.org/10.1093/ije/dyy262.\n\n\nSanderson, E, W Spiller, and J Bowden. 2021. “Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization.” Statistics in Medicine 40 (25): 5434–52. https://doi.org/10.1002/sim.9133."
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+    "text": "mregger examples\nUsing the data provided by Do et al. (2013) recreate from Bowden et al. (2016) the Table 4, LDL-c “All genetic variants” estimates.\nIVW (with fixed effect standard errors, i.e. variance of residuals [residual variance] constrained to 1).\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\n\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance [multiplicative random effects]).\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger reporting I2GX statistic and heterogeneity Q-test.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse) heterogi\n\n                                           Q_GX statistic (weighted) = 3454.26\n                                         I^2_GX statistic (weighted) =  97.92%\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n                Ruecker's Q for heterogeneity; chi2(71) = 170.11 (p =  0.0000)\n                             I-squared statistic = 58.3% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using a t-distribution for inference (p-values) & CI limits.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, tdist\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      t    P&gt;|t|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4110251    .8236012\n       _cons |  -.0087706   .0054812    -1.60   0.114    -.0196998    .0021585\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation and reporting heterogeneity (Rucker’s) Q-test.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial heterogi\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n                Ruecker's Q for heterogeneity; chi2(71) = 169.98 (p =  0.0000)\n                             I-squared statistic = 58.2% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------"
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-    "text": "Fit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable Mendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c (Burgess, Dudbridge, and Thompson 2015).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 2\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.514\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4670719   .0581901     8.03   0.000     .3530214    .5811224\n    hdlcbeta |  -.2930048   .1211822    -2.42   0.016    -.5305175   -.0554921\n------------------------------------------------------------------------------\n\n\nAdditionally include a third phenotype – triglycerides.\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n\n\nReport the QA statistic for instrument validity and the conditional F-statistics for instrument strength for each phenotype (Sanderson et al. 2019; Sanderson, Spiller, and Bowden 2021).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse tgse)\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n Q_A statistic for instrument validity; chi2(70) = 152.88 (p =  0.0000)\n Conditional F-statistics for instrument strength:\n F_x1 = 130.31   (ldlcbeta)\n F_x2 = 36.29    (hdlcbeta)\n F_x3 = 40.44    (tgbeta)"
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+    "section": "mreggersimex examples",
+    "text": "mreggersimex examples\nSIMEX suppressing bootstrapped SEs (for speed - remove the noboot option to obtain SEs).\n\nmreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot\nqui gr export mreggersimex-plot.svg, width(600) replace\n\n                                                      Number of genotypes = 73\n                                                    Bootstrap replications = 0\n                                                  Simulation replications = 50\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n       slope |   .6256194          .        .       .            .           .\n       _cons |  -.0089987          .        .       .            .           .\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nSIMEX plots for the MR-Egger model."
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-    "text": "Fit MVMR-Egger regression (Rees, Wood, and Burgess 2017), by default orienting the model to the first phenotype in the main varlist.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                       MVMR-Egger model oriented wrt: ldlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.469\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .5672993   .1002611     5.66   0.000      .370791    .7638075\n    hdlcbeta |  -.1364113   .1332727    -1.02   0.306    -.3976209    .1247983\n      tgbeta |   .2739803   .1246927     2.20   0.028     .0295871    .5183735\n       _cons |  -.0093655   .0054187    -1.73   0.084     -.019986     .001255\n------------------------------------------------------------------------------\n\n\nWe can also orient the model with respect to HDL-C instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)\n\n                                       MVMR-Egger model oriented wrt: hdlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.501\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4286398   .0614056     6.98   0.000      .308287    .5489926\n    hdlcbeta |  -.1989637   .1541909    -1.29   0.197    -.5011723    .1032449\n      tgbeta |   .2256794   .1243221     1.82   0.069    -.0179875    .4693463\n       _cons |   .0002155   .0036218     0.06   0.953     -.006883    .0073141\n------------------------------------------------------------------------------\n\n\nOr we can orient the model with respect to triglycerides instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)\n\n                                         MVMR-Egger model oriented wrt: tgbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.499\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4203073   .0660026     6.37   0.000     .2909447      .54967\n    hdlcbeta |  -.1903089   .1321536    -1.44   0.150    -.4493252    .0687075\n      tgbeta |   .2065651   .1365427     1.51   0.130    -.0610537     .474184\n       _cons |   .0013499    .003951     0.34   0.733    -.0063939    .0090936\n------------------------------------------------------------------------------"
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+    "section": "mreggerplot examples",
+    "text": "mreggerplot examples\n\nmreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1\nqui gr export mreggerplot.svg, width(600) replace\n\n\n\n\n\n\nScatter plot showing the genotype summary level data points and the fitted MR-Egger model."
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-    "text": "Burgess, S, F Dudbridge, and SG Thompson. 2015. “Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.” American Journal of Epidemiology 181 (4): 251–60. https://doi.org/10.1093/aje/kwu283.\n\n\nDo, Ron, Cristen J Willer, Ellen M Schmidt, Sebanti Sengupta, Chi Gao, Gina M Peloso, Stefan Gustafsson, et al. 2013. “Common Variants Associated with Plasma Triglycerides and Risk for Coronary Artery Disease.” Nature Genetics 45 (11): 1345–52. https://doi.org/10.1038/ng.2795.\n\n\nRees, J, A Wood, and S Burgess. 2017. “Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy.” Statistics in Medicine 36 (29): 4705–18. https://doi.org/10.1002/sim.7492.\n\n\nSanderson, E, G Davey Smith, F Windmeijer, and J Bowden. 2019. “An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.” International Journal of Epidemiology 48 (3): 713–27. https://doi.org/10.1093/ije/dyy262.\n\n\nSanderson, E, W Spiller, and J Bowden. 2021. “Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization.” Statistics in Medicine 40 (25): 5434–52. https://doi.org/10.1002/sim.9133."
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+    "section": "mrmedian examples",
+    "text": "mrmedian examples\nWeighted median estimator.\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0633137     7.24   0.000     .3341648    .5823499\n------------------------------------------------------------------------------"
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-    "text": "The paper is available here (Spiller, Davies, and Palmer 2018)."
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+    "section": "mrmodal examples",
+    "text": "mrmodal examples\n\nmrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)\nqui gr export mrmodalplot.svg, width(600) replace\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4198713   .2267297     1.85   0.064    -.0245107    .8642533\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4218667   .1981169     2.13   0.033     .0335647    .8101688\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1356432     3.63   0.000     .2258796     .757591\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of the phi parameter.\n\n\n\n\nSimple mode estimator.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1276278     3.85   0.000     .2415894    .7418812\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n\n\nSimple mode estimator with NOME assumption.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, nome\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1266936     3.88   0.000     .2434204    .7400501\n------------------------------------------------------------------------------"
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-    "section": "mrrobust set-up",
-    "text": "mrrobust set-up\nInstall the mrrobust package using the user-written github package.\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\nIf you have Stata 12 or earlier you will need to install some of these manually (see here for instructions)."
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+    "section": "mrfunnel examples",
+    "text": "mrfunnel examples\n\nmrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)\nqui gr export mrfunnel.svg, width(600) replace\n\n\n\n\n\n\nExample funnel plot."
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-    "section": "Summary data description and overview",
-    "text": "Summary data description and overview\nAccompanying this paper are two sets of data BMI.csv, and Height.csv, containing the set of summary estimates required for performing the BMI-serum glucose and height-serum glucose analyses respectively. Each dataset is organised into 5 columns under the following headings:\n\nSNP: A set of identifying numbers (rsids) for each genetic variant\nbeta.exposure: a set of values representing the coefficient from regressing the exposure upon the genetic variant within a GWAS\nbeta.outcome: a set of values representing the coefficient from regressing the outcome upon the genetic variant within a GWAS\nse.exposure: a set of values representing the standard error corresponding to the coefficient in beta.exposure\nse.outcome: a set of values representing the standard error corresponding to the coefficient in beta.outcome.\n\nNote Stata removes the . in the variable names when the data is imported.\nIn BMI.csv the exposure is standardised body mass index (BMI), and is therefore interpreted on a standard deviation scale. The summary statistics are reported by Locke et al. (2015). In Height.csv the exposure is standardised height in meters and also interpreted on a standard deviation scale. The summary statistics are reported by Wood et al. (2014).\nFor both analyses log transformed serum glucose was used as an outcome, reported by Shin et al. (2014). All the data was obtained from the MRBase GWAS catalogue available at https://www.mrbase.org/ (Hemani et al. 2018). Genetic variants were pruned so as to be independent (\\(R^2\\) = 0.0001), and the effect alleles were aligned between the exposure and outcome datasets using the MRBase web application, prior to implementing mrrobust."
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+    "title": "Examples from helpfiles in the mrrobust package",
+    "section": "Leave one out analysis",
+    "text": "Leave one out analysis\nCreate a smaller subset of data.\n\ngen byte sel2 = (ldlcp2 &lt; 1e-25)\n\nPerform leave one out analysis using the IVW estimator\n\nmrleaveoneout chdbeta ldlcbeta if sel2==1, gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-01.svg, width(600) replace\n\n\n\n\n\n\nPlot showing leave one (genotype) out at a time analysis for the IVW estimate.\n\n\n\n\nPerform leave one out analysis using MVMR collecting the estimate for LDL-c.\n\nmrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta if sel2==1, ///\n    method(mvmr) gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-02.svg, width(600) replace\n\n\n\n\n\n\nPlot showing leave one (genotype) out at a time analysis for the MVMR estimate."
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-    "section": "Stata output for each estimation method using mrrobust: BMI-Serum Glucose",
-    "text": "Stata output for each estimation method using mrrobust: BMI-Serum Glucose\n\nRead in data\n\nimport delimited using BMI.csv, clear\n\n\n\nIVW\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n\n                                                      Number of genotypes = 79\n                                              Residual standard error =  1.039\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\nbetaexposure |   .0231866   .0079957     2.90   0.004     .0075154    .0388578\n------------------------------------------------------------------------------\n\n\n\n\nMR-Egger\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n\n                                                      Number of genotypes = 79\n                                              Residual standard error =  1.046\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\n       slope |   .0218507   .0221852     0.98   0.325    -.0216315    .0653329\n       _cons |    .000038   .0005877     0.06   0.948    -.0011138    .0011897\n------------------------------------------------------------------------------\n\n\n\n\nPlot of the MR-Egger model\n\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-bmi.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nPlot of the MR-Egger model for the BMI data.\n\n\n\n\n\n\nWeighted median\n\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0339256   .0120248     2.82   0.005     .0103576    .0574937\n------------------------------------------------------------------------------"
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+    "title": "Examples from helpfiles in the mrrobust package",
+    "section": "References",
+    "text": "References\n\n\nBowden, Jack, George Davey Smith, Philip C Haycock, and Stephen Burgess. 2016. “Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator.” Genetic Epidemiology 40 (4): 304–14. https://doi.org/10.1002/gepi.21965.\n\n\nDo, Ron, Cristen J Willer, Ellen M Schmidt, Sebanti Sengupta, Chi Gao, Gina M Peloso, Stefan Gustafsson, et al. 2013. “Common Variants Associated with Plasma Triglycerides and Risk for Coronary Artery Disease.” Nature Genetics 45 (11): 1345–52. https://doi.org/10.1038/ng.2795."
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-    "section": "Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose",
-    "text": "Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose\nUsing the mrmodalplot command, modal estimates are calculated using bandwidths of 0.25, 0.5, and 1 respectively. This command also produces three overlaid density plots for each value, as shown in the Figure.\n\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-bmi.svg, width(600) replace\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0374507   .0424036     0.88   0.377    -.0456588    .1205602\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0416424   .0369758     1.13   0.260    -.0308289    .1141137\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0431816   .0281684     1.53   0.125    -.0120274    .0983906\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of phi."
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+    "title": "How to collect and export results",
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+    "text": "This example shows how to conveniently save and export your estimates using the r(table) matrix that is now returned by each command.\n\n\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\nSelect observations (p-value with exposure &lt; 10-8)\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)\n\n\n\n\nIVW (with fixed effect standard errors)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\nmat ivw = r(table)\n\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\nmat mregger = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\nmat radial = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat mode = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n\n\nWeighted median estimator\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat median = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0653722     7.01   0.000     .3301302    .5863845\n------------------------------------------------------------------------------\n\n\n\n\n\nCheck our matrices\n\nmat dir\nmat list ivw\nmat list mregger\nmat list radial\nmat list mode\nmat list median\n\n       median[9,1]\n         mode[9,1]\n       radial[9,2]\n      mregger[9,2]\n          ivw[9,1]\n\n\nivw[9,1]\n          chdbeta:\n         ldlcbeta\n     b  .48150551\n    se  .03822098\n     z  12.597937\npvalue  2.167e-36\n    ll  .40659377\n    ul  .55641726\n    df          .\n  crit   1.959964\n eform          0\n\n\nmregger[9,2]\n           chdbeta:    chdbeta:\n             slope       _cons\n     b   .61731315  -.00877065\n    se   .10345735   .00548118\n     z   5.9668371    -1.60014\npvalue   2.419e-09   .10956752\n    ll   .41454047  -.01951356\n    ul   .82008582   .00197226\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nradial[9,2]\n          radialGD:   radialGD:\n          radialGP       _cons\n     b   .64258196  -.57373006\n    se   .11578709   .35456584\n     z   5.5496858    -1.61812\npvalue   2.862e-08   .10563675\n    ll   .41564344  -1.2686663\n    ul   .86952048   .12120621\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nmode[9,1]\n             beta\n     b   .4789702\n    se  .06631445\n     z   7.222712\npvalue  5.096e-13\n    ll  .34899626\n    ul  .60894415\n    df          .\n  crit   1.959964\n eform          0\n\n\nmedian[9,1]\n             beta\n     b  .45825733\n    se   .0653722\n     z  7.0099725\npvalue  2.384e-12\n    ll  .33013017\n    ul  .58638448\n    df          .\n  crit   1.959964\n eform          0\n\n\nCombined into single matrix\n\nmat output = (ivw, mregger, radial, mode, median)\nmat colnames output = ivw_beta mregger_beta mregger_cons ///\n    radial_beta radial_cons mode_beta median_beta\nmat coleq output = \"\" \"\" \"\" \"\" \"\" \"\" \"\"\nmat output = output'\nmat list output, format(%4.3f)\n\noutput[7,9]\n                   b      se       z  pvalue      ll      ul      df    crit   eform\n    ivw_beta   0.482   0.038  12.598   0.000   0.407   0.556       .   1.960   0.000\nmregger_beta   0.617   0.103   5.967   0.000   0.415   0.820       .   1.960   0.000\nmregger_cons  -0.009   0.005  -1.600   0.110  -0.020   0.002       .   1.960   0.000\n radial_beta   0.643   0.116   5.550   0.000   0.416   0.870       .   1.960   0.000\n radial_cons  -0.574   0.355  -1.618   0.106  -1.269   0.121       .   1.960   0.000\n   mode_beta   0.479   0.066   7.223   0.000   0.349   0.609       .   1.960   0.000\n median_beta   0.458   0.065   7.010   0.000   0.330   0.586       .   1.960   0.000\n\n\nExport matrix to dataset\n\ndrop _all\nsvmat output, names(col)\nlocal rownames : rownames output\ndi \"`rownames'\"\ntokenize `rownames'\ngen str15 estimate = \"\"\nforvalues i = 1/7 {\n    replace estimate = \"``i''\" in `i'\n}\n\nShow dataset\n\nlist estimate b se z pvalue ll ul, clean noobs\n\n        estimate           b         se          z     pvalue          ll         ul  \n        ivw_beta    .4815055    .038221   12.59794   2.17e-36    .4065938   .5564172  \n    mregger_beta    .6173131   .1034573   5.966837   2.42e-09    .4145405   .8200858  \n    mregger_cons   -.0087707   .0054812   -1.60014   .1095675   -.0195136   .0019723  \n     radial_beta    .6425819   .1157871   5.549686   2.86e-08    .4156434   .8695205  \n     radial_cons   -.5737301   .3545658   -1.61812   .1056367   -1.268666   .1212062  \n       mode_beta    .4789702   .0663145   7.222712   5.10e-13    .3489963   .6089441  \n     median_beta    .4582573   .0653722   7.009973   2.38e-12    .3301302   .5863845  \n\n\nSave dataset\nsave myestimates, replace\nExport as tab-delimited textfile\nexport delimited using myestimates.txt, delimiter(tab) replace"
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-    "section": "Stata output for each estimation method using mrrobust: Height-Serum Glucose",
-    "text": "Stata output for each estimation method using mrrobust: Height-Serum Glucose\n\nRead in data\n\nimport delimited using Height.csv, clear\n\n\n\nIVW\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n\n                                                     Number of genotypes = 367\n                                              Residual standard error =  1.044\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\nbetaexposure |   .0015412   .0033017     0.47   0.641      -.00493    .0080124\n------------------------------------------------------------------------------\n\n\n\n\nMR-Egger\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n\n                                                     Number of genotypes = 367\n                                              Residual standard error =  1.045\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\n       slope |  -.0025878   .0091178    -0.28   0.777    -.0204584    .0152828\n       _cons |   .0001338   .0002754     0.49   0.627     -.000406    .0006736\n------------------------------------------------------------------------------\n\n\n\n\nPlot of the MR-Egger model\n\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-height.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nPlot of the MR-Egger model for the Height data.\n\n\n\n\n\n\nWeighted median\n\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |          0   .0052323     0.00   1.000    -.0102551    .0102551\n------------------------------------------------------------------------------"
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+    "title": "How to collect and export results",
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+    "text": "use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\nSelect observations (p-value with exposure &lt; 10-8)\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)"
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-    "section": "Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose",
-    "text": "Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose\n\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-height.svg, width(600) replace\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0061368   .0245472     0.25   0.803    -.0419748    .0542484\n------------------------------------------------------------------------------\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0015595   .0212232     0.07   0.941    -.0400372    .0431561\n------------------------------------------------------------------------------\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |  -.0054772   .0149074    -0.37   0.713    -.0346952    .0237408\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of phi."
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+    "text": "IVW (with fixed effect standard errors)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\nmat ivw = r(table)\n\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\nmat mregger = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\nmat radial = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat mode = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n\n\nWeighted median estimator\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat median = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0653722     7.01   0.000     .3301302    .5863845\n------------------------------------------------------------------------------"
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-    "title": "Examples from our IJE paper",
-    "section": "References",
-    "text": "References\n\n\nHemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408.\n\n\nLocke, Adam E, Bratati Kahali, Sonja I Berndt, Anne E Justice, Tune H Pers, Felix R Day, Corey Powell, et al. 2015. “Genetic Studies of Body Mass Index Yield New Insights for Obesity Biology.” Nature 518 (7538): 197–206. https://doi.org/10.1038/nature14177.\n\n\nShin, So-Youn, Eric B Fauman, Ann-Kristin Petersen, Jan Krumsiek, Rita Santos, Jie Huang, Matthias Arnold, et al. 2014. “An Atlas of Genetic Influences on Human Blood Metabolites.” Nature Genetics 46 (6): 543–50. https://doi.org/10.1038/ng.2982.\n\n\nSpiller, Wes, Neil M Davies, and Tom M Palmer. 2018. “Software application profile: mrrobust—a tool for performing two-sample summary Mendelian randomization analyses.” International Journal of Epidemiology 48 (3): 684–90. https://doi.org/10.1093/ije/dyy195.\n\n\nWood, Andrew R, Tonu Esko, Jian Yang, Sailaja Vedantam, Tune H Pers, Stefan Gustafsson, Audrey Y Chu, et al. 2014. “Defining the Role of Common Variation in the Genomic and Biological Architecture of Adult Human Height.” Nature Genetics 46 (11): 1173–86. https://doi.org/10.1038/ng.3097."
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+    "section": "",
+    "text": "Check our matrices\n\nmat dir\nmat list ivw\nmat list mregger\nmat list radial\nmat list mode\nmat list median\n\n       median[9,1]\n         mode[9,1]\n       radial[9,2]\n      mregger[9,2]\n          ivw[9,1]\n\n\nivw[9,1]\n          chdbeta:\n         ldlcbeta\n     b  .48150551\n    se  .03822098\n     z  12.597937\npvalue  2.167e-36\n    ll  .40659377\n    ul  .55641726\n    df          .\n  crit   1.959964\n eform          0\n\n\nmregger[9,2]\n           chdbeta:    chdbeta:\n             slope       _cons\n     b   .61731315  -.00877065\n    se   .10345735   .00548118\n     z   5.9668371    -1.60014\npvalue   2.419e-09   .10956752\n    ll   .41454047  -.01951356\n    ul   .82008582   .00197226\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nradial[9,2]\n          radialGD:   radialGD:\n          radialGP       _cons\n     b   .64258196  -.57373006\n    se   .11578709   .35456584\n     z   5.5496858    -1.61812\npvalue   2.862e-08   .10563675\n    ll   .41564344  -1.2686663\n    ul   .86952048   .12120621\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nmode[9,1]\n             beta\n     b   .4789702\n    se  .06631445\n     z   7.222712\npvalue  5.096e-13\n    ll  .34899626\n    ul  .60894415\n    df          .\n  crit   1.959964\n eform          0\n\n\nmedian[9,1]\n             beta\n     b  .45825733\n    se   .0653722\n     z  7.0099725\npvalue  2.384e-12\n    ll  .33013017\n    ul  .58638448\n    df          .\n  crit   1.959964\n eform          0\n\n\nCombined into single matrix\n\nmat output = (ivw, mregger, radial, mode, median)\nmat colnames output = ivw_beta mregger_beta mregger_cons ///\n    radial_beta radial_cons mode_beta median_beta\nmat coleq output = \"\" \"\" \"\" \"\" \"\" \"\" \"\"\nmat output = output'\nmat list output, format(%4.3f)\n\noutput[7,9]\n                   b      se       z  pvalue      ll      ul      df    crit   eform\n    ivw_beta   0.482   0.038  12.598   0.000   0.407   0.556       .   1.960   0.000\nmregger_beta   0.617   0.103   5.967   0.000   0.415   0.820       .   1.960   0.000\nmregger_cons  -0.009   0.005  -1.600   0.110  -0.020   0.002       .   1.960   0.000\n radial_beta   0.643   0.116   5.550   0.000   0.416   0.870       .   1.960   0.000\n radial_cons  -0.574   0.355  -1.618   0.106  -1.269   0.121       .   1.960   0.000\n   mode_beta   0.479   0.066   7.223   0.000   0.349   0.609       .   1.960   0.000\n median_beta   0.458   0.065   7.010   0.000   0.330   0.586       .   1.960   0.000\n\n\nExport matrix to dataset\n\ndrop _all\nsvmat output, names(col)\nlocal rownames : rownames output\ndi \"`rownames'\"\ntokenize `rownames'\ngen str15 estimate = \"\"\nforvalues i = 1/7 {\n    replace estimate = \"``i''\" in `i'\n}\n\nShow dataset\n\nlist estimate b se z pvalue ll ul, clean noobs\n\n        estimate           b         se          z     pvalue          ll         ul  \n        ivw_beta    .4815055    .038221   12.59794   2.17e-36    .4065938   .5564172  \n    mregger_beta    .6173131   .1034573   5.966837   2.42e-09    .4145405   .8200858  \n    mregger_cons   -.0087707   .0054812   -1.60014   .1095675   -.0195136   .0019723  \n     radial_beta    .6425819   .1157871   5.549686   2.86e-08    .4156434   .8695205  \n     radial_cons   -.5737301   .3545658   -1.61812   .1056367   -1.268666   .1212062  \n       mode_beta    .4789702   .0663145   7.222712   5.10e-13    .3489963   .6089441  \n     median_beta    .4582573   .0653722   7.009973   2.38e-12    .3301302   .5863845  \n\n\nSave dataset\nsave myestimates, replace\nExport as tab-delimited textfile\nexport delimited using myestimates.txt, delimiter(tab) replace"
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-    "text": "Latest updates\nShort video introduction\nHelpfile examples\nOverview\nInstalling and updating mrrobust\nUnit tests\nAuthors\nHow to cite the mrrobust package\nReferences\nCollaboration\nAcknowledgements\n\n\n\nTo obtain the latest update please see the instructions below.\n\nJanuary 2024:\n\nReran certification scripts under Stata 18.0\n\nAugust 2023:\n\nAdded a record of the Stata version in the certification scripts\n\nApril 2023:\n\nImproved the alt text descriptions for the images in the README and package website, and also centred the images\nRemade the mrrobust website using Quarto\nUpdated the Sanderson et al., bioRxiv, 2020, doi reference to its published version Sanderson et al., Statistics in Medicine, 2021, doi\nReran certification scripts under latest Stata 17.0\n\nFebruary 2023:\n\nUpdated R Markdown example to use the CRAN version of the Statamarkdown package\n\nSeptember 2022:\n\nUpdated manual installation instructions\n\nFebruary 2022:\n\nRan cscripts under Stata 17.0\nUpdated website examples to run under Stata 17.0\n\nSeptember 2021:\n\nChanged relevant http: URLs to https:\nMinor edits to the helpfiles\n\nJune 2021:\n\nPublished an interactive Code Ocean capsule demonstrating the use of the mrrobust package here\nBy default mrforest now specifies a fixed effect standard error for its IVW estimate\n\nApril 2021:\n\nAdded the I-squared statistic and its 95% CI to the mregger ..., heterogi output\n\nFebruary 2021:\n\nFixes to mrforest and mrleaveoneout related to the recent update to metan. mrforest and mrleaveoneout now use metan9 instead of metan because of the changes to metan syntax. No change was necessary in the dependency scripts because metan9 is also installed with ssc install metan\nChecked cscripts pass\nChecked examples on website run. And changed the 2 examples which use the TwoSampleMR R package to use the new ID code for the exposure data\nUpdated dependency.do to make it more robust to the more frequent updates to metan\nUpdated mrdeps to make it more robust to the more frequent updates to metan\n\nOctober 2020:\n\ndependency.do and mrdeps now install the updated version of the moremata package\nChecked the cscripts run under Stata 16.1\nAdded description of Q-statistic as Cochran’s and Ruecker’s for the IVW and MR-Egger models respectively\nIn various helpfiles added clarification that genotype-disease stands for SNP-outcome (or indeed instrument-outcome) and that genotype-phenotype stands for SNP-exposure (or indeed instrument-exposure) respectively; i.e. the estimates required for the top and bottom of the IV Wald ratio estimate\n\nAugust 2020:\n\nAdded html versions of the helpfiles to the website. These are available from the Helpfiles website menu bar item\nAdded extra decimal places examples to helpfiles of mrforest and mrleaveoneout\nmrfunnel now includes a legend on its plot\n\nJuly 2020:\n\nAdded gxse() option to mrmvivw to return instrument strength QA statistic for instrument validity in e(Qa) (Sanderson et al. 2019)\nThe gxse() option additionally returns the Qx and conditional F-statistics for each phenotype for instrument strength in e(Qx) and e(Fx) (Sanderson et al. 2020)\nAdded tdist option to mrmvivw and mrmvegger\nmrmvivw and mrmvegger now ereturn the RMSE in e(phi)\nmregger, ivw now displays the square root of the residual variance (residual standard error) and ereturns this is e(phi)\nChecked that examples on website still run\nAdded mrleaveoneout command to perform leave one out analysis\n\nJune 2020:\n\nSimplified the outcome variable name in mregger b and V e-returned matrices. Turn this off with new oldnames option\nAdded basic multivariable MR-Egger command, mrmvegger\nAdded basic multivariable IVW command, mrmvivw (currently command names mvmr and mvivw also work)\n\nFebruary 2020:\n\nUpdated contact details\nMinor edits to helpfiles, to show examples setting seed() option where helpful\nFixed mregger bug where r(table) was not returned with the gxse or heterogi options. The output for these options now appears before the coefficient table.\nMinor amendments to formatting of mregger gxse output\nmregger now ereturns e(phi), the scale parameter, in some cases\n\nJanuary 2020:\n\nmregger now additionally returns r(table)\nCertification scripts: added master.do and renamed and edited a few scripts\nAdded mr command. Commands may now be run as either mr egger ... or as previously mregger ....\nBest of IJE 2019!\nmrmedian, mrmedianobs, mreggersimex, mrmodal, and mrratio now additionally return the r(table) matrix (the information from the coefficient table)\nAdded an example showing how you can save and export your estimates using r(table), see here\n\nDecember 2019:\n\nAdded Q_GX to ereturn and display output when gxse() option specified to mregger\nChanged Q_GX and I^2_GX output to use first order weights in mregger output. This matches the output from the mr_egger() function in the MendelianRandomization R package. Use the unwi2gx option to report the unweighted statistics.\n\nJuly 2019:\n\nChecked that examples on website still run\n\nDecember 2018:\n\nImproved compatibility with the github Stata package, i.e., mrrobust and its dependencies can be installed simply by issuing: gitget mrrobust, if you have the github package installed. See below for instructions.\nmrdeps command added for conveniently installing dependencies\n\nNovember 2018:\n\nExample showing the use of the TwoSampleMR R package and mrrobust in the same R Markdown script (.Rmd file) is here\nExample showing the use of the TwoSampleMR R package and mrrobust in the same Stata Markdown script (.stmd file) is here\n\nSeptember 2018:\n\nIJE paper published online here\n\nAugust 2018:\n\nClick here for the example code and output from our IJE article\n\nMay 2018:\n\nClick here for code and output from the examples in the helpfiles\nThis page is now rendered on GitHub Pages here\n\nApril 2018:\n\nmregger now has option radial which implements the radial formulation of the MR-Egger model, and of the IVW model when used with option ivw\n\n\n\n\n\nClick here for a short video demonstrating the use of the package.\n\n\n\n\n\n\nClick here for some of the code and output from the examples in the helpfiles.\nOnce the package is installed, there is a summary helpfile which can be viewed in Stata with:\nhelp mrrobust\nThis has links to the helpfile for each command, which has an example near the bottom. In these examples you can click on the code to run it.\n\n\n\nThe mrrobust package is a collection of commands for performing two-sample Mendelian randomization analyses using summary data of genotype-phenotype and genotype-outcome associations.\nSuch data can be obtained from repositories such as MR-Base https://www.mrbase.org (Hemani et al. 2016).\nThe package contains the following commands:\n\nmrdeps installs dependencies for the package\nmrratio implements the standard instrumental variable ratio (Wald) estimate with a choice of standard errors/confidence intervals\nmrivests automates calling mrratio on all the selected genotypes in your dataset\nmregger implements the IVW and MR-Egger regression approaches introduced in Bowden et al. (2015)\nmreggersimex implements the simulation extrapolation algorithm for the MR-Egger model\nmreggerplot implements a scatter plot with fitted line (either from IVW, MR-Egger, or weighted median estimators) and confidence interval\nmrmedian and mrmedianobs implement the unweighted, weighted, and penalized weighted median IV estimators robust to 50% invalid instruments in Bowden et al. (2016)\nmrmodal implements the zero modal estimator of Hartwig et al. (2017)\nmrmodalplot plot of density used in modal estimator\nmrforest implements a forest plot of genotype specific IV estimates and estimates from models (e.g. IVW and MR-Egger)\nmrfunnel funnel plot of genotype specific IV estimates\nmr acts as a primary command, e.g. so the other commands can be run as mr egger ... as well as mregger ...\nmrmvivw (mvmr, mvivw) implements the multivariable IVW model\nmrmvegger implements the multivariable MR-Egger model\nmrleaveoneout implements leave one (genotype) out analysis\n\n\n\n\nTo install mrrobust in Stata versions 13 and later you have two choices.\n\n\nnet install mrrobust, from(\"https://raw.github.com/remlapmot/mrrobust/master/\") replace\nmrdeps\nIn this code mrdeps installs the dependencies. These are addplot, kdens, and moremata packages (all by Ben Jann), the heterogi command (Orsini et al.), the metan command (Harris et al.), and the grc1leg command (Wiggins).\nIf you have previously installed the package and the net install command above fails with an error message that there are two copies of the package installed simply run adoupdate.\nTo check if there is an update available to any of your user-written Stata packages run adoupdate. To update mrrobust run:\nadoupdate mrrobust, update\nTo uninstall mrrobust, issue in Stata:\nado uninstall mrrobust\nIf this fails with an error message mentioning that you have “multiple citations/instances of the package installed” simply issue adoupdate mrrobust. This should leave you with the most recent version of the package you previously installed. You can then run ado uninstall mrrobust.\n\n\n\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\nThis automatically installs the dependencies.\nTo update the package issue:\ngithub update mrrobust\nTo uninstall mrrobust issue:\ngithub uninstall mrrobust\n\n\n\nThe net install syntax for installing mrrobust does not work under Stata version 12 and earlier because this webpage has an address starting with https rather than http. In such cases you need to do a manual installation.\n\n\n\nClick the green “Clone or download” button at the top of the GitHub repository here and download as a zip archive or click this direct link.\nIn your file explorer extract the zip archive and find its filepath, e.g. C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master\nIn Stata run\nnet install mrrobust, from(\"C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master\") replace\n\nThe installation commands for the other dependencies should work. However, if you need to install them manually their zip archives are available at the following links (extract the files from the downloaded zip archives and place them in your PERSONAL directory on your adopath):\n\nthe moremata package is available as a zip file here\nthe addplot command is available here\nthe heterogi command is available here\nthe kdens command is available here\nthe metan command is available here\nthe grc1leg command can be installed with\nnet install grc1leg, from(\"https://www.stata.com/users/vwiggins\")\n\n\n\n\n\n\nAs far as I know, and unlike R which has the testthat package (and other testing packages), there is no recognised standard for writing unit tests for Stata commands. StataCorp. refer to do-files with tests as cscripts (certification scripts). I publish my cscripts (and their log files of output) in the cscripts directory.\n\n\n\nTom Palmer tom.palmer@bristol.ac.uk, Wesley Spiller, Neil Davies\n\n\n\nSpiller W, Davies NM, Palmer TM. Software Application Profile: mrrobust — A tool for performing two-sample summary Mendelian randomization analyses. International Journal of Epidemiology, 2019, 48, 3, 684–690. https://doi.org/10.1093/ije/dyy195.\nThank you to all our users who have cited mrrobust. We made The Best of IJE 2019!\n\n\n\nIf you would like to extend the code or add new commands I am open to receiving pull requests on GitHub or send me an email to tom.palmer@bristol.ac.uk.\n\n\n\n\nBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 2015, 44, 2, 512–525. https://dx.doi.org/10.1093/ije/dyv080.\nBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 2016, 40, 4, 304–314. https://dx.doi.org/10.1002/gepi.21965.\nHartwig FP, Davey Smith G, Bowden J. Robust inference in two-sample Mendelian randomisation via the zero modal pleiotropy assumption. International Journal of Epidemiology, 2017, 46, 6, 1985–1998. https://doi.org/10.1093/ije/dyx102.\nHemani G et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife, 2018, 7:e34408. https://doi.org/10.7554/eLife.34408.001.\nSanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International Journal of Epidemiology, 2019, 48, 3, 713–727. https://doi.org/10.1093/ije/dyy262.\nSanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Statistics in Medicine, 2021, 40, 25, 5434–5452. https://doi.org/10.1002/sim.9133.\n\n\n\n\nThanks for helpful feedback and suggestions to (in no particular order): Jasmine Khouja, Michael Holmes, Caroline Dale, Amy Taylor, Rebecca Richmond, Judith Brand, Yanchun Bao, Kawthar Al-Dabhani, Michalis Katsoulis, Ghazaleh Fatemifar, Lai-Te Chen, Sean Harrison, Emma Anderson, Cassianne Robinson-Cohen, Alisa Kjaergaard, and Steve Burgess."
+    "text": "Latest updates\nShort video introduction\nHelpfile examples\nOverview\nInstalling and updating mrrobust\nUnit tests\nAuthors\nHow to cite the mrrobust package\nReferences\nCollaboration\nAcknowledgements\n\n\n\nTo obtain the latest update please see the instructions below.\n\nJuly 2024:\n\nUpdated website to mention OpenGWAS instead of MR-Base\n\nJanuary 2024:\n\nReran certification scripts under Stata 18.0\n\nAugust 2023:\n\nAdded a record of the Stata version in the certification scripts\n\nApril 2023:\n\nImproved the alt text descriptions for the images in the README and package website, and also centred the images\nRemade the mrrobust website using Quarto\nUpdated the Sanderson et al., bioRxiv, 2020, doi reference to its published version Sanderson et al., Statistics in Medicine, 2021, doi\nReran certification scripts under latest Stata 17.0\n\nFebruary 2023:\n\nUpdated R Markdown example to use the CRAN version of the Statamarkdown package\n\nSeptember 2022:\n\nUpdated manual installation instructions\n\nFebruary 2022:\n\nRan cscripts under Stata 17.0\nUpdated website examples to run under Stata 17.0\n\nSeptember 2021:\n\nChanged relevant http: URLs to https:\nMinor edits to the helpfiles\n\nJune 2021:\n\nPublished an interactive Code Ocean capsule demonstrating the use of the mrrobust package here\nBy default mrforest now specifies a fixed effect standard error for its IVW estimate\n\nApril 2021:\n\nAdded the I-squared statistic and its 95% CI to the mregger ..., heterogi output\n\nFebruary 2021:\n\nFixes to mrforest and mrleaveoneout related to the recent update to metan. mrforest and mrleaveoneout now use metan9 instead of metan because of the changes to metan syntax. No change was necessary in the dependency scripts because metan9 is also installed with ssc install metan\nChecked cscripts pass\nChecked examples on website run. And changed the 2 examples which use the TwoSampleMR R package to use the new ID code for the exposure data\nUpdated dependency.do to make it more robust to the more frequent updates to metan\nUpdated mrdeps to make it more robust to the more frequent updates to metan\n\nOctober 2020:\n\ndependency.do and mrdeps now install the updated version of the moremata package\nChecked the cscripts run under Stata 16.1\nAdded description of Q-statistic as Cochran’s and Ruecker’s for the IVW and MR-Egger models respectively\nIn various helpfiles added clarification that genotype-disease stands for SNP-outcome (or indeed instrument-outcome) and that genotype-phenotype stands for SNP-exposure (or indeed instrument-exposure) respectively; i.e. the estimates required for the top and bottom of the IV Wald ratio estimate\n\nAugust 2020:\n\nAdded html versions of the helpfiles to the website. These are available from the Helpfiles website menu bar item\nAdded extra decimal places examples to helpfiles of mrforest and mrleaveoneout\nmrfunnel now includes a legend on its plot\n\nJuly 2020:\n\nAdded gxse() option to mrmvivw to return instrument strength QA statistic for instrument validity in e(Qa) (Sanderson et al. 2019)\nThe gxse() option additionally returns the Qx and conditional F-statistics for each phenotype for instrument strength in e(Qx) and e(Fx) (Sanderson et al. 2020)\nAdded tdist option to mrmvivw and mrmvegger\nmrmvivw and mrmvegger now ereturn the RMSE in e(phi)\nmregger, ivw now displays the square root of the residual variance (residual standard error) and ereturns this is e(phi)\nChecked that examples on website still run\nAdded mrleaveoneout command to perform leave one out analysis\n\nJune 2020:\n\nSimplified the outcome variable name in mregger b and V e-returned matrices. Turn this off with new oldnames option\nAdded basic multivariable MR-Egger command, mrmvegger\nAdded basic multivariable IVW command, mrmvivw (currently command names mvmr and mvivw also work)\n\nFebruary 2020:\n\nUpdated contact details\nMinor edits to helpfiles, to show examples setting seed() option where helpful\nFixed mregger bug where r(table) was not returned with the gxse or heterogi options. The output for these options now appears before the coefficient table.\nMinor amendments to formatting of mregger gxse output\nmregger now ereturns e(phi), the scale parameter, in some cases\n\nJanuary 2020:\n\nmregger now additionally returns r(table)\nCertification scripts: added master.do and renamed and edited a few scripts\nAdded mr command. Commands may now be run as either mr egger ... or as previously mregger ....\nBest of IJE 2019!\nmrmedian, mrmedianobs, mreggersimex, mrmodal, and mrratio now additionally return the r(table) matrix (the information from the coefficient table)\nAdded an example showing how you can save and export your estimates using r(table), see here\n\nDecember 2019:\n\nAdded Q_GX to ereturn and display output when gxse() option specified to mregger\nChanged Q_GX and I^2_GX output to use first order weights in mregger output. This matches the output from the mr_egger() function in the MendelianRandomization R package. Use the unwi2gx option to report the unweighted statistics.\n\nJuly 2019:\n\nChecked that examples on website still run\n\nDecember 2018:\n\nImproved compatibility with the github Stata package, i.e., mrrobust and its dependencies can be installed simply by issuing: gitget mrrobust, if you have the github package installed. See below for instructions.\nmrdeps command added for conveniently installing dependencies\n\nNovember 2018:\n\nExample showing the use of the TwoSampleMR R package and mrrobust in the same R Markdown script (.Rmd file) is here\nExample showing the use of the TwoSampleMR R package and mrrobust in the same Stata Markdown script (.stmd file) is here\n\nSeptember 2018:\n\nIJE paper published online here\n\nAugust 2018:\n\nClick here for the example code and output from our IJE article\n\nMay 2018:\n\nClick here for code and output from the examples in the helpfiles\nThis page is now rendered on GitHub Pages here\n\nApril 2018:\n\nmregger now has option radial which implements the radial formulation of the MR-Egger model, and of the IVW model when used with option ivw\n\n\n\n\n\nClick here for a short video demonstrating the use of the package.\n\n\n\n\n\n\nClick here for some of the code and output from the examples in the helpfiles.\nOnce the package is installed, there is a summary helpfile which can be viewed in Stata with:\nhelp mrrobust\nThis has links to the helpfile for each command, which has an example near the bottom. In these examples you can click on the code to run it.\n\n\n\nThe mrrobust package is a collection of commands for performing two-sample Mendelian randomization analyses using summary data of genotype-phenotype and genotype-outcome associations.\nSuch data can be obtained from repositories such as MR-Base https://www.mrbase.org (Hemani et al. 2016).\nThe package contains the following commands:\n\nmrdeps installs dependencies for the package\nmrratio implements the standard instrumental variable ratio (Wald) estimate with a choice of standard errors/confidence intervals\nmrivests automates calling mrratio on all the selected genotypes in your dataset\nmregger implements the IVW and MR-Egger regression approaches introduced in Bowden et al. (2015)\nmreggersimex implements the simulation extrapolation algorithm for the MR-Egger model\nmreggerplot implements a scatter plot with fitted line (either from IVW, MR-Egger, or weighted median estimators) and confidence interval\nmrmedian and mrmedianobs implement the unweighted, weighted, and penalized weighted median IV estimators robust to 50% invalid instruments in Bowden et al. (2016)\nmrmodal implements the zero modal estimator of Hartwig et al. (2017)\nmrmodalplot plot of density used in modal estimator\nmrforest implements a forest plot of genotype specific IV estimates and estimates from models (e.g. IVW and MR-Egger)\nmrfunnel funnel plot of genotype specific IV estimates\nmr acts as a primary command, e.g. so the other commands can be run as mr egger ... as well as mregger ...\nmrmvivw (mvmr, mvivw) implements the multivariable IVW model\nmrmvegger implements the multivariable MR-Egger model\nmrleaveoneout implements leave one (genotype) out analysis\n\n\n\n\nTo install mrrobust in Stata versions 13 and later you have two choices.\n\n\nnet install mrrobust, from(\"https://raw.github.com/remlapmot/mrrobust/master/\") replace\nmrdeps\nIn this code mrdeps installs the dependencies. These are addplot, kdens, and moremata packages (all by Ben Jann), the heterogi command (Orsini et al.), the metan command (Harris et al.), and the grc1leg command (Wiggins).\nIf you have previously installed the package and the net install command above fails with an error message that there are two copies of the package installed simply run adoupdate.\nTo check if there is an update available to any of your user-written Stata packages run adoupdate. To update mrrobust run:\nadoupdate mrrobust, update\nTo uninstall mrrobust, issue in Stata:\nado uninstall mrrobust\nIf this fails with an error message mentioning that you have “multiple citations/instances of the package installed” simply issue adoupdate mrrobust. This should leave you with the most recent version of the package you previously installed. You can then run ado uninstall mrrobust.\n\n\n\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\nThis automatically installs the dependencies.\nTo update the package issue:\ngithub update mrrobust\nTo uninstall mrrobust issue:\ngithub uninstall mrrobust\n\n\n\nThe net install syntax for installing mrrobust does not work under Stata version 12 and earlier because this webpage has an address starting with https rather than http. In such cases you need to do a manual installation.\n\n\n\nClick the green “Clone or download” button at the top of the GitHub repository here and download as a zip archive or click this direct link.\nIn your file explorer extract the zip archive and find its filepath, e.g. C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master\nIn Stata run\nnet install mrrobust, from(\"C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master\") replace\n\nThe installation commands for the other dependencies should work. However, if you need to install them manually their zip archives are available at the following links (extract the files from the downloaded zip archives and place them in your PERSONAL directory on your adopath):\n\nthe moremata package is available as a zip file here\nthe addplot command is available here\nthe heterogi command is available here\nthe kdens command is available here\nthe metan command is available here\nthe grc1leg command can be installed with\nnet install grc1leg, from(\"https://www.stata.com/users/vwiggins\")\n\n\n\n\n\n\nAs far as I know, and unlike R which has the testthat package (and other testing packages), there is no recognised standard for writing unit tests for Stata commands. StataCorp. refer to do-files with tests as cscripts (certification scripts). I publish my cscripts (and their log files of output) in the cscripts directory.\n\n\n\nTom Palmer tom.palmer@bristol.ac.uk, Wesley Spiller, Neil Davies\n\n\n\nSpiller W, Davies NM, Palmer TM. Software Application Profile: mrrobust — A tool for performing two-sample summary Mendelian randomization analyses. International Journal of Epidemiology, 2019, 48, 3, 684–690. https://doi.org/10.1093/ije/dyy195.\nThank you to all our users who have cited mrrobust. We made The Best of IJE 2019!\n\n\n\nIf you would like to extend the code or add new commands I am open to receiving pull requests on GitHub or send me an email to tom.palmer@bristol.ac.uk.\n\n\n\n\nBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 2015, 44, 2, 512–525. https://dx.doi.org/10.1093/ije/dyv080.\nBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 2016, 40, 4, 304–314. https://dx.doi.org/10.1002/gepi.21965.\nHartwig FP, Davey Smith G, Bowden J. Robust inference in two-sample Mendelian randomisation via the zero modal pleiotropy assumption. International Journal of Epidemiology, 2017, 46, 6, 1985–1998. https://doi.org/10.1093/ije/dyx102.\nHemani G et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife, 2018, 7:e34408. https://doi.org/10.7554/eLife.34408.001.\nSanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International Journal of Epidemiology, 2019, 48, 3, 713–727. https://doi.org/10.1093/ije/dyy262.\nSanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Statistics in Medicine, 2021, 40, 25, 5434–5452. https://doi.org/10.1002/sim.9133.\n\n\n\n\nThanks for helpful feedback and suggestions to (in no particular order): Jasmine Khouja, Michael Holmes, Caroline Dale, Amy Taylor, Rebecca Richmond, Judith Brand, Yanchun Bao, Kawthar Al-Dabhani, Michalis Katsoulis, Ghazaleh Fatemifar, Lai-Te Chen, Sean Harrison, Emma Anderson, Cassianne Robinson-Cohen, Alisa Kjaergaard, and Steve Burgess."
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-    "text": "To obtain the latest update please see the instructions below.\n\nJanuary 2024:\n\nReran certification scripts under Stata 18.0\n\nAugust 2023:\n\nAdded a record of the Stata version in the certification scripts\n\nApril 2023:\n\nImproved the alt text descriptions for the images in the README and package website, and also centred the images\nRemade the mrrobust website using Quarto\nUpdated the Sanderson et al., bioRxiv, 2020, doi reference to its published version Sanderson et al., Statistics in Medicine, 2021, doi\nReran certification scripts under latest Stata 17.0\n\nFebruary 2023:\n\nUpdated R Markdown example to use the CRAN version of the Statamarkdown package\n\nSeptember 2022:\n\nUpdated manual installation instructions\n\nFebruary 2022:\n\nRan cscripts under Stata 17.0\nUpdated website examples to run under Stata 17.0\n\nSeptember 2021:\n\nChanged relevant http: URLs to https:\nMinor edits to the helpfiles\n\nJune 2021:\n\nPublished an interactive Code Ocean capsule demonstrating the use of the mrrobust package here\nBy default mrforest now specifies a fixed effect standard error for its IVW estimate\n\nApril 2021:\n\nAdded the I-squared statistic and its 95% CI to the mregger ..., heterogi output\n\nFebruary 2021:\n\nFixes to mrforest and mrleaveoneout related to the recent update to metan. mrforest and mrleaveoneout now use metan9 instead of metan because of the changes to metan syntax. No change was necessary in the dependency scripts because metan9 is also installed with ssc install metan\nChecked cscripts pass\nChecked examples on website run. And changed the 2 examples which use the TwoSampleMR R package to use the new ID code for the exposure data\nUpdated dependency.do to make it more robust to the more frequent updates to metan\nUpdated mrdeps to make it more robust to the more frequent updates to metan\n\nOctober 2020:\n\ndependency.do and mrdeps now install the updated version of the moremata package\nChecked the cscripts run under Stata 16.1\nAdded description of Q-statistic as Cochran’s and Ruecker’s for the IVW and MR-Egger models respectively\nIn various helpfiles added clarification that genotype-disease stands for SNP-outcome (or indeed instrument-outcome) and that genotype-phenotype stands for SNP-exposure (or indeed instrument-exposure) respectively; i.e. the estimates required for the top and bottom of the IV Wald ratio estimate\n\nAugust 2020:\n\nAdded html versions of the helpfiles to the website. These are available from the Helpfiles website menu bar item\nAdded extra decimal places examples to helpfiles of mrforest and mrleaveoneout\nmrfunnel now includes a legend on its plot\n\nJuly 2020:\n\nAdded gxse() option to mrmvivw to return instrument strength QA statistic for instrument validity in e(Qa) (Sanderson et al. 2019)\nThe gxse() option additionally returns the Qx and conditional F-statistics for each phenotype for instrument strength in e(Qx) and e(Fx) (Sanderson et al. 2020)\nAdded tdist option to mrmvivw and mrmvegger\nmrmvivw and mrmvegger now ereturn the RMSE in e(phi)\nmregger, ivw now displays the square root of the residual variance (residual standard error) and ereturns this is e(phi)\nChecked that examples on website still run\nAdded mrleaveoneout command to perform leave one out analysis\n\nJune 2020:\n\nSimplified the outcome variable name in mregger b and V e-returned matrices. Turn this off with new oldnames option\nAdded basic multivariable MR-Egger command, mrmvegger\nAdded basic multivariable IVW command, mrmvivw (currently command names mvmr and mvivw also work)\n\nFebruary 2020:\n\nUpdated contact details\nMinor edits to helpfiles, to show examples setting seed() option where helpful\nFixed mregger bug where r(table) was not returned with the gxse or heterogi options. The output for these options now appears before the coefficient table.\nMinor amendments to formatting of mregger gxse output\nmregger now ereturns e(phi), the scale parameter, in some cases\n\nJanuary 2020:\n\nmregger now additionally returns r(table)\nCertification scripts: added master.do and renamed and edited a few scripts\nAdded mr command. Commands may now be run as either mr egger ... or as previously mregger ....\nBest of IJE 2019!\nmrmedian, mrmedianobs, mreggersimex, mrmodal, and mrratio now additionally return the r(table) matrix (the information from the coefficient table)\nAdded an example showing how you can save and export your estimates using r(table), see here\n\nDecember 2019:\n\nAdded Q_GX to ereturn and display output when gxse() option specified to mregger\nChanged Q_GX and I^2_GX output to use first order weights in mregger output. This matches the output from the mr_egger() function in the MendelianRandomization R package. Use the unwi2gx option to report the unweighted statistics.\n\nJuly 2019:\n\nChecked that examples on website still run\n\nDecember 2018:\n\nImproved compatibility with the github Stata package, i.e., mrrobust and its dependencies can be installed simply by issuing: gitget mrrobust, if you have the github package installed. See below for instructions.\nmrdeps command added for conveniently installing dependencies\n\nNovember 2018:\n\nExample showing the use of the TwoSampleMR R package and mrrobust in the same R Markdown script (.Rmd file) is here\nExample showing the use of the TwoSampleMR R package and mrrobust in the same Stata Markdown script (.stmd file) is here\n\nSeptember 2018:\n\nIJE paper published online here\n\nAugust 2018:\n\nClick here for the example code and output from our IJE article\n\nMay 2018:\n\nClick here for code and output from the examples in the helpfiles\nThis page is now rendered on GitHub Pages here\n\nApril 2018:\n\nmregger now has option radial which implements the radial formulation of the MR-Egger model, and of the IVW model when used with option ivw"
+    "text": "To obtain the latest update please see the instructions below.\n\nJuly 2024:\n\nUpdated website to mention OpenGWAS instead of MR-Base\n\nJanuary 2024:\n\nReran certification scripts under Stata 18.0\n\nAugust 2023:\n\nAdded a record of the Stata version in the certification scripts\n\nApril 2023:\n\nImproved the alt text descriptions for the images in the README and package website, and also centred the images\nRemade the mrrobust website using Quarto\nUpdated the Sanderson et al., bioRxiv, 2020, doi reference to its published version Sanderson et al., Statistics in Medicine, 2021, doi\nReran certification scripts under latest Stata 17.0\n\nFebruary 2023:\n\nUpdated R Markdown example to use the CRAN version of the Statamarkdown package\n\nSeptember 2022:\n\nUpdated manual installation instructions\n\nFebruary 2022:\n\nRan cscripts under Stata 17.0\nUpdated website examples to run under Stata 17.0\n\nSeptember 2021:\n\nChanged relevant http: URLs to https:\nMinor edits to the helpfiles\n\nJune 2021:\n\nPublished an interactive Code Ocean capsule demonstrating the use of the mrrobust package here\nBy default mrforest now specifies a fixed effect standard error for its IVW estimate\n\nApril 2021:\n\nAdded the I-squared statistic and its 95% CI to the mregger ..., heterogi output\n\nFebruary 2021:\n\nFixes to mrforest and mrleaveoneout related to the recent update to metan. mrforest and mrleaveoneout now use metan9 instead of metan because of the changes to metan syntax. No change was necessary in the dependency scripts because metan9 is also installed with ssc install metan\nChecked cscripts pass\nChecked examples on website run. And changed the 2 examples which use the TwoSampleMR R package to use the new ID code for the exposure data\nUpdated dependency.do to make it more robust to the more frequent updates to metan\nUpdated mrdeps to make it more robust to the more frequent updates to metan\n\nOctober 2020:\n\ndependency.do and mrdeps now install the updated version of the moremata package\nChecked the cscripts run under Stata 16.1\nAdded description of Q-statistic as Cochran’s and Ruecker’s for the IVW and MR-Egger models respectively\nIn various helpfiles added clarification that genotype-disease stands for SNP-outcome (or indeed instrument-outcome) and that genotype-phenotype stands for SNP-exposure (or indeed instrument-exposure) respectively; i.e. the estimates required for the top and bottom of the IV Wald ratio estimate\n\nAugust 2020:\n\nAdded html versions of the helpfiles to the website. These are available from the Helpfiles website menu bar item\nAdded extra decimal places examples to helpfiles of mrforest and mrleaveoneout\nmrfunnel now includes a legend on its plot\n\nJuly 2020:\n\nAdded gxse() option to mrmvivw to return instrument strength QA statistic for instrument validity in e(Qa) (Sanderson et al. 2019)\nThe gxse() option additionally returns the Qx and conditional F-statistics for each phenotype for instrument strength in e(Qx) and e(Fx) (Sanderson et al. 2020)\nAdded tdist option to mrmvivw and mrmvegger\nmrmvivw and mrmvegger now ereturn the RMSE in e(phi)\nmregger, ivw now displays the square root of the residual variance (residual standard error) and ereturns this is e(phi)\nChecked that examples on website still run\nAdded mrleaveoneout command to perform leave one out analysis\n\nJune 2020:\n\nSimplified the outcome variable name in mregger b and V e-returned matrices. Turn this off with new oldnames option\nAdded basic multivariable MR-Egger command, mrmvegger\nAdded basic multivariable IVW command, mrmvivw (currently command names mvmr and mvivw also work)\n\nFebruary 2020:\n\nUpdated contact details\nMinor edits to helpfiles, to show examples setting seed() option where helpful\nFixed mregger bug where r(table) was not returned with the gxse or heterogi options. The output for these options now appears before the coefficient table.\nMinor amendments to formatting of mregger gxse output\nmregger now ereturns e(phi), the scale parameter, in some cases\n\nJanuary 2020:\n\nmregger now additionally returns r(table)\nCertification scripts: added master.do and renamed and edited a few scripts\nAdded mr command. Commands may now be run as either mr egger ... or as previously mregger ....\nBest of IJE 2019!\nmrmedian, mrmedianobs, mreggersimex, mrmodal, and mrratio now additionally return the r(table) matrix (the information from the coefficient table)\nAdded an example showing how you can save and export your estimates using r(table), see here\n\nDecember 2019:\n\nAdded Q_GX to ereturn and display output when gxse() option specified to mregger\nChanged Q_GX and I^2_GX output to use first order weights in mregger output. This matches the output from the mr_egger() function in the MendelianRandomization R package. Use the unwi2gx option to report the unweighted statistics.\n\nJuly 2019:\n\nChecked that examples on website still run\n\nDecember 2018:\n\nImproved compatibility with the github Stata package, i.e., mrrobust and its dependencies can be installed simply by issuing: gitget mrrobust, if you have the github package installed. See below for instructions.\nmrdeps command added for conveniently installing dependencies\n\nNovember 2018:\n\nExample showing the use of the TwoSampleMR R package and mrrobust in the same R Markdown script (.Rmd file) is here\nExample showing the use of the TwoSampleMR R package and mrrobust in the same Stata Markdown script (.stmd file) is here\n\nSeptember 2018:\n\nIJE paper published online here\n\nAugust 2018:\n\nClick here for the example code and output from our IJE article\n\nMay 2018:\n\nClick here for code and output from the examples in the helpfiles\nThis page is now rendered on GitHub Pages here\n\nApril 2018:\n\nmregger now has option radial which implements the radial formulation of the MR-Egger model, and of the IVW model when used with option ivw"
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     "text": "Thanks for helpful feedback and suggestions to (in no particular order): Jasmine Khouja, Michael Holmes, Caroline Dale, Amy Taylor, Rebecca Richmond, Judith Brand, Yanchun Bao, Kawthar Al-Dabhani, Michalis Katsoulis, Ghazaleh Fatemifar, Lai-Te Chen, Sean Harrison, Emma Anderson, Cassianne Robinson-Cohen, Alisa Kjaergaard, and Steve Burgess."
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+    "href": "examples/markstat-call-R-example/index.html",
+    "title": "Access the OpenGWAS API from a Stata do-file or Stata Markdown script",
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+    "text": "This example shows how to run R and Stata code within the same Stata Markdown (.stmd) script. The general approach is detailed on the Stata Markdown website here and here.\nThis means that we can use the functions provided by the TwoSampleMR package to obtain data from the [OpenGWAS API}(https://api.opengwas.io/) which is the successor to MR-Base (Hemani et al. 2018).\nThe OpenGWAS API requires authentication. You must place an OPENGWAS_JWT token (by creating a free account on https://api.opengwas.io/profile/) in your .Renviron file before running the code below. See the ieugwasr website for more information.\nBefore you start please install the following two Stata packages from the SSC archive, so in Stata issue the following commands (I have commented them out because I have already installed them).\nssc install whereis\nssc install markstat\n\n\n\nThe following is the example Stata Markdown document.\n---\ntitle: Using mrrobust in a Markstat document example\n---\n\nWe first need to register the R executable with Stata.\n\n```s\nif c(os) == \"Windows\" local rpath \"C:\\\\Program Files\\\\R\\\\R-4.4.1\\\\bin\\\\x64\\\\R.exe\"\nelse if c(os) == \"Unix\" local rpath \"/usr/bin/R\"\nelse local rpath \"/usr/local/bin/R\"\nwhereis R \"`rpath'\"\n```\n\nNext we have an R code chunk in which we install the required packages in R.\n\n```r\ninstall.packages(\"remotes\")\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\n```\n\n## Extracting data from the OpenGWAS API\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). \nThe R code we will use is from \n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nFirst, we load the packages into our R session.\nNote that the **foreign** package provides the \n`write.dta()` function which we will use to save the data in Stata format.\n\n```r\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\nOur edited version of the code starts by reading in some code\nto generate a set of plots in R.\n\n```r\nsource(\"mrplots.R\")\n```\n\nWe can access the data using the **MRInstruments** package.\n\n```r\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;- \n  subset(gwas_catalog, \n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;- \n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and \ngenotype-outcome association data saved in an object in our \nR session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```r\n# Perform MR\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\n```r\npng(\"ldl-chd.png\", width=1000, height=1000)\nmr_plots(dat)\ndev.off()\n```\n\n![Plots generated by the TwoSampleMR R package.](ldl-chd.png)\n\nWe now save our `dat` object as a Stata dataset.\n\n```r\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Performing the analysis using mrrobust in Stata\n\nWe now switch from using R code chunks to Stata code chunks.\nWe read the data into Stata and list the variable names\n(note any `.` in the colnames of `dat` have been replaced with `_`).\n\n```s\nuse dat, clear\nds, v(28)\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with multiplicative\nstandard errors.\n\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw\n```\n\nIt is helpful to view the forest plot of genotype specific IV estimates.\n\n```s\nmrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///\n    xlabel(-3,-2,-1,0,1,2,3)\ngraph export ldl-chd-mrforest.svg, width(600) replace\n```\n\n![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)\n\nWe can visualise this model with `mreggerplot`.\n\n```s\nmreggerplot beta_outcome se_outcome beta_exposure se_exposure\ngraph export ldl-chd-mreggerplot.svg, width(600) replace\n```\n\n![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```s\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```s\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\nAnd we could continue with additional Stata code (or indeed R code) as we liked.\nNote to run this .stmd file in Stata we do so with the following command (specifying additional options as required, see help markstat for more information).\nmarkstat using markstat-call-R-example\n\n\n\n\n\nHemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408."
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+    "text": "This example shows how to run R and Stata code within the same Stata Markdown (.stmd) script. The general approach is detailed on the Stata Markdown website here and here.\nThis means that we can use the functions provided by the TwoSampleMR package to obtain data from the [OpenGWAS API}(https://api.opengwas.io/) which is the successor to MR-Base (Hemani et al. 2018).\nThe OpenGWAS API requires authentication. You must place an OPENGWAS_JWT token (by creating a free account on https://api.opengwas.io/profile/) in your .Renviron file before running the code below. See the ieugwasr website for more information.\nBefore you start please install the following two Stata packages from the SSC archive, so in Stata issue the following commands (I have commented them out because I have already installed them).\nssc install whereis\nssc install markstat"
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+    "text": "The following is the example Stata Markdown document.\n---\ntitle: Using mrrobust in a Markstat document example\n---\n\nWe first need to register the R executable with Stata.\n\n```s\nif c(os) == \"Windows\" local rpath \"C:\\\\Program Files\\\\R\\\\R-4.4.1\\\\bin\\\\x64\\\\R.exe\"\nelse if c(os) == \"Unix\" local rpath \"/usr/bin/R\"\nelse local rpath \"/usr/local/bin/R\"\nwhereis R \"`rpath'\"\n```\n\nNext we have an R code chunk in which we install the required packages in R.\n\n```r\ninstall.packages(\"remotes\")\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\n```\n\n## Extracting data from the OpenGWAS API\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). \nThe R code we will use is from \n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nFirst, we load the packages into our R session.\nNote that the **foreign** package provides the \n`write.dta()` function which we will use to save the data in Stata format.\n\n```r\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\nOur edited version of the code starts by reading in some code\nto generate a set of plots in R.\n\n```r\nsource(\"mrplots.R\")\n```\n\nWe can access the data using the **MRInstruments** package.\n\n```r\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;- \n  subset(gwas_catalog, \n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;- \n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and \ngenotype-outcome association data saved in an object in our \nR session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```r\n# Perform MR\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\n```r\npng(\"ldl-chd.png\", width=1000, height=1000)\nmr_plots(dat)\ndev.off()\n```\n\n![Plots generated by the TwoSampleMR R package.](ldl-chd.png)\n\nWe now save our `dat` object as a Stata dataset.\n\n```r\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Performing the analysis using mrrobust in Stata\n\nWe now switch from using R code chunks to Stata code chunks.\nWe read the data into Stata and list the variable names\n(note any `.` in the colnames of `dat` have been replaced with `_`).\n\n```s\nuse dat, clear\nds, v(28)\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with multiplicative\nstandard errors.\n\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw\n```\n\nIt is helpful to view the forest plot of genotype specific IV estimates.\n\n```s\nmrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///\n    xlabel(-3,-2,-1,0,1,2,3)\ngraph export ldl-chd-mrforest.svg, width(600) replace\n```\n\n![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)\n\nWe can visualise this model with `mreggerplot`.\n\n```s\nmreggerplot beta_outcome se_outcome beta_exposure se_exposure\ngraph export ldl-chd-mreggerplot.svg, width(600) replace\n```\n\n![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```s\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```s\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\nAnd we could continue with additional Stata code (or indeed R code) as we liked.\nNote to run this .stmd file in Stata we do so with the following command (specifying additional options as required, see help markstat for more information).\nmarkstat using markstat-call-R-example"
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+    "text": "Hemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408."
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-    "text": "This example shows how to run R and Stata code within the same R Markdown (.Rmd) file using the Statamarkdown R package. More information about this package is available here and here. To install this package and load it into your current R session run the following in R.\n# install.packages(\"Statamarkdown\") # uncomment on first run\nlibrary(Statamarkdown)\nNote when writing our Stata code chunks we need to be careful when we specify the collectcode=TRUE code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.\nUsing R and Stata code in the same script means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base (Hemani et al. 2018).\nNext we install the other required packages in R.\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\ninstall.packages(\"foreign\")\n\n\n\n---\ntitle: Using mrrobust in an R Markdown or Quarto document\n---\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)).\nThe R code we will use is from\n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nWe load the packages into our R session.\nNote that the **foreign** package provides the `write.dta()` function\nwhich we will use to save the data in Stata format.\n```{r}\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\n\nWe can access the data using the **MRInstruments** package.\n\n```{r}\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;-\n  subset(gwas_catalog,\n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;-\n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and genotype-outcome\nassociation data saved in an object in our R session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```{r}\n# Perform MR analysis\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\n\nTo proceed in Stata we can save our `dat` object as a Stata dataset\n\n```{r}\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n\n## Analysis in Stata using the mrrobust package\n\nAt this point in Stata install the **mrrobust** package and its\ndependencies if you have not done so previously.\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\n\nWe now read the dataset into Stata and look at the variable names\nand the number of observations.\n\n```{stata, collectcode=TRUE}\nqui use dat, clear\n```\n\n```{stata}\nds, v(28)\n```\n\n```{stata}\ndi _N\n```\n\n\nWe can then run the IVW model using `mregger` with fixed effect standard errors.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe\n```\n\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```{stata}\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```{stata}\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n\nAnd we could continue with additional Stata code (or indeed R code) as we like.\nTo run this R Markdown file, either; open it in RStudio and click the Knit button in the toolbar of the Source code window, or run\nrmarkdown::render('rmarkdown-call-stata-example.Rmd')\n\n\n\n\n\nHemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408."
+    "text": "This example shows how to run R and Stata code within the same R Markdown (.Rmd) or Quarto (.qmd) file using the Statamarkdown R package. More information about this package is available here and here. To install this package run the following in R.\ninstall.packages(\"Statamarkdown\")\nNote when writing our Stata code chunks we need to be careful when we specify the collectcode=TRUE code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.\nUsing R and Stata code in the same script means that we can use the functions provided by the TwoSampleMR package to obtain data from the OpenGWAS API, which is the successor to MR-Base (Hemani et al. 2018).\nThe OpenGWAS API requires authentication. You must place an OPENGWAS_JWT token (by creating a free account on https://api.opengwas.io/profile/) in your .Renviron file before running the code below. See the ieugwasr website for more information.\nNext we install the other required packages in R.\ninstall.packages(\n  \"TwoSampleMR\",\n  repos = c(\n    \"https://mrcieu.r-universe.dev\",\n    \"https://cloud.r-project.org\"\n  )\n)\ninstall.packages(\"foreign\")\n\n\n\nSave the following code chunk as an .Rmd or .qmd file, e.g. myanalysis.Rmd.\n---\ntitle: Using mrrobust in an R Markdown or Quarto document\n---\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)).\nThe R code we will use is from\n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nWe load the packages into our R session.\nNote that the **foreign** package provides the `write.dta()` function\nwhich we will use to save the data in Stata format.\n\n```{r}\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\nlibrary(Statamarkdown)\n```\n\nWe can access the data using the **MRInstruments** package.\n\n```{r}\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;-\n  subset(gwas_catalog,\n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;-\n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and genotype-outcome\nassociation data saved in an object in our R session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```{r}\n# Perform MR analysis\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\nTo proceed in Stata we can save our `dat` object as a Stata dataset\n\n```{r}\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Analysis in Stata using the mrrobust package\n\nAt this point in Stata install the **mrrobust** package and its\ndependencies if you have not done so previously.\n\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\n\nWe now read the dataset into Stata and look at the variable names\nand the number of observations.\n\n```{stata, collectcode=TRUE}\nqui use dat, clear\n```\n\n```{stata}\nds, v(28)\n```\n\n```{stata}\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with fixed effect standard errors.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe\n```\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```{stata}\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```{stata}\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\nAnd we could continue with additional Stata code (or indeed R code) as we like.\nTo render your R Markdown file, either; open it in RStudio and click the Knit button in the toolbar of the Source code window, or run\nrmarkdown::render('myanalysis.Rmd')\nor to render a Quarto file run the following in your Terminal\nquarto render myanalysis.qmd\n\n\n\n\n\nHemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408."
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-    "text": "This example shows how to run R and Stata code within the same R Markdown (.Rmd) file using the Statamarkdown R package. More information about this package is available here and here. To install this package and load it into your current R session run the following in R.\n# install.packages(\"Statamarkdown\") # uncomment on first run\nlibrary(Statamarkdown)\nNote when writing our Stata code chunks we need to be careful when we specify the collectcode=TRUE code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.\nUsing R and Stata code in the same script means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base (Hemani et al. 2018).\nNext we install the other required packages in R.\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\ninstall.packages(\"foreign\")"
+    "text": "This example shows how to run R and Stata code within the same R Markdown (.Rmd) or Quarto (.qmd) file using the Statamarkdown R package. More information about this package is available here and here. To install this package run the following in R.\ninstall.packages(\"Statamarkdown\")\nNote when writing our Stata code chunks we need to be careful when we specify the collectcode=TRUE code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.\nUsing R and Stata code in the same script means that we can use the functions provided by the TwoSampleMR package to obtain data from the OpenGWAS API, which is the successor to MR-Base (Hemani et al. 2018).\nThe OpenGWAS API requires authentication. You must place an OPENGWAS_JWT token (by creating a free account on https://api.opengwas.io/profile/) in your .Renviron file before running the code below. See the ieugwasr website for more information.\nNext we install the other required packages in R.\ninstall.packages(\n  \"TwoSampleMR\",\n  repos = c(\n    \"https://mrcieu.r-universe.dev\",\n    \"https://cloud.r-project.org\"\n  )\n)\ninstall.packages(\"foreign\")"
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-    "text": "---\ntitle: Using mrrobust in an R Markdown or Quarto document\n---\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)).\nThe R code we will use is from\n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nWe load the packages into our R session.\nNote that the **foreign** package provides the `write.dta()` function\nwhich we will use to save the data in Stata format.\n```{r}\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\n\nWe can access the data using the **MRInstruments** package.\n\n```{r}\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;-\n  subset(gwas_catalog,\n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;-\n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and genotype-outcome\nassociation data saved in an object in our R session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```{r}\n# Perform MR analysis\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\n\nTo proceed in Stata we can save our `dat` object as a Stata dataset\n\n```{r}\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n\n## Analysis in Stata using the mrrobust package\n\nAt this point in Stata install the **mrrobust** package and its\ndependencies if you have not done so previously.\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\n\nWe now read the dataset into Stata and look at the variable names\nand the number of observations.\n\n```{stata, collectcode=TRUE}\nqui use dat, clear\n```\n\n```{stata}\nds, v(28)\n```\n\n```{stata}\ndi _N\n```\n\n\nWe can then run the IVW model using `mregger` with fixed effect standard errors.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe\n```\n\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```{stata}\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```{stata}\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n\nAnd we could continue with additional Stata code (or indeed R code) as we like.\nTo run this R Markdown file, either; open it in RStudio and click the Knit button in the toolbar of the Source code window, or run\nrmarkdown::render('rmarkdown-call-stata-example.Rmd')"
+    "text": "Save the following code chunk as an .Rmd or .qmd file, e.g. myanalysis.Rmd.\n---\ntitle: Using mrrobust in an R Markdown or Quarto document\n---\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)).\nThe R code we will use is from\n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nWe load the packages into our R session.\nNote that the **foreign** package provides the `write.dta()` function\nwhich we will use to save the data in Stata format.\n\n```{r}\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\nlibrary(Statamarkdown)\n```\n\nWe can access the data using the **MRInstruments** package.\n\n```{r}\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps &lt;-\n  subset(gwas_catalog,\n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure &lt;-\n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome &lt;- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat &lt;- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and genotype-outcome\nassociation data saved in an object in our R session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```{r}\n# Perform MR analysis\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure &lt;- \"LDL cholesterol\"\ndat$outcome &lt;- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels &lt;- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] &lt;- NA\n```\n\nTo proceed in Stata we can save our `dat` object as a Stata dataset\n\n```{r}\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Analysis in Stata using the mrrobust package\n\nAt this point in Stata install the **mrrobust** package and its\ndependencies if you have not done so previously.\n\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\n\nWe now read the dataset into Stata and look at the variable names\nand the number of observations.\n\n```{stata, collectcode=TRUE}\nqui use dat, clear\n```\n\n```{stata}\nds, v(28)\n```\n\n```{stata}\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with fixed effect standard errors.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe\n```\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```{stata}\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```{stata}\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\nAnd we could continue with additional Stata code (or indeed R code) as we like.\nTo render your R Markdown file, either; open it in RStudio and click the Knit button in the toolbar of the Source code window, or run\nrmarkdown::render('myanalysis.Rmd')\nor to render a Quarto file run the following in your Terminal\nquarto render myanalysis.qmd"
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     "text": "Hemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408."
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-    "text": "net install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust"
+    "text": "Read in the Do et al. (2013) example dataset.\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\nSelect observations (p-value with LDL-C &lt; 10-8)\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)\n\n\n\nFit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable Mendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c (Burgess, Dudbridge, and Thompson 2015).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 2\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.514\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4670719   .0581901     8.03   0.000     .3530214    .5811224\n    hdlcbeta |  -.2930048   .1211822    -2.42   0.016    -.5305175   -.0554921\n------------------------------------------------------------------------------\n\n\nAdditionally include a third phenotype – triglycerides.\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n\n\nReport the QA statistic for instrument validity and the conditional F-statistics for instrument strength for each phenotype (Sanderson et al. 2019; Sanderson, Spiller, and Bowden 2021).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse tgse)\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n Q_A statistic for instrument validity; chi2(70) = 152.88 (p =  0.0000)\n Conditional F-statistics for instrument strength:\n F_x1 = 130.31   (ldlcbeta)\n F_x2 = 36.29    (hdlcbeta)\n F_x3 = 40.44    (tgbeta)\n\n\n\n\n\nFit MVMR-Egger regression (Rees, Wood, and Burgess 2017), by default orienting the model to the first phenotype in the main varlist.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                       MVMR-Egger model oriented wrt: ldlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.469\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .5672993   .1002611     5.66   0.000      .370791    .7638075\n    hdlcbeta |  -.1364113   .1332727    -1.02   0.306    -.3976209    .1247983\n      tgbeta |   .2739803   .1246927     2.20   0.028     .0295871    .5183735\n       _cons |  -.0093655   .0054187    -1.73   0.084     -.019986     .001255\n------------------------------------------------------------------------------\n\n\nWe can also orient the model with respect to HDL-C instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)\n\n                                       MVMR-Egger model oriented wrt: hdlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.501\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4286398   .0614056     6.98   0.000      .308287    .5489926\n    hdlcbeta |  -.1989637   .1541909    -1.29   0.197    -.5011723    .1032449\n      tgbeta |   .2256794   .1243221     1.82   0.069    -.0179875    .4693463\n       _cons |   .0002155   .0036218     0.06   0.953     -.006883    .0073141\n------------------------------------------------------------------------------\n\n\nOr we can orient the model with respect to triglycerides instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)\n\n                                         MVMR-Egger model oriented wrt: tgbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.499\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4203073   .0660026     6.37   0.000     .2909447      .54967\n    hdlcbeta |  -.1903089   .1321536    -1.44   0.150    -.4493252    .0687075\n      tgbeta |   .2065651   .1365427     1.51   0.130    -.0610537     .474184\n       _cons |   .0013499    .003951     0.34   0.733    -.0063939    .0090936\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nBurgess, S, F Dudbridge, and SG Thompson. 2015. “Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.” American Journal of Epidemiology 181 (4): 251–60. https://doi.org/10.1093/aje/kwu283.\n\n\nDo, Ron, Cristen J Willer, Ellen M Schmidt, Sebanti Sengupta, Chi Gao, Gina M Peloso, Stefan Gustafsson, et al. 2013. “Common Variants Associated with Plasma Triglycerides and Risk for Coronary Artery Disease.” Nature Genetics 45 (11): 1345–52. https://doi.org/10.1038/ng.2795.\n\n\nRees, J, A Wood, and S Burgess. 2017. “Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy.” Statistics in Medicine 36 (29): 4705–18. https://doi.org/10.1002/sim.7492.\n\n\nSanderson, E, G Davey Smith, F Windmeijer, and J Bowden. 2019. “An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.” International Journal of Epidemiology 48 (3): 713–27. https://doi.org/10.1093/ije/dyy262.\n\n\nSanderson, E, W Spiller, and J Bowden. 2021. “Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization.” Statistics in Medicine 40 (25): 5434–52. https://doi.org/10.1002/sim.9133."
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-    "text": "net install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust"
+    "text": "Fit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable Mendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c (Burgess, Dudbridge, and Thompson 2015).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 2\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.514\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4670719   .0581901     8.03   0.000     .3530214    .5811224\n    hdlcbeta |  -.2930048   .1211822    -2.42   0.016    -.5305175   -.0554921\n------------------------------------------------------------------------------\n\n\nAdditionally include a third phenotype – triglycerides.\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n\n\nReport the QA statistic for instrument validity and the conditional F-statistics for instrument strength for each phenotype (Sanderson et al. 2019; Sanderson, Spiller, and Bowden 2021).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse tgse)\n\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n Q_A statistic for instrument validity; chi2(70) = 152.88 (p =  0.0000)\n Conditional F-statistics for instrument strength:\n F_x1 = 130.31   (ldlcbeta)\n F_x2 = 36.29    (hdlcbeta)\n F_x3 = 40.44    (tgbeta)"
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-    "text": "Read in example data\nRead in example data from Do et al. (2013).\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\nSelect observations (p-value with exposure &lt; 10-8).\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)"
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+    "text": "Fit MVMR-Egger regression (Rees, Wood, and Burgess 2017), by default orienting the model to the first phenotype in the main varlist.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n                                       MVMR-Egger model oriented wrt: ldlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.469\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .5672993   .1002611     5.66   0.000      .370791    .7638075\n    hdlcbeta |  -.1364113   .1332727    -1.02   0.306    -.3976209    .1247983\n      tgbeta |   .2739803   .1246927     2.20   0.028     .0295871    .5183735\n       _cons |  -.0093655   .0054187    -1.73   0.084     -.019986     .001255\n------------------------------------------------------------------------------\n\n\nWe can also orient the model with respect to HDL-C instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)\n\n                                       MVMR-Egger model oriented wrt: hdlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.501\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4286398   .0614056     6.98   0.000      .308287    .5489926\n    hdlcbeta |  -.1989637   .1541909    -1.29   0.197    -.5011723    .1032449\n      tgbeta |   .2256794   .1243221     1.82   0.069    -.0179875    .4693463\n       _cons |   .0002155   .0036218     0.06   0.953     -.006883    .0073141\n------------------------------------------------------------------------------\n\n\nOr we can orient the model with respect to triglycerides instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)\n\n                                         MVMR-Egger model oriented wrt: tgbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.499\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4203073   .0660026     6.37   0.000     .2909447      .54967\n    hdlcbeta |  -.1903089   .1321536    -1.44   0.150    -.4493252    .0687075\n      tgbeta |   .2065651   .1365427     1.51   0.130    -.0610537     .474184\n       _cons |   .0013499    .003951     0.34   0.733    -.0063939    .0090936\n------------------------------------------------------------------------------"
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-    "text": "mrforest examples\nForest plot of genotype specific IV estimates and IVW and MR-Egger estimates, labelling the genotypes with their RSID.\n\nmrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) ///\n    xlabel(-5,-4,-3,-2,-1,0,1,2,3,4,5)\nqui gr export mrforest.svg, width(600) replace\n\n\n\n\n\n\nExample forest plot of genotype specific IV estimates."
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-    "text": "mregger examples\nUsing the data provided by Do et al. (2013) recreate from Bowden et al. (2016) the Table 4, LDL-c “All genetic variants” estimates.\nIVW (with fixed effect standard errors, i.e. variance of residuals [residual variance] constrained to 1).\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\n\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance [multiplicative random effects]).\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger reporting I2GX statistic and heterogeneity Q-test.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse) heterogi\n\n                                           Q_GX statistic (weighted) = 3454.26\n                                         I^2_GX statistic (weighted) =  97.92%\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n                Ruecker's Q for heterogeneity; chi2(71) = 170.11 (p =  0.0000)\n                             I-squared statistic = 58.3% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using a t-distribution for inference (p-values) & CI limits.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, tdist\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      t    P&gt;|t|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4110251    .8236012\n       _cons |  -.0087706   .0054812    -1.60   0.114    -.0196998    .0021585\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation and reporting heterogeneity (Rucker’s) Q-test.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial heterogi\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n                Ruecker's Q for heterogeneity; chi2(71) = 169.98 (p =  0.0000)\n                             I-squared statistic = 58.2% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------"
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+    "text": "The paper is available here (Spiller, Davies, and Palmer 2018)."
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-    "section": "mreggersimex examples",
-    "text": "mreggersimex examples\nSIMEX suppressing bootstrapped SEs (for speed - remove the noboot option to obtain SEs).\n\nmreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot\nqui gr export mreggersimex-plot.svg, width(600) replace\n\n                                                      Number of genotypes = 73\n                                                    Bootstrap replications = 0\n                                                  Simulation replications = 50\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n       slope |   .6256194          .        .       .            .           .\n       _cons |  -.0089987          .        .       .            .           .\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nSIMEX plots for the MR-Egger model."
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+    "section": "mrrobust set-up",
+    "text": "mrrobust set-up\nInstall the mrrobust package using the user-written github package.\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\nIf you have Stata 12 or earlier you will need to install some of these manually (see here for instructions)."
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-    "text": "mreggerplot examples\n\nmreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1\nqui gr export mreggerplot.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nScatter plot showing the genotype summary level data points and the fitted MR-Egger model."
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+    "section": "Summary data description and overview",
+    "text": "Summary data description and overview\nAccompanying this paper are two sets of data BMI.csv, and Height.csv, containing the set of summary estimates required for performing the BMI-serum glucose and height-serum glucose analyses respectively. Each dataset is organised into 5 columns under the following headings:\n\nSNP: A set of identifying numbers (rsids) for each genetic variant\nbeta.exposure: a set of values representing the coefficient from regressing the exposure upon the genetic variant within a GWAS\nbeta.outcome: a set of values representing the coefficient from regressing the outcome upon the genetic variant within a GWAS\nse.exposure: a set of values representing the standard error corresponding to the coefficient in beta.exposure\nse.outcome: a set of values representing the standard error corresponding to the coefficient in beta.outcome.\n\nNote Stata removes the . in the variable names when the data is imported.\nIn BMI.csv the exposure is standardised body mass index (BMI), and is therefore interpreted on a standard deviation scale. The summary statistics are reported by Locke et al. (2015). In Height.csv the exposure is standardised height in meters and also interpreted on a standard deviation scale. The summary statistics are reported by Wood et al. (2014).\nFor both analyses log transformed serum glucose was used as an outcome, reported by Shin et al. (2014). All the data was obtained from the MRBase GWAS catalogue available at https://www.mrbase.org/ (Hemani et al. 2018) which has now been superseeded by the OpenGWAS API. Genetic variants were pruned so as to be independent (\\(R^2\\) = 0.0001), and the effect alleles were aligned between the exposure and outcome datasets using the MRBase web application, prior to implementing mrrobust."
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-    "text": "mrmedian examples\nWeighted median estimator.\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0633137     7.24   0.000     .3341648    .5823499\n------------------------------------------------------------------------------"
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+    "section": "Stata output for each estimation method using mrrobust: BMI-Serum Glucose",
+    "text": "Stata output for each estimation method using mrrobust: BMI-Serum Glucose\n\nRead in data\n\nimport delimited using BMI.csv, clear\n\n\n\nIVW\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n\n                                                      Number of genotypes = 79\n                                              Residual standard error =  1.039\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\nbetaexposure |   .0231866   .0079957     2.90   0.004     .0075154    .0388578\n------------------------------------------------------------------------------\n\n\n\n\nMR-Egger\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n\n                                                      Number of genotypes = 79\n                                              Residual standard error =  1.046\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\n       slope |   .0218507   .0221852     0.98   0.325    -.0216315    .0653329\n       _cons |    .000038   .0005877     0.06   0.948    -.0011138    .0011897\n------------------------------------------------------------------------------\n\n\n\n\nPlot of the MR-Egger model\n\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-bmi.svg, width(600) replace\n\n\n\n\n\n\nPlot of the MR-Egger model for the BMI data.\n\n\n\n\n\n\nWeighted median\n\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0339256   .0120248     2.82   0.005     .0103576    .0574937\n------------------------------------------------------------------------------"
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-    "section": "mrmodal examples",
-    "text": "mrmodal examples\n\nmrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)\nqui gr export mrmodalplot.svg, width(600) replace\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4198713   .2267297     1.85   0.064    -.0245107    .8642533\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4218667   .1981169     2.13   0.033     .0335647    .8101688\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1356432     3.63   0.000     .2258796     .757591\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of the phi parameter.\n\n\n\n\nSimple mode estimator.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1276278     3.85   0.000     .2415894    .7418812\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n\n\nSimple mode estimator with NOME assumption.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, nome\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1266936     3.88   0.000     .2434204    .7400501\n------------------------------------------------------------------------------"
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+    "section": "Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose",
+    "text": "Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose\nUsing the mrmodalplot command, modal estimates are calculated using bandwidths of 0.25, 0.5, and 1 respectively. This command also produces three overlaid density plots for each value, as shown in the Figure.\n\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-bmi.svg, width(600) replace\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0374507   .0424036     0.88   0.377    -.0456588    .1205602\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0416424   .0369758     1.13   0.260    -.0308289    .1141137\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0431816   .0281684     1.53   0.125    -.0120274    .0983906\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of phi."
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-    "text": "mrfunnel examples\n\nmrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)\nqui gr export mrfunnel.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nExample funnel plot."
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+    "text": "Stata output for each estimation method using mrrobust: Height-Serum Glucose\n\nRead in data\n\nimport delimited using Height.csv, clear\n\n\n\nIVW\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n\n                                                     Number of genotypes = 367\n                                              Residual standard error =  1.044\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\nbetaexposure |   .0015412   .0033017     0.47   0.641      -.00493    .0080124\n------------------------------------------------------------------------------\n\n\n\n\nMR-Egger\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n\n                                                     Number of genotypes = 367\n                                              Residual standard error =  1.045\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\n       slope |  -.0025878   .0091178    -0.28   0.777    -.0204584    .0152828\n       _cons |   .0001338   .0002754     0.49   0.627     -.000406    .0006736\n------------------------------------------------------------------------------\n\n\n\n\nPlot of the MR-Egger model\n\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-height.svg, width(600) replace\n\n\n\n\n\n\nPlot of the MR-Egger model for the Height data.\n\n\n\n\n\n\nWeighted median\n\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |          0   .0052323     0.00   1.000    -.0102551    .0102551\n------------------------------------------------------------------------------"
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-    "section": "Leave one out analysis",
-    "text": "Leave one out analysis\nCreate a smaller subset of data.\n\ngen byte sel2 = (ldlcp2 &lt; 1e-25)\n\n\n\n\nPerform leave one out analysis using the IVW estimator\n\nmrleaveoneout chdbeta ldlcbeta if sel2==1, gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-01.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nPlot showing leave one (genotype) out at a time analysis for the IVW estimate.\n\n\n\n\nPerform leave one out analysis using MVMR collecting the estimate for LDL-c.\n\nmrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta if sel2==1, ///\n    method(mvmr) gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-02.svg, width(600) replace\n\n\n\n\n\n\nPlot showing leave one (genotype) out at a time analysis for the MVMR estimate."
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+    "section": "Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose",
+    "text": "Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose\n\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-height.svg, width(600) replace\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0061368   .0245472     0.25   0.803    -.0419748    .0542484\n------------------------------------------------------------------------------\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0015595   .0212232     0.07   0.941    -.0400372    .0431561\n------------------------------------------------------------------------------\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |  -.0054772   .0149074    -0.37   0.713    -.0346952    .0237408\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of phi."
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     "section": "References",
-    "text": "References\n\n\nBowden, Jack, George Davey Smith, Philip C Haycock, and Stephen Burgess. 2016. “Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator.” Genetic Epidemiology 40 (4): 304–14. https://doi.org/10.1002/gepi.21965.\n\n\nDo, Ron, Cristen J Willer, Ellen M Schmidt, Sebanti Sengupta, Chi Gao, Gina M Peloso, Stefan Gustafsson, et al. 2013. “Common Variants Associated with Plasma Triglycerides and Risk for Coronary Artery Disease.” Nature Genetics 45 (11): 1345–52. https://doi.org/10.1038/ng.2795."
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-    "text": "This example shows how to conveniently save and export your estimates using the r(table) matrix that is now returned by each command.\n\n\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\n\n\n\nSelect observations (p-value with exposure &lt; 10-8)\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)\n\n\n\n\n\n\n\nIVW (with fixed effect standard errors)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\nmat ivw = r(table)\n\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\nmat mregger = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\nmat radial = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat mode = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n\n\nWeighted median estimator\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat median = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0653722     7.01   0.000     .3301302    .5863845\n------------------------------------------------------------------------------\n\n\n\n\n\nCheck our matrices\n\nmat dir\nmat list ivw\nmat list mregger\nmat list radial\nmat list mode\nmat list median\n\n       median[9,1]\n         mode[9,1]\n       radial[9,2]\n      mregger[9,2]\n          ivw[9,1]\n\n\nivw[9,1]\n          chdbeta:\n         ldlcbeta\n     b  .48150551\n    se  .03822098\n     z  12.597937\npvalue  2.167e-36\n    ll  .40659377\n    ul  .55641726\n    df          .\n  crit   1.959964\n eform          0\n\n\nmregger[9,2]\n           chdbeta:    chdbeta:\n             slope       _cons\n     b   .61731315  -.00877065\n    se   .10345735   .00548118\n     z   5.9668371    -1.60014\npvalue   2.419e-09   .10956752\n    ll   .41454047  -.01951356\n    ul   .82008582   .00197226\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nradial[9,2]\n          radialGD:   radialGD:\n          radialGP       _cons\n     b   .64258196  -.57373006\n    se   .11578709   .35456584\n     z   5.5496858    -1.61812\npvalue   2.862e-08   .10563675\n    ll   .41564344  -1.2686663\n    ul   .86952048   .12120621\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nmode[9,1]\n             beta\n     b   .4789702\n    se  .06631445\n     z   7.222712\npvalue  5.096e-13\n    ll  .34899626\n    ul  .60894415\n    df          .\n  crit   1.959964\n eform          0\n\n\nmedian[9,1]\n             beta\n     b  .45825733\n    se   .0653722\n     z  7.0099725\npvalue  2.384e-12\n    ll  .33013017\n    ul  .58638448\n    df          .\n  crit   1.959964\n eform          0\n\n\nCombined into single matrix\n\nmat output = (ivw, mregger, radial, mode, median)\nmat colnames output = ivw_beta mregger_beta mregger_cons ///\n    radial_beta radial_cons mode_beta median_beta\nmat coleq output = \"\" \"\" \"\" \"\" \"\" \"\" \"\"\nmat output = output'\nmat list output, format(%4.3f)\n\noutput[7,9]\n                   b      se       z  pvalue      ll      ul      df    crit   eform\n    ivw_beta   0.482   0.038  12.598   0.000   0.407   0.556       .   1.960   0.000\nmregger_beta   0.617   0.103   5.967   0.000   0.415   0.820       .   1.960   0.000\nmregger_cons  -0.009   0.005  -1.600   0.110  -0.020   0.002       .   1.960   0.000\n radial_beta   0.643   0.116   5.550   0.000   0.416   0.870       .   1.960   0.000\n radial_cons  -0.574   0.355  -1.618   0.106  -1.269   0.121       .   1.960   0.000\n   mode_beta   0.479   0.066   7.223   0.000   0.349   0.609       .   1.960   0.000\n median_beta   0.458   0.065   7.010   0.000   0.330   0.586       .   1.960   0.000\n\n\nExport matrix to dataset\n\ndrop _all\nsvmat output, names(col)\nlocal rownames : rownames output\ndi \"`rownames'\"\ntokenize `rownames'\ngen str15 estimate = \"\"\nforvalues i = 1/7 {\n    replace estimate = \"``i''\" in `i'\n}\n\nShow dataset\n\nlist estimate b se z pvalue ll ul, clean noobs\n\n        estimate           b         se          z     pvalue          ll         ul  \n        ivw_beta    .4815055    .038221   12.59794   2.17e-36    .4065938   .5564172  \n    mregger_beta    .6173131   .1034573   5.966837   2.42e-09    .4145405   .8200858  \n    mregger_cons   -.0087707   .0054812   -1.60014   .1095675   -.0195136   .0019723  \n     radial_beta    .6425819   .1157871   5.549686   2.86e-08    .4156434   .8695205  \n     radial_cons   -.5737301   .3545658   -1.61812   .1056367   -1.268666   .1212062  \n       mode_beta    .4789702   .0663145   7.222712   5.10e-13    .3489963   .6089441  \n     median_beta    .4582573   .0653722   7.009973   2.38e-12    .3301302   .5863845  \n\n\nSave dataset\nsave myestimates, replace\nExport as tab-delimited textfile\nexport delimited using myestimates.txt, delimiter(tab) replace"
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-  {
-    "objectID": "examples/save-estimates/index.html#setup-example-data",
-    "href": "examples/save-estimates/index.html#setup-example-data",
-    "title": "How to collect and export results",
-    "section": "",
-    "text": "use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\n\n\n\nSelect observations (p-value with exposure &lt; 10-8)\n\ngen byte sel1 = (ldlcp2 &lt; 1e-8)"
-  },
-  {
-    "objectID": "examples/save-estimates/index.html#fit",
-    "href": "examples/save-estimates/index.html#fit",
-    "title": "How to collect and export results",
-    "section": "",
-    "text": "IVW (with fixed effect standard errors)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\nmat ivw = r(table)\n\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\nmat mregger = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\nmat radial = r(table)\n\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat mode = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n\n\nWeighted median estimator\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat median = r(table)\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P&gt;|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0653722     7.01   0.000     .3301302    .5863845\n------------------------------------------------------------------------------"
-  },
-  {
-    "objectID": "examples/save-estimates/index.html#combine-and-export-results",
-    "href": "examples/save-estimates/index.html#combine-and-export-results",
-    "title": "How to collect and export results",
-    "section": "",
-    "text": "Check our matrices\n\nmat dir\nmat list ivw\nmat list mregger\nmat list radial\nmat list mode\nmat list median\n\n       median[9,1]\n         mode[9,1]\n       radial[9,2]\n      mregger[9,2]\n          ivw[9,1]\n\n\nivw[9,1]\n          chdbeta:\n         ldlcbeta\n     b  .48150551\n    se  .03822098\n     z  12.597937\npvalue  2.167e-36\n    ll  .40659377\n    ul  .55641726\n    df          .\n  crit   1.959964\n eform          0\n\n\nmregger[9,2]\n           chdbeta:    chdbeta:\n             slope       _cons\n     b   .61731315  -.00877065\n    se   .10345735   .00548118\n     z   5.9668371    -1.60014\npvalue   2.419e-09   .10956752\n    ll   .41454047  -.01951356\n    ul   .82008582   .00197226\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nradial[9,2]\n          radialGD:   radialGD:\n          radialGP       _cons\n     b   .64258196  -.57373006\n    se   .11578709   .35456584\n     z   5.5496858    -1.61812\npvalue   2.862e-08   .10563675\n    ll   .41564344  -1.2686663\n    ul   .86952048   .12120621\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nmode[9,1]\n             beta\n     b   .4789702\n    se  .06631445\n     z   7.222712\npvalue  5.096e-13\n    ll  .34899626\n    ul  .60894415\n    df          .\n  crit   1.959964\n eform          0\n\n\nmedian[9,1]\n             beta\n     b  .45825733\n    se   .0653722\n     z  7.0099725\npvalue  2.384e-12\n    ll  .33013017\n    ul  .58638448\n    df          .\n  crit   1.959964\n eform          0\n\n\nCombined into single matrix\n\nmat output = (ivw, mregger, radial, mode, median)\nmat colnames output = ivw_beta mregger_beta mregger_cons ///\n    radial_beta radial_cons mode_beta median_beta\nmat coleq output = \"\" \"\" \"\" \"\" \"\" \"\" \"\"\nmat output = output'\nmat list output, format(%4.3f)\n\noutput[7,9]\n                   b      se       z  pvalue      ll      ul      df    crit   eform\n    ivw_beta   0.482   0.038  12.598   0.000   0.407   0.556       .   1.960   0.000\nmregger_beta   0.617   0.103   5.967   0.000   0.415   0.820       .   1.960   0.000\nmregger_cons  -0.009   0.005  -1.600   0.110  -0.020   0.002       .   1.960   0.000\n radial_beta   0.643   0.116   5.550   0.000   0.416   0.870       .   1.960   0.000\n radial_cons  -0.574   0.355  -1.618   0.106  -1.269   0.121       .   1.960   0.000\n   mode_beta   0.479   0.066   7.223   0.000   0.349   0.609       .   1.960   0.000\n median_beta   0.458   0.065   7.010   0.000   0.330   0.586       .   1.960   0.000\n\n\nExport matrix to dataset\n\ndrop _all\nsvmat output, names(col)\nlocal rownames : rownames output\ndi \"`rownames'\"\ntokenize `rownames'\ngen str15 estimate = \"\"\nforvalues i = 1/7 {\n    replace estimate = \"``i''\" in `i'\n}\n\nShow dataset\n\nlist estimate b se z pvalue ll ul, clean noobs\n\n        estimate           b         se          z     pvalue          ll         ul  \n        ivw_beta    .4815055    .038221   12.59794   2.17e-36    .4065938   .5564172  \n    mregger_beta    .6173131   .1034573   5.966837   2.42e-09    .4145405   .8200858  \n    mregger_cons   -.0087707   .0054812   -1.60014   .1095675   -.0195136   .0019723  \n     radial_beta    .6425819   .1157871   5.549686   2.86e-08    .4156434   .8695205  \n     radial_cons   -.5737301   .3545658   -1.61812   .1056367   -1.268666   .1212062  \n       mode_beta    .4789702   .0663145   7.222712   5.10e-13    .3489963   .6089441  \n     median_beta    .4582573   .0653722   7.009973   2.38e-12    .3301302   .5863845  \n\n\nSave dataset\nsave myestimates, replace\nExport as tab-delimited textfile\nexport delimited using myestimates.txt, delimiter(tab) replace"
+    "text": "References\n\n\nHemani, Gibran, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, et al. 2018. “The MR-Base platform supports systematic causal inference across the human phenome.” eLife 7: e34408. https://doi.org/10.7554/eLife.34408.\n\n\nLocke, Adam E, Bratati Kahali, Sonja I Berndt, Anne E Justice, Tune H Pers, Felix R Day, Corey Powell, et al. 2015. “Genetic Studies of Body Mass Index Yield New Insights for Obesity Biology.” Nature 518 (7538): 197–206. https://doi.org/10.1038/nature14177.\n\n\nShin, So-Youn, Eric B Fauman, Ann-Kristin Petersen, Jan Krumsiek, Rita Santos, Jie Huang, Matthias Arnold, et al. 2014. “An Atlas of Genetic Influences on Human Blood Metabolites.” Nature Genetics 46 (6): 543–50. https://doi.org/10.1038/ng.2982.\n\n\nSpiller, Wes, Neil M Davies, and Tom M Palmer. 2018. “Software application profile: mrrobust—a tool for performing two-sample summary Mendelian randomization analyses.” International Journal of Epidemiology 48 (3): 684–90. https://doi.org/10.1093/ije/dyy195.\n\n\nWood, Andrew R, Tonu Esko, Jian Yang, Sailaja Vedantam, Tune H Pers, Stefan Gustafsson, Audrey Y Chu, et al. 2014. “Defining the Role of Common Variation in the Genomic and Biological Architecture of Adult Human Height.” Nature Genetics 46 (11): 1173–86. https://doi.org/10.1038/ng.3097."
   }
 ]
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diff --git a/docs/site_libs/bootstrap/bootstrap.min.css b/docs/site_libs/bootstrap/bootstrap.min.css
index 5d495494..04d66ed4 100644
--- a/docs/site_libs/bootstrap/bootstrap.min.css
+++ b/docs/site_libs/bootstrap/bootstrap.min.css
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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 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auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto 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.accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion 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.nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 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 * that seems to be what ansi_up emits
 *
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diff --git a/docs/site_libs/quarto-html/quarto-syntax-highlighting.css b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
index d9fd98f0..b30ce576 100644
--- a/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
+++ b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
@@ -85,6 +85,7 @@ code span.st {
 
 code span.cf {
   color: #003B4F;
+  font-weight: bold;
   font-style: inherit;
 }
 
@@ -193,6 +194,7 @@ code span.dv {
 
 code span.kw {
   color: #003B4F;
+  font-weight: bold;
   font-style: inherit;
 }
 
diff --git a/docs/site_libs/quarto-html/quarto.js b/docs/site_libs/quarto-html/quarto.js
index 3ebd49cb..39e68699 100644
--- a/docs/site_libs/quarto-html/quarto.js
+++ b/docs/site_libs/quarto-html/quarto.js
@@ -94,7 +94,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       if (link.href.indexOf("#") !== -1) {
         const anchor = link.href.split("#")[1];
         const heading = window.document.querySelector(
-          `[data-anchor-id=${anchor}]`
+          `[data-anchor-id="${anchor}"]`
         );
         if (heading) {
           // Add the class
@@ -134,8 +134,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       window.innerHeight + window.pageYOffset >=
       window.document.body.offsetHeight
     ) {
+      // This is the no-scroll case where last section should be the active one
       sectionIndex = 0;
     } else {
+      // This finds the last section visible on screen that should be made active
       sectionIndex = [...sections].reverse().findIndex((section) => {
         if (section) {
           return window.pageYOffset >= section.offsetTop - sectionMargin;
@@ -317,6 +319,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           for (const child of el.children) {
             child.style.opacity = 0;
             child.style.overflow = "hidden";
+            child.style.pointerEvents = "none";
           }
 
           nexttick(() => {
@@ -358,6 +361,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
 
               const clone = child.cloneNode(true);
               clone.style.opacity = 1;
+              clone.style.pointerEvents = null;
               clone.style.display = null;
               toggleContents.append(clone);
             }
@@ -432,6 +436,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           for (const child of el.children) {
             child.style.opacity = 1;
             child.style.overflow = null;
+            child.style.pointerEvents = null;
           }
 
           const placeholderEl = window.document.getElementById(
@@ -739,6 +744,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     // Process the collapse state if this is an UL
     if (el.tagName === "UL") {
       if (tocOpenDepth === -1 && depth > 1) {
+        // toc-expand: false
         el.classList.add("collapse");
       } else if (
         depth <= tocOpenDepth ||
@@ -757,10 +763,9 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   };
 
   // walk the TOC and expand / collapse any items that should be shown
-
   if (tocEl) {
-    walk(tocEl, 0);
     updateActiveLink();
+    walk(tocEl, 0);
   }
 
   // Throttle the scroll event and walk peridiocally
@@ -779,6 +784,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   window.addEventListener(
     "resize",
     throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
       if (!isReaderMode()) {
         hideOverlappedSidebars();
       }
diff --git a/docs/site_libs/quarto-nav/quarto-nav.js b/docs/site_libs/quarto-nav/quarto-nav.js
index ebfc262e..38cc4305 100644
--- a/docs/site_libs/quarto-nav/quarto-nav.js
+++ b/docs/site_libs/quarto-nav/quarto-nav.js
@@ -5,9 +5,45 @@ const headroomChanged = new CustomEvent("quarto-hrChanged", {
   composed: false,
 });
 
+const announceDismiss = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    annEl.remove();
+
+    const annId = annEl.getAttribute("data-announcement-id");
+    window.localStorage.setItem(`quarto-announce-${annId}`, "true");
+  }
+};
+
+const announceRegister = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    const annId = annEl.getAttribute("data-announcement-id");
+    const isDismissed =
+      window.localStorage.getItem(`quarto-announce-${annId}`) || false;
+    if (isDismissed) {
+      announceDismiss();
+      return;
+    } else {
+      annEl.classList.remove("hidden");
+    }
+
+    const actionEl = annEl.querySelector(".quarto-announcement-action");
+    if (actionEl) {
+      actionEl.addEventListener("click", function (e) {
+        e.preventDefault();
+        // Hide the bar immediately
+        announceDismiss();
+      });
+    }
+  }
+};
+
 window.document.addEventListener("DOMContentLoaded", function () {
   let init = false;
 
+  announceRegister();
+
   // Manage the back to top button, if one is present.
   let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
   const scrollDownBuffer = 5;
@@ -237,6 +273,7 @@ window.document.addEventListener("DOMContentLoaded", function () {
     const links = window.document.querySelectorAll("a");
     for (let i = 0; i < links.length; i++) {
       if (links[i].href) {
+        links[i].dataset.originalHref = links[i].href;
         links[i].href = links[i].href.replace(/\/index\.html/, "/");
       }
     }
diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js
index aefb6a58..d788a958 100644
--- a/docs/site_libs/quarto-search/quarto-search.js
+++ b/docs/site_libs/quarto-search/quarto-search.js
@@ -675,6 +675,18 @@ function showCopyLink(query, options) {
 // create the index
 var fuseIndex = undefined;
 var shownWarning = false;
+
+// fuse index options
+const kFuseIndexOptions = {
+  keys: [
+    { name: "title", weight: 20 },
+    { name: "section", weight: 20 },
+    { name: "text", weight: 10 },
+  ],
+  ignoreLocation: true,
+  threshold: 0.1,
+};
+
 async function readSearchData() {
   // Initialize the search index on demand
   if (fuseIndex === undefined) {
@@ -685,17 +697,7 @@ async function readSearchData() {
       shownWarning = true;
       return;
     }
-    // create fuse index
-    const options = {
-      keys: [
-        { name: "title", weight: 20 },
-        { name: "section", weight: 20 },
-        { name: "text", weight: 10 },
-      ],
-      ignoreLocation: true,
-      threshold: 0.1,
-    };
-    const fuse = new window.Fuse([], options);
+    const fuse = new window.Fuse([], kFuseIndexOptions);
 
     // fetch the main search.json
     const response = await fetch(offsetURL("search.json"));
@@ -1226,8 +1228,34 @@ function algoliaSearch(query, limit, algoliaOptions) {
   });
 }
 
-function fuseSearch(query, fuse, fuseOptions) {
-  return fuse.search(query, fuseOptions).map((result) => {
+let subSearchTerm = undefined;
+let subSearchFuse = undefined;
+const kFuseMaxWait = 125;
+
+async function fuseSearch(query, fuse, fuseOptions) {
+  let index = fuse;
+  // Fuse.js using the Bitap algorithm for text matching which runs in
+  // O(nm) time (no matter the structure of the text). In our case this
+  // means that long search terms mixed with large index gets very slow
+  //
+  // This injects a subIndex that will be used once the terms get long enough
+  // Usually making this subindex is cheap since there will typically be
+  // a subset of results matching the existing query
+  if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) {
+    // Use the existing subSearchFuse
+    index = subSearchFuse;
+  } else if (subSearchFuse !== undefined) {
+    // The term changed, discard the existing fuse
+    subSearchFuse = undefined;
+    subSearchTerm = undefined;
+  }
+
+  // Search using the active fuse
+  const then = performance.now();
+  const resultsRaw = await index.search(query, fuseOptions);
+  const now = performance.now();
+
+  const results = resultsRaw.map((result) => {
     const addParam = (url, name, value) => {
       const anchorParts = url.split("#");
       const baseUrl = anchorParts[0];
@@ -1244,4 +1272,19 @@ function fuseSearch(query, fuse, fuseOptions) {
       crumbs: result.item.crumbs,
     };
   });
+
+  // If we don't have a subfuse and the query is long enough, go ahead
+  // and create a subfuse to use for subsequent queries
+  if (
+    now - then > kFuseMaxWait &&
+    subSearchFuse === undefined &&
+    resultsRaw.length < fuseOptions.limit
+  ) {
+    subSearchTerm = query;
+    subSearchFuse = new window.Fuse([], kFuseIndexOptions);
+    resultsRaw.forEach((rr) => {
+      subSearchFuse.add(rr.item);
+    });
+  }
+  return results;
 }
diff --git a/site/_freeze/examples/markstat-call-R-example/index/execute-results/html.json b/site/_freeze/examples/markstat-call-R-example/index/execute-results/html.json
deleted file mode 100644
index 4d7f1b78..00000000
--- a/site/_freeze/examples/markstat-call-R-example/index/execute-results/html.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "hash": "aa0f967c8865978382fcfdb1475d20a1",
-  "result": {
-    "markdown": "---\nengine: knitr\n---\n\n\n# Run MR-Base from a Stata do-file or Stata Markdown script {#title}\n\n## Introduction\n\nThis example shows how to run R and Stata code within the same Stata Markdown (`.stmd`) script. \nThe general approach is detailed on the Stata Markdown website \n[here](https://data.princeton.edu/stata/markdown/quantiles) and \n[here](https://data.princeton.edu/stata/markdown/gettingStarted#R).\n\nThis means that we can use the functions provided by the \n[**TwoSampleMR**](https://github.com/MRCIEU/TwoSampleMR) package to obtain data from \n[MR-Base](https://www.mrbase.org/) [@mrbase].\n\nBefore you start please install the following two Stata packages from the SSC archive, so in Stata \nissue the following commands (I have commented them out because I have already installed them).\n```stata\nssc install whereis\nssc install markstat\n```\n\n## Example Stata Markdown document\n\nThe following is the example Stata Markdown document.\n\n\n````markdown\n---\ntitle: Using mrrobust in a Markstat document example\n---\n\nWe first need to register the R executable with Stata.\n```s\nif c(os) == \"Windows\" local rpath \"C:\\\\Program Files\\\\R\\\\R-4.3.0\\\\bin\\\\x64\\\\R.exe\"\nelse if c(os) == \"Unix\" local rpath \"/usr/bin/R\"\nelse local rpath \"/usr/local/bin/R\"\nwhereis R \"`rpath'\"\n```\n\n\nNext we have an R code chunk in which we install the required packages in R.\n```r\ninstall.packages(\"remotes\")\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\n```\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). \nThe R code we will use is from \n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nFirst, we load the packages into our R session.\nNote that the **foreign** package provides the \n`write.dta()` function which we will use to save the data in Stata format.\n```r\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\nOur edited version of the code starts by reading in some code\nto generate a set of plots in R.\n```r\nsource(\"mrplots.R\")\n```\n\nWe can access the data using the **MRInstruments** package.\n```r\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps <- \n  subset(gwas_catalog, \n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure <- \n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome <- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat <- harmonise_data(exposure, outcome, action = 1)\n```\nAt this point we have our harmonised genotype-exposure and \ngenotype-outcome association data saved in an object in our \nR session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n```r\n# Perform MR\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure <- \"LDL cholesterol\"\ndat$outcome <- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels <- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] <- NA\n```\n\n```r\npng(\"ldl-chd.png\", width=1000, height=1000)\nmr_plots(dat)\ndev.off()\n```\n\n![Plots generated by the TwoSampleMR R package.](ldl-chd.png)\n\nWe now save our `dat` object as a Stata dataset.\n```r\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Performing the analysis using mrrobust in Stata\n\nWe now switch from using R code chunks to Stata code chunks.\nWe read the data into Stata and list the variable names\n(note any `.` in the colnames of `dat` have been replaced with `_`).\n```s\nuse dat, clear\nds, v(28)\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with multiplicative\nstandard errors.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw\n```\n\nIt is helpful to view the forest plot of genotype specific IV estimates.\n```s\nmrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///\n\txlabel(-3,-2,-1,0,1,2,3)\ngraph export ldl-chd-mrforest.svg, width(600) replace\n```\n\n![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)\n\nWe can visualise this model with `mreggerplot`.\n```s\nmreggerplot beta_outcome se_outcome beta_exposure se_exposure\ngraph export ldl-chd-mreggerplot.svg, width(600) replace\n```\n\n![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)\n\nWe then fit the MR-Egger, median, and modal based estimators.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```s\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```s\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n````\n\nAnd we could continue with additional Stata code (or indeed R code) as we liked.\n\nNote to run this `.stmd` file in Stata we do so with the following command (specifying additional \noptions as required, see `help markstat` for more information).\n```stata\nmarkstat using markstat-call-R-example\n```\n\n## References\n\n::: {#refs}\n:::\n",
-    "supporting": [
-      "index_files"
-    ],
-    "filters": [
-      "rmarkdown/pagebreak.lua"
-    ],
-    "includes": {},
-    "engineDependencies": {},
-    "preserve": {},
-    "postProcess": true
-  }
-}
\ No newline at end of file
diff --git a/site/_freeze/examples/mrrobust-examples/index/execute-results/html.json b/site/_freeze/examples/mrrobust-examples/index/execute-results/html.json
deleted file mode 100644
index e7c67f73..00000000
--- a/site/_freeze/examples/mrrobust-examples/index/execute-results/html.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "hash": "f0aefbb3559aee5998a32099f79b14ac",
-  "result": {
-    "markdown": "---\ntitle: Examples from helpfiles in the mrrobust package\n---\n\n\n\n\n\n\n\n\n## Install the mrrobust package\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\n\n## Read in example data\n\nRead in example data from @do-natgen-2013.\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n```\n:::\n\n\nSelect observations (*p*-value with exposure < 10^-8^).\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\ngen byte sel1 = (ldlcp2 < 1e-8)\n```\n:::\n\n\n## mrforest examples\nForest plot of genotype specific IV estimates and IVW and MR-Egger estimates, \nlabelling the genotypes with their RSID.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) ///\n    xlabel(-5,-4,-3,-2,-1,0,1,2,3,4,5)\nqui gr export mrforest.svg, width(600) replace\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Example forest plot of genotype specific IV estimates.](mrforest.svg){fig-align='center' fig-alt='Example forest plot of genotype specific IV estimates.'}\n:::\n:::\n\n\n## mregger examples\nUsing the data provided by @do-natgen-2013 \nrecreate from @bowden-genepi-2016 the Table 4, LDL-c \"All genetic variants\" estimates.\n\nIVW (with fixed effect standard errors, i.e. variance of residuals \n[residual variance] constrained to 1).\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nMR-Egger (with SEs using an unconstrained residual variance \n[multiplicative random effects]).\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nMR-Egger reporting I^2^~GX~ statistic and heterogeneity Q-test.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse) heterogi\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                           Q_GX statistic (weighted) = 3454.26\n                                         I^2_GX statistic (weighted) =  97.92%\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n                Ruecker's Q for heterogeneity; chi2(71) = 170.11 (p =  0.0000)\n                             I-squared statistic = 58.3% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nMR-Egger using a t-distribution for inference (p-values) & CI limits.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, tdist\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4110251    .8236012\n       _cons |  -.0087706   .0054812    -1.60   0.114    -.0196998    .0021585\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nMR-Egger using the radial formulation.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nMR-Egger using the radial formulation and reporting heterogeneity (Rucker's) Q-test.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial heterogi\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n                Ruecker's Q for heterogeneity; chi2(71) = 169.98 (p =  0.0000)\n                             I-squared statistic = 58.2% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n## mreggersimex examples\nSIMEX suppressing bootstrapped SEs (for speed - remove the `noboot` option to obtain SEs).\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot\nqui gr export mreggersimex-plot.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                    Bootstrap replications = 0\n                                                  Simulation replications = 50\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n       slope |   .6256194          .        .       .            .           .\n       _cons |  -.0089987          .        .       .            .           .\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![SIMEX plots for the MR-Egger model.](mreggersimex-plot.svg){fig-align='center' fig-alt='SIMEX plots for the MR-Egger model.'}\n:::\n:::\n\n\n## mreggerplot examples\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1\nqui gr export mreggerplot.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Scatter plot showing the genotype summary level data points and the fitted MR-Egger model.](mreggerplot.svg){fig-align='center' fig-alt='Scatter plot showing the genotype summary level data points and the fitted MR-Egger model.'}\n:::\n:::\n\n\n## mrmedian examples\nWeighted median estimator.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0633137     7.24   0.000     .3341648    .5823499\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n## mrmodal examples\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)\nqui gr export mrmodalplot.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4198713   .2267297     1.85   0.064    -.0245107    .8642533\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4218667   .1981169     2.13   0.033     .0335647    .8101688\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1356432     3.63   0.000     .2258796     .757591\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Densities of the IV estimates using different values of the phi parameter.](mrmodalplot.svg){fig-align='center' fig-alt='Densities of the IV estimates using different values of the phi parameter.'}\n:::\n:::\n\n\nSimple mode estimator.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1276278     3.85   0.000     .2415894    .7418812\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nWeighted mode estimator.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nSimple mode estimator with NOME assumption.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, nome\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4917353   .1266936     3.88   0.000     .2434204    .7400501\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n## mrfunnel examples\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)\nqui gr export mrfunnel.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Example funnel plot.](mrfunnel.svg){fig-align='center' fig-alt='Example funnel plot.'}\n:::\n:::\n\n\n## Leave one out analysis\nCreate a smaller subset of data.\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\ngen byte sel2 = (ldlcp2 < 1e-25)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n\nPerform leave one out analysis using the IVW estimator\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrleaveoneout chdbeta ldlcbeta if sel2==1, gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-01.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Plot showing leave one (genotype) out at a time analysis for the IVW estimate.](mrleaveoneout-plot-01.svg){fig-align='center' fig-alt='Plot showing leave one (genotype) out at a time analysis for the IVW estimate.'}\n:::\n:::\n\n\nPerform leave one out analysis using MVMR collecting the estimate for LDL-c.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta if sel2==1, ///\n    method(mvmr) gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-02.svg, width(600) replace\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Plot showing leave one (genotype) out at a time analysis for the MVMR estimate.](mrleaveoneout-plot-02.svg){fig-align='center' fig-alt='Plot showing leave one (genotype) out at a time analysis for the MVMR estimate.'}\n:::\n:::\n\n\n## References\n\n::: {#refs}\n:::\n\n",
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-    "markdown": "---\n---\n\n\n\n\n\n\n\n\n# Example demonstrating MVMR commands\n\nRead in the @do-natgen-2013 example dataset.\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n\nSelect observations (p-value with LDL-C < 10<sup>-8</sup>)\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\ngen byte sel1 = (ldlcp2 < 1e-8)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n\n## MV-IVW\n\nFit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable \nMendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c [@burgess-aje-2015].\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] if sel1==1\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 2\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.514\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4670719   .0581901     8.03   0.000     .3530214    .5811224\n    hdlcbeta |  -.2930048   .1211822    -2.42   0.016    -.5305175   -.0554921\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nAdditionally include a third phenotype -- triglycerides.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nReport the Q<sub>A</sub> statistic for instrument validity and the conditional \nF-statistics for instrument strength for each phenotype [@sanderson-ije-2019; \n@sanderson-statsmed-2021].\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse tgse)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                                Standard errors: Random effect\n                                              Residual standard error =  1.490\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113\n    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211\n      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755\n------------------------------------------------------------------------------\n Q_A statistic for instrument validity; chi2(70) = 152.88 (p =  0.0000)\n Conditional F-statistics for instrument strength:\n F_x1 = 130.31   (ldlcbeta)\n F_x2 = 36.29    (hdlcbeta)\n F_x3 = 40.44    (tgbeta)\n```\n:::\n:::\n\n\n## MVMR-Egger\n\nFit MVMR-Egger regression [@rees-statsmed-2017], by default orienting the model \nto the first phenotype in the main varlist.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                       MVMR-Egger model oriented wrt: ldlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.469\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .5672993   .1002611     5.66   0.000      .370791    .7638075\n    hdlcbeta |  -.1364113   .1332727    -1.02   0.306    -.3976209    .1247983\n      tgbeta |   .2739803   .1246927     2.20   0.028     .0295871    .5183735\n       _cons |  -.0093655   .0054187    -1.73   0.084     -.019986     .001255\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nWe can also orient the model with respect to HDL-C instead of LDL-C.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                       MVMR-Egger model oriented wrt: hdlcbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.501\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4286398   .0614056     6.98   0.000      .308287    .5489926\n    hdlcbeta |  -.1989637   .1541909    -1.29   0.197    -.5011723    .1032449\n      tgbeta |   .2256794   .1243221     1.82   0.069    -.0179875    .4693463\n       _cons |   .0002155   .0036218     0.06   0.953     -.006883    .0073141\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nOr we can orient the model with respect to triglycerides instead of LDL-C.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                         MVMR-Egger model oriented wrt: tgbeta\n                                                      Number of genotypes = 73\n                                                      Number of phenotypes = 3\n                                              Residual standard error =  1.499\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4203073   .0660026     6.37   0.000     .2909447      .54967\n    hdlcbeta |  -.1903089   .1321536    -1.44   0.150    -.4493252    .0687075\n      tgbeta |   .2065651   .1365427     1.51   0.130    -.0610537     .474184\n       _cons |   .0013499    .003951     0.34   0.733    -.0063939    .0090936\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n## References\n\n::: {#refs}\n:::\n",
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-    "markdown": "---\nengine: knitr\n---\n\n\n# How to include mrrobust Stata code in an R Markdown or Quarto document using the Statamarkdown R package\n\n## Introduction\n\nThis example shows how to run R and Stata code within the same R Markdown (`.Rmd`) file using the **Statamarkdown** R package. More information about this package is available [here](https://github.com/Hemken/Statamarkdown) and [here](https://www.ssc.wisc.edu/~hemken/Stataworkshops/Statamarkdown/stata-and-r-markdown.html). To install this package and load it into your current R session run the following in R.\n```r\n# install.packages(\"Statamarkdown\") # uncomment on first run\nlibrary(Statamarkdown)\n```\n\nNote when writing our Stata code chunks we need to be careful when we specify the `collectcode=TRUE` code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.\n\nUsing R and Stata code in the same script means that we can use the functions provided by the [**TwoSampleMR**](https://github.com/MRCIEU/TwoSampleMR) package to obtain data from [MR-Base](https://www.mrbase.org/) [@mrbase].\n\nNext we install the other required packages in R.\n```r\nremotes::install_github(\"MRCIEU/TwoSampleMR\")\nremotes::install_github(\"MRCIEU/MRInstruments\")\ninstall.packages(\"foreign\")\n```\n\n## Example R Markdown document\n\n\n\n````markdown\n---\ntitle: Using mrrobust in an R Markdown or Quarto document\n---\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)).\nThe R code we will use is from\n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nWe load the packages into our R session.\nNote that the **foreign** package provides the `write.dta()` function\nwhich we will use to save the data in Stata format.\n```{r}\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\n\nWe can access the data using the **MRInstruments** package.\n\n```{r}\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps <-\n  subset(gwas_catalog,\n    grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure <-\n  convert_outcome_to_exposure(\n    extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome <- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat <- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and genotype-outcome\nassociation data saved in an object in our R session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```{r}\n# Perform MR analysis\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure <- \"LDL cholesterol\"\ndat$outcome <- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels <- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] <- NA\n```\n\n\nTo proceed in Stata we can save our `dat` object as a Stata dataset\n\n```{r}\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n\n## Analysis in Stata using the mrrobust package\n\nAt this point in Stata install the **mrrobust** package and its\ndependencies if you have not done so previously.\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\n\nWe now read the dataset into Stata and look at the variable names\nand the number of observations.\n\n```{stata, collectcode=TRUE}\nqui use dat, clear\n```\n\n```{stata}\nds, v(28)\n```\n\n```{stata}\ndi _N\n```\n\n\nWe can then run the IVW model using `mregger` with fixed effect standard errors.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe\n```\n\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```{stata}\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```{stata}\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n\nAnd we could continue with additional Stata code (or indeed R code) as we like.\n````\n\nTo run this R Markdown file, either; open it in RStudio and click the `Knit` button in the toolbar of the Source code window, or run\n```r\nrmarkdown::render('rmarkdown-call-stata-example.Rmd')\n```\n\n## References\n\n::: {#refs}\n:::\n",
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-    "markdown": "---\n---\n\n\n\n\n\n\n\n\n# How to collect and export results\n\nThis example shows how to conveniently save and export your estimates using the `r(table)` matrix \nthat is now returned by each command.\n\n## Setup example data\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n\nSelect observations (p-value with exposure < 10^-8^)\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\ngen byte sel1 = (ldlcp2 < 1e-8)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n\n## Fit estimators - collecting results using `r(table)` matrix {#fit}\n\nIVW (with fixed effect standard errors)\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\nmat ivw = r(table)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                      Residual standard error constrained at 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nMR-Egger (with SEs using an unconstrained residual variance)\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\nmat mregger = r(table)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.548\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta      |\n       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858\n       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nMR-Egger using the radial formulation\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\nmat radial = r(table)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                              Residual standard error =  1.547\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD     |\n    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205\n       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nWeighted mode estimator\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat mode = r(table)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\nWeighted median estimator\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat median = r(table)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 73\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .4582573   .0653722     7.01   0.000     .3301302    .5863845\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n## Combine and export results\n\nCheck our matrices\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmat dir\nmat list ivw\nmat list mregger\nmat list radial\nmat list mode\nmat list median\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n       median[9,1]\n         mode[9,1]\n       radial[9,2]\n      mregger[9,2]\n          ivw[9,1]\n\n\nivw[9,1]\n          chdbeta:\n         ldlcbeta\n     b  .48150551\n    se  .03822098\n     z  12.597937\npvalue  2.167e-36\n    ll  .40659377\n    ul  .55641726\n    df          .\n  crit   1.959964\n eform          0\n\n\nmregger[9,2]\n           chdbeta:    chdbeta:\n             slope       _cons\n     b   .61731315  -.00877065\n    se   .10345735   .00548118\n     z   5.9668371    -1.60014\npvalue   2.419e-09   .10956752\n    ll   .41454047  -.01951356\n    ul   .82008582   .00197226\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nradial[9,2]\n          radialGD:   radialGD:\n          radialGP       _cons\n     b   .64258196  -.57373006\n    se   .11578709   .35456584\n     z   5.5496858    -1.61812\npvalue   2.862e-08   .10563675\n    ll   .41564344  -1.2686663\n    ul   .86952048   .12120621\n    df           .           .\n  crit    1.959964    1.959964\n eform           0           0\n\n\nmode[9,1]\n             beta\n     b   .4789702\n    se  .06631445\n     z   7.222712\npvalue  5.096e-13\n    ll  .34899626\n    ul  .60894415\n    df          .\n  crit   1.959964\n eform          0\n\n\nmedian[9,1]\n             beta\n     b  .45825733\n    se   .0653722\n     z  7.0099725\npvalue  2.384e-12\n    ll  .33013017\n    ul  .58638448\n    df          .\n  crit   1.959964\n eform          0\n```\n:::\n:::\n\n\nCombined into single matrix\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nmat output = (ivw, mregger, radial, mode, median)\nmat colnames output = ivw_beta mregger_beta mregger_cons ///\n\tradial_beta radial_cons mode_beta median_beta\nmat coleq output = \"\" \"\" \"\" \"\" \"\" \"\" \"\"\nmat output = output'\nmat list output, format(%4.3f)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\noutput[7,9]\n                   b      se       z  pvalue      ll      ul      df    crit   eform\n    ivw_beta   0.482   0.038  12.598   0.000   0.407   0.556       .   1.960   0.000\nmregger_beta   0.617   0.103   5.967   0.000   0.415   0.820       .   1.960   0.000\nmregger_cons  -0.009   0.005  -1.600   0.110  -0.020   0.002       .   1.960   0.000\n radial_beta   0.643   0.116   5.550   0.000   0.416   0.870       .   1.960   0.000\n radial_cons  -0.574   0.355  -1.618   0.106  -1.269   0.121       .   1.960   0.000\n   mode_beta   0.479   0.066   7.223   0.000   0.349   0.609       .   1.960   0.000\n median_beta   0.458   0.065   7.010   0.000   0.330   0.586       .   1.960   0.000\n```\n:::\n:::\n\n\nExport matrix to dataset\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\ndrop _all\nsvmat output, names(col)\nlocal rownames : rownames output\ndi \"`rownames'\"\ntokenize `rownames'\ngen str15 estimate = \"\"\nforvalues i = 1/7 {\n\treplace estimate = \"``i''\" in `i'\n}\n```\n:::\n\n\nShow dataset\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nlist estimate b se z pvalue ll ul, clean noobs\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n        estimate           b         se          z     pvalue          ll         ul  \n        ivw_beta    .4815055    .038221   12.59794   2.17e-36    .4065938   .5564172  \n    mregger_beta    .6173131   .1034573   5.966837   2.42e-09    .4145405   .8200858  \n    mregger_cons   -.0087707   .0054812   -1.60014   .1095675   -.0195136   .0019723  \n     radial_beta    .6425819   .1157871   5.549686   2.86e-08    .4156434   .8695205  \n     radial_cons   -.5737301   .3545658   -1.61812   .1056367   -1.268666   .1212062  \n       mode_beta    .4789702   .0663145   7.222712   5.10e-13    .3489963   .6089441  \n     median_beta    .4582573   .0653722   7.009973   2.38e-12    .3301302   .5863845  \n```\n:::\n:::\n\n\nSave dataset\n```stata\nsave myestimates, replace\n```\n\nExport as tab-delimited textfile\n```stata\nexport delimited using myestimates.txt, delimiter(tab) replace\n```\n",
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-    "markdown": "---\ntitle: \"Examples from our IJE paper\"\n#author: \"Wes Spiller, Neil Davies, Tom Palmer\"\n#date: \"30 August 2018\"\nengine: knitr\n---\n\n\n\n\n\n\n\n\nThe paper is available [here](https://doi.org/10.1093/ije/dyy195) [@spiller-ije-2018].\n\n## mrrobust set-up\nInstall the `mrrobust` package using the user-written `github` package.\n\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\nIf you have Stata 12 or earlier you will need to install some of these manually \n(see [here](https://remlapmot.github.io/mrrobust/#installing-and-updating-mrrobust) \nfor instructions).\n\n## Summary data description and overview\nAccompanying this paper are two sets of data `BMI.csv`, and `Height.csv`, containing the set of \nsummary estimates required for performing the BMI-serum glucose and height-serum glucose analyses \nrespectively. Each dataset is organised into 5 columns under the following headings:\n\n* `SNP`: A set of identifying numbers (rsids) for each genetic variant\n* `beta.exposure`: a set of values representing the coefficient from regressing the exposure upon \nthe genetic variant within a GWAS\n* `beta.outcome`: a set of values representing the coefficient from regressing the outcome upon \nthe genetic variant within a GWAS\n* `se.exposure`: a set of values representing the standard error corresponding to the coefficient \nin beta.exposure\n* `se.outcome`: a set of values representing the standard error corresponding to the coefficient \nin beta.outcome.\n\nNote Stata removes the `.` in the variable names when the data is imported.\n\nIn `BMI.csv` the exposure is standardised body mass index (BMI), and is therefore interpreted on a\nstandard deviation scale. The summary statistics are reported by @locke-nat-2015.\nIn `Height.csv` the exposure is standardised height in meters and also interpreted on a standard\ndeviation scale. The summary statistics are reported by @wood-natgen-2014.\n\nFor both analyses log transformed serum glucose was used as an outcome, reported by @shin-natgen-2014.\nAll the data was obtained from the MRBase GWAS catalogue available at <https://www.mrbase.org/> [@mrbase].\nGenetic variants were pruned so as to be independent ($R^2$ = 0.0001), and the effect alleles were \naligned between the exposure and outcome datasets using the MRBase web application, prior to \nimplementing `mrrobust`.\n\n## Stata output for each estimation method using mrrobust: BMI-Serum Glucose {#estimation}\n### Read in data\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nimport delimited using BMI.csv, clear\n```\n:::\n\n\n### IVW\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 79\n                                              Residual standard error =  1.039\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\nbetaexposure |   .0231866   .0079957     2.90   0.004     .0075154    .0388578\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n### MR-Egger\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 79\n                                              Residual standard error =  1.046\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\n       slope |   .0218507   .0221852     0.98   0.325    -.0216315    .0653329\n       _cons |    .000038   .0005877     0.06   0.948    -.0011138    .0011897\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n### Plot of the MR-Egger model\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-bmi.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Plot of the MR-Egger model for the BMI data.](mreggerplot-bmi.svg){fig-align='center' fig-alt='Plot of the MR-Egger model for the BMI data.'}\n:::\n:::\n\n\t\n### Weighted median\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0339256   .0120248     2.82   0.005     .0103576    .0574937\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n## Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose {#mode}\nUsing the `mrmodalplot` command, modal estimates are calculated using bandwidths of 0.25,\n0.5, and 1 respectively. This command also produces three overlaid density plots for each\nvalue, as shown in the Figure.\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-bmi.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0374507   .0424036     0.88   0.377    -.0456588    .1205602\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0416424   .0369758     1.13   0.260    -.0308289    .1141137\n------------------------------------------------------------------------------\n\n                                                      Number of genotypes = 79\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0431816   .0281684     1.53   0.125    -.0120274    .0983906\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Densities of the IV estimates using different values of phi.](mrmodalplot-bmi.svg){fig-align='center' fig-alt='Densities of the IV estimates using different values of phi.'}\n:::\n:::\n\n\n## Stata output for each estimation method using mrrobust: Height-Serum Glucose {#height}\n\n### Read in data\n\n::: {.cell layout-align=\"center\" collectcode='true'}\n\n```{.stata .cell-code}\nimport delimited using Height.csv, clear\n```\n:::\n\n\n### IVW\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                     Number of genotypes = 367\n                                              Residual standard error =  1.044\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\nbetaexposure |   .0015412   .0033017     0.47   0.641      -.00493    .0080124\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n### MR-Egger\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                     Number of genotypes = 367\n                                              Residual standard error =  1.045\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome  |\n       slope |  -.0025878   .0091178    -0.28   0.777    -.0204584    .0152828\n       _cons |   .0001338   .0002754     0.49   0.627     -.000406    .0006736\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n### Plot of the MR-Egger model\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-height.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Plot of the MR-Egger model for the Height data.](mreggerplot-height.svg){fig-align='center' fig-alt='Plot of the MR-Egger model for the Height data.'}\n:::\n:::\n\n\n### Weighted median\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |          0   .0052323     0.00   1.000    -.0102551    .0102551\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n\n## Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose {#height-mode}\n\n::: {.cell layout-align=\"center\"}\n\n```{.stata .cell-code}\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-height.svg, width(600) replace\n```\n\n\n::: {.cell-output .cell-output-stdout}\n```\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                     Phi = .25\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0061368   .0245472     0.25   0.803    -.0419748    .0542484\n------------------------------------------------------------------------------\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                      Phi = .5\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |   .0015595   .0212232     0.07   0.941    -.0400372    .0431561\n------------------------------------------------------------------------------\n\n                                                     Number of genotypes = 367\n                                                           Replications = 1000\n                                                                       Phi = 1\n------------------------------------------------------------------------------\n             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]\n-------------+----------------------------------------------------------------\n        beta |  -.0054772   .0149074    -0.37   0.713    -.0346952    .0237408\n------------------------------------------------------------------------------\n```\n:::\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Densities of the IV estimates using different values of phi.](mrmodalplot-height.svg){fig-align='center' fig-alt='Densities of the IV estimates using different values of phi.'}\n:::\n:::\n\n\n## References\n\n::: {#refs}\n:::\n",
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diff --git a/site/_freeze/index/execute-results/html.json b/site/_freeze/index/execute-results/html.json
deleted file mode 100644
index cef277ee..00000000
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-{
-  "hash": "791429dde30447654b1b042492b6e44b",
-  "result": {
-    "engine": "knitr",
-    "markdown": "---\n#title: \"mrrobust\"\n---\n\n\n# mrrobust: Stata package for two-sample Mendelian randomization analyses\n\n* [Latest updates](#latest-updates)\n* [Short video introduction](#short-video-introduction)\n* [Helpfile examples](#helpfile-examples)\n* [Overview](#overview)\n* [Installing and updating mrrobust](#installing-and-updating-mrrobust)\n* [Unit tests](#unit-tests)\n* [Authors](#authors)\n* [How to cite the mrrobust package](#how-to-cite-the-mrrobust-package)\n* [References](#references)\n* [Collaboration](#collaboration)\n* [Acknowledgements](#acknowledgements)\n\n## Latest updates\n\nTo obtain the latest update please see the instructions [below](#installing-and-updating-mrrobust).\n\n* January 2024:\n  - Reran certification scripts under Stata 18.0\n* August 2023:\n  - Added a record of the Stata version in the certification scripts\n* April 2023:\n  - Improved the alt text descriptions for the images in the *README* and package website, and also centred the images\n  - Remade the **mrrobust** website using [**Quarto**](https://quarto.org/)\n  - Updated the Sanderson et al., bioRxiv, 2020, [doi](https://doi.org/10.1101/2020.04.02.021980) reference to its published version Sanderson et al., Statistics in Medicine, 2021, [doi](https://doi.org/10.1002/sim.9133)\n  - Reran certification scripts under latest Stata 17.0\n* February 2023:\n  - Updated R Markdown example to use the CRAN version of the [**Statamarkdown**](https://cran.r-project.org/package=Statamarkdown) package\n* September 2022:\n  - Updated manual installation instructions\n* February 2022:\n  - Ran cscripts under Stata 17.0\n  - Updated website examples to run under Stata 17.0\n* September 2021:\n  - Changed relevant `http:` URLs to `https:`\n  - Minor edits to the helpfiles\n* June 2021:\n  - Published an interactive Code Ocean capsule demonstrating the use of the **mrrobust** package \n  [here](https://doi.org/10.24433/CO.0587524.v1)\n  - By default `mrforest` now specifies a fixed effect standard error for its IVW estimate \n* April 2021:\n  - Added the I-squared statistic and its 95% CI to the `mregger ..., heterogi` output\n* February 2021:\n  - Fixes to `mrforest` and `mrleaveoneout` related to the recent update to `metan`. \n  `mrforest` and `mrleaveoneout` now use `metan9` instead of `metan` because of the changes to \n  `metan` syntax. No change was necessary in the dependency scripts because `metan9` is also \n  installed with `ssc install metan`\n  - Checked cscripts pass\n  - Checked examples on website run. And changed the 2 examples which use the **TwoSampleMR** R\n  package to use the new ID code for the exposure data\n  - Updated `dependency.do` to make it more robust to the more frequent updates to `metan`\n  - Updated `mrdeps` to make it more robust to the more frequent updates to `metan`\n* October 2020:\n  - `dependency.do` and `mrdeps` now install the updated version of the `moremata` package\n  - Checked the cscripts run under Stata 16.1\n  - Added description of Q-statistic as Cochran's and Ruecker's for the IVW and MR-Egger models \n  respectively\n  - In various helpfiles added clarification that genotype-disease stands for SNP-outcome \n  (or indeed instrument-outcome) and that genotype-phenotype stands for SNP-exposure (or indeed \n  instrument-exposure) respectively; i.e. the estimates required for the top and bottom of the IV \n  Wald ratio estimate\n* August 2020:\n  - Added html versions of the helpfiles to the website. These are available from the *Helpfiles* website \n  menu bar item\n  - Added extra decimal places examples to helpfiles of `mrforest` and `mrleaveoneout`\n  - `mrfunnel` now includes a legend on its plot\n* July 2020:\n  - Added `gxse()` option to `mrmvivw` to return instrument strength Q<sub>A</sub> statistic for \n  instrument validity in `e(Qa)` ([Sanderson et al. 2019](#references))\n  - The `gxse()` option additionally returns the Q<sub>x</sub> and conditional F-statistics for \n  each phenotype for instrument strength in `e(Qx)` and `e(Fx)` \n  ([Sanderson et al. 2020](#references))\n  - Added `tdist` option to `mrmvivw` and `mrmvegger`\n  - `mrmvivw` and `mrmvegger` now ereturn the RMSE in `e(phi)`\n  - `mregger, ivw` now displays the square root of the residual variance (residual standard error) \n  and ereturns this is `e(phi)`\n  - Checked that examples on website still run\n  - Added `mrleaveoneout` command to perform leave one out analysis\n* June 2020:\n  - Simplified the outcome variable name in `mregger` `b` and `V` e-returned matrices. Turn this off \n  with new `oldnames` option\n  - Added basic multivariable MR-Egger command, `mrmvegger`\n  - Added basic multivariable IVW command, `mrmvivw` (currently command names `mvmr` and `mvivw` \n  also work)\n* February 2020:\n  - Updated contact details\n  - Minor edits to helpfiles, to show examples setting `seed()` option where helpful\n  - Fixed `mregger` bug where `r(table)` was not returned with the `gxse` or `heterogi` options. \n  The output for these options now appears before the coefficient table.\n  - Minor amendments to formatting of `mregger` `gxse` output\n  - `mregger` now ereturns `e(phi)`, the scale parameter, in some cases\n* January 2020:\n  - `mregger` now additionally returns `r(table)`\n  - Certification scripts: added `master.do` and renamed and edited a few scripts\n  - Added `mr` command. Commands may now be run as either `mr egger ...` or as previously \n  `mregger ...`.\n  - [*Best of IJE 2019*!](https://academic.oup.com/ije/pages/the_best_of_ije)\n  - `mrmedian`, `mrmedianobs`, `mreggersimex`, `mrmodal`, and `mrratio` now additionally return \n  the `r(table)` matrix (the information from the coefficient table)\n  - Added an example showing how you can save and export your estimates using `r(table)`, see \n  [here](https://remlapmot.github.io/mrrobust/examples/save-estimates/index.html)\n* December 2019:\n  - Added `Q_GX` to ereturn and display output when `gxse()` option specified to `mregger`\n  - Changed `Q_GX` and `I^2_GX` output to use first order weights in `mregger` output. This matches \n  the output from the `mr_egger()` function in the **MendelianRandomization** R package. Use the \n  `unwi2gx` option to report the unweighted statistics.\n* July 2019:\n  - Checked that examples on website still run\n* December 2018: \n  - Improved compatibility with the [**github**](https://haghish.github.io/github/) Stata package,\n  i.e., **mrrobust** and its dependencies can be installed simply by issuing: `gitget mrrobust`,\n  if you have the **github** package installed. \n  [See below for instructions](#2-use-the-github-package).\n  - `mrdeps` command added for conveniently installing dependencies\n* November 2018:\n  - Example showing the use of the **TwoSampleMR** R package and **mrrobust** in the same \n  [R Markdown script](https://rmarkdown.rstudio.com/) (`.Rmd` file) is \n  [here](https://remlapmot.github.io/mrrobust/examples/rmarkdown-call-stata-example/index.html)\n  - Example showing the use of the **TwoSampleMR** R package and **mrrobust** in the same \n  [Stata Markdown](https://data.princeton.edu/stata/markdown) script (`.stmd` file) is \n  [here](https://remlapmot.github.io/mrrobust/examples/markstat-call-R-example/index.html)\n* September 2018: \n  - IJE paper published online [here](https://doi.org/10.1093/ije/dyy195)\n* August 2018:\n  - Click [here](https://remlapmot.github.io/mrrobust/examples/spiller-ije-2018-examples/index.html) for the \n  example code and output from our IJE article\n* May 2018: \n  - Click [here](https://remlapmot.github.io/mrrobust/examples/mrrobust-examples/index.html) for code and output \n  from the examples in the helpfiles\n  - This page is now rendered on GitHub Pages [here](https://remlapmot.github.io/mrrobust/)\n* April 2018: \n  - `mregger` now has option `radial` which implements the radial formulation of the MR-Egger \n  model, and of the IVW model when used with option `ivw`\n\n## Short video introduction\nClick [here](https://drive.google.com/open?id=0B1owQlNgzNcPY0lMSGk0SnFfQWs) for a short video \ndemonstrating the use of the package.\n\n<p align=\"center\"><a href=\"https://drive.google.com/open?id=0B1owQlNgzNcPY0lMSGk0SnFfQWs\"><img src=\"./img/mrconf2017_video_mrforest_screenshot.png\" width=\"528\" height=\"300\" alt=\"A screenshot of a video demonstrating the use of the mrrobust package.\"></a></p>\n\n## Helpfile examples\nClick [here](https://remlapmot.github.io/mrrobust/examples/mrrobust-examples/index.html) for some of the code and \noutput from the examples in the helpfiles.\n\nOnce the package is installed, there is a summary helpfile which can be viewed in Stata with:\n```stata\nhelp mrrobust\n```\nThis has links to the helpfile for each command, which has an example near the bottom. In these \nexamples you can click on the code to run it.\n\n## Overview\nThe **mrrobust** package is a collection of commands for performing two-sample Mendelian randomization \nanalyses using summary data of genotype-phenotype and genotype-outcome associations. \n\nSuch data can be obtained from repositories such as MR-Base <https://www.mrbase.org> \n([Hemani et al. 2016](#references)).\n\nThe package contains the following commands:\n\n - `mrdeps` installs dependencies for the package\n - `mrratio` implements the standard instrumental variable ratio (Wald) estimate with a choice of \n standard errors/confidence intervals\n - `mrivests` automates calling `mrratio` on all the selected genotypes in your dataset\n - `mregger` implements the IVW and MR-Egger regression approaches introduced in \n [Bowden et al. (2015)](#references)\n - `mreggersimex` implements the simulation extrapolation algorithm for the MR-Egger model\n - `mreggerplot` implements a scatter plot with fitted line (either from IVW, MR-Egger, or weighted \n median estimators) and confidence interval\n - `mrmedian` and `mrmedianobs` implement the unweighted, weighted, and penalized weighted median \n IV estimators robust to 50% invalid instruments in [Bowden et al. (2016)](#references)\n - `mrmodal` implements the zero modal estimator of [Hartwig et al. (2017)](#references)\n - `mrmodalplot` plot of density used in modal estimator\n - `mrforest` implements a forest plot of genotype specific IV estimates and estimates from models \n (e.g. IVW and MR-Egger)\n - `mrfunnel` funnel plot of genotype specific IV estimates\n - `mr` acts as a primary command, e.g. so the other commands can be run as `mr egger ...` as well \n as `mregger ...`\n - `mrmvivw` (`mvmr`, `mvivw`) implements the multivariable IVW model\n - `mrmvegger` implements the multivariable MR-Egger model\n - `mrleaveoneout` implements leave one (genotype) out analysis\n\n## Installing and updating mrrobust\nTo install **mrrobust** in Stata versions 13 and later you have two choices.\n\n### 1. Use `net install`\n\n``` stata\nnet install mrrobust, from(\"https://raw.github.com/remlapmot/mrrobust/master/\") replace\nmrdeps\n```\nIn this code `mrdeps` installs the dependencies. These are `addplot`, `kdens`, and `moremata` \npackages (all by Ben Jann), the `heterogi` command (Orsini et al.), the `metan` command (Harris et \nal.), and the `grc1leg` command (Wiggins).\n\nIf you have previously installed the package and the `net install` command above fails with an error\nmessage that there are two copies of the package installed simply run `adoupdate`.\n\nTo check if there is an update available to any of your user-written Stata packages run `adoupdate`.\nTo update **mrrobust** run:\n``` stata\nadoupdate mrrobust, update\n```\n\nTo uninstall **mrrobust**, issue in Stata:\n``` stata\nado uninstall mrrobust\n```\nIf this fails with an error message mentioning that you have *\"multiple citations/instances of the \npackage installed\"* simply issue `adoupdate mrrobust`. This should leave you with the most recent \nversion of the package you previously installed. You can then run `ado uninstall mrrobust`.\n\n### 2. Use the `github` package\n\n``` stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\nThis automatically installs the dependencies.\n\nTo update the package issue:\n``` stata\ngithub update mrrobust\n```\n\nTo uninstall mrrobust issue:\n``` stata\ngithub uninstall mrrobust\n```\n\n### Installation instructions for Stata version 12 and earlier versions (and perhaps Stata version 13)\nThe `net install` syntax for installing `mrrobust` does not work under Stata version 12 and earlier \nbecause this webpage has an address starting with https rather than http. In such cases you need to \ndo a manual installation.\n\n#### To download and install mrrobust manually:\n\n* Click the green \"Clone or download\" button at the top of the GitHub repository \n[here](https://github.com/remlapmot/mrrobust) and download as a zip archive or click this direct [link](https://github.com/remlapmot/mrrobust/archive/refs/heads/master.zip).\n* In your file explorer extract the zip archive and find its filepath, e.g. `C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master`\n* In Stata run\n  ```stata\n  net install mrrobust, from(\"C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master\") replace\n  ```\n\nThe installation commands for the other dependencies should work. However, if you need to install \nthem manually their zip archives are available at the following links (extract the files from the downloaded zip archives and place them in your `PERSONAL` directory on your `adopath`): \n\n * the **moremata** package is available as a zip file \n [here](http://fmwww.bc.edu/repec/bocode/m/moremata.zip)\n * the `addplot` command is available [here](http://fmwww.bc.edu/repec/bocode/a/addplot.zip) \n * the `heterogi` command is available [here](https://ideas.repec.org/c/boc/bocode/s449201.html)\n * the `kdens` command is available [here](http://fmwww.bc.edu/repec/bocode/k/kdens.zip)\n * the `metan` command is available [here](https://ideas.repec.org/c/boc/bocode/s456798.html)\n * the `grc1leg` command can be installed with\n   ``` stata\n   net install grc1leg, from(\"https://www.stata.com/users/vwiggins\")\n   ```\n\n## Unit tests\nAs far as I know, and unlike R which has the **testthat** package (and other testing packages),\nthere is no recognised standard for writing unit tests for Stata commands.\nStataCorp. refer to do-files with tests as cscripts (certification scripts).\nI publish my cscripts (and their log files of output) in the\n[cscripts directory](https://github.com/remlapmot/mrrobust/tree/master/cscripts). \n\n## Authors\nTom Palmer <tom.palmer@bristol.ac.uk>, Wesley Spiller, Neil Davies\n\n## How to cite the mrrobust package\nSpiller W, Davies NM, Palmer TM. Software Application Profile: mrrobust &mdash; A tool for performing \ntwo-sample summary Mendelian randomization analyses. International Journal of Epidemiology, 2019, \n48, 3, 684&ndash;690. <https://doi.org/10.1093/ije/dyy195>.\n\nThank you to all our users who have cited **mrrobust**. We made \n[*The Best of IJE 2019*](https://academic.oup.com/ije/pages/the_best_of_ije)!\n\n## Collaboration\nIf you would like to extend the code or add new commands I am open to receiving pull requests on \nGitHub or send me an email to <tom.palmer@bristol.ac.uk>.\n\n## References\n\n<div class=\"hanging-indent\">\n\nBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect\nestimation and bias detection through Egger regression. International Journal of Epidemiology,\n2015, 44, 2, 512&ndash;525. <https://dx.doi.org/10.1093/ije/dyv080>.\n\nBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization\nwith some invalid instruments using a weighted median estimator. Genetic Epidemiology, 2016, 40, 4,\n304&ndash;314. <https://dx.doi.org/10.1002/gepi.21965>.\n\nHartwig FP, Davey Smith G, Bowden J. Robust inference in two-sample Mendelian randomisation via\nthe zero modal pleiotropy assumption. International Journal of Epidemiology, 2017, 46, 6,\n1985&ndash;1998. <https://doi.org/10.1093/ije/dyx102>.\n\nHemani G et al. The MR-Base platform supports systematic causal inference across the human\nphenome. eLife, 2018, 7:e34408. <https://doi.org/10.7554/eLife.34408.001>.\n\nSanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian\nrandomization in the single-sample and two-sample summary data settings. International Journal of\nEpidemiology, 2019, 48, 3, 713&ndash;727. <https://doi.org/10.1093/ije/dyy262>.\n\nSanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments in\ntwo-sample multivariable Mendelian randomization. Statistics in Medicine, 2021, 40, 25, 5434&ndash;5452.\n<https://doi.org/10.1002/sim.9133>.\n</div>\n\n## Acknowledgements\nThanks for helpful feedback and suggestions to (in no particular order): Jasmine Khouja, \nMichael Holmes, Caroline Dale, Amy Taylor, Rebecca Richmond, Judith Brand, Yanchun Bao, \nKawthar Al-Dabhani, Michalis Katsoulis, Ghazaleh Fatemifar, Lai-Te Chen, Sean Harrison,\nEmma Anderson, Cassianne Robinson-Cohen, Alisa Kjaergaard, and Steve Burgess.\n\n\n<!-- For generating the html versions of the helpfiles -->\n\n\n\n\n",
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diff --git a/site/examples/markstat-call-R-example/index.qmd b/site/examples/markstat-call-R-example/index.qmd
index a27886f3..3fdd3bd6 100644
--- a/site/examples/markstat-call-R-example/index.qmd
+++ b/site/examples/markstat-call-R-example/index.qmd
@@ -34,6 +34,7 @@ title: Using mrrobust in a Markstat document example
 ---
 
 We first need to register the R executable with Stata.
+
 ```s
 if c(os) == "Windows" local rpath "C:\\Program Files\\R\\R-4.4.1\\bin\\x64\\R.exe"
 else if c(os) == "Unix" local rpath "/usr/bin/R"
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-	<text x="4959.33" y="1270.05" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.20 (-0.10, 2.50)</text>
-	<text x="4959.33" y="1363.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.12 (-0.41, 2.65)</text>
-	<text x="4959.33" y="1456.08" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.04 (0.51, 1.57)</text>
-	<text x="4959.33" y="1549.23" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.02 (0.50, 1.53)</text>
-	<text x="4959.33" y="1642.38" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.92 (0.42, 1.41)</text>
-	<text x="4959.33" y="1735.53" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.90 (0.15, 1.64)</text>
-	<text x="4959.33" y="1828.41" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.88 (-0.05, 1.81)</text>
-	<text x="4959.33" y="1921.56" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.85 (-0.55, 2.24)</text>
-	<text x="4959.33" y="2014.71" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.82 (-0.05, 1.69)</text>
-	<text x="4959.33" y="2107.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.78 (-0.39, 1.94)</text>
-	<text x="4959.33" y="2200.74" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.76 (-0.19, 1.71)</text>
-	<text x="4959.33" y="2293.89" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.75 (0.35, 1.16)</text>
-	<text x="4959.33" y="2387.04" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.73 (-0.33, 1.79)</text>
-	<text x="4959.33" y="2480.19" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.71 (-0.54, 1.96)</text>
-	<text x="4959.33" y="2573.07" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.69 (-0.11, 1.50)</text>
-	<text x="4959.33" y="2666.22" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.67 (-0.39, 1.72)</text>
-	<text x="4959.33" y="2759.37" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.65 (-0.38, 1.69)</text>
-	<text x="4959.33" y="2852.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.05, 1.18)</text>
-	<text x="4959.33" y="2945.40" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.28, 0.90)</text>
-	<text x="4959.33" y="3038.55" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (-0.38, 1.56)</text>
-	<text x="4959.33" y="3131.70" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.37, 0.80)</text>
-	<text x="4959.33" y="3224.85" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.57 (-0.35, 1.49)</text>
-	<text x="4959.33" y="3317.73" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.50 (-0.51, 1.51)</text>
-	<text x="4959.33" y="3410.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.47 (-0.65, 1.58)</text>
-	<text x="4959.33" y="3504.03" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (0.19, 0.73)</text>
-	<text x="4959.33" y="3597.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (-0.17, 1.08)</text>
-	<text x="4959.33" y="3690.06" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (-0.20, 1.10)</text>
-	<text x="4959.33" y="3783.21" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (0.07, 0.84)</text>
-	<text x="4959.33" y="3876.36" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.44 (-0.35, 1.24)</text>
-	<text x="4959.33" y="3969.51" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.43 (-0.64, 1.50)</text>
-	<text x="4959.33" y="4062.39" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.52, 1.36)</text>
-	<text x="4959.33" y="4155.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.77, 1.61)</text>
-	<text x="4959.33" y="4248.69" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.39 (-0.10, 0.89)</text>
-	<text x="4959.33" y="4341.84" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.38 (-0.65, 1.40)</text>
-	<text x="4959.33" y="4434.72" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-0.45, 1.18)</text>
-	<text x="4959.33" y="4527.87" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-1.02, 1.73)</text>
-	<text x="4959.33" y="4621.02" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.35 (-0.63, 1.34)</text>
-	<text x="4959.33" y="4714.17" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.31 (-0.42, 1.05)</text>
-	<text x="4959.33" y="4807.05" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (0.04, 0.54)</text>
-	<text x="4959.33" y="4900.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.82, 1.40)</text>
-	<text x="4959.33" y="4993.35" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.04, 0.62)</text>
-	<text x="4959.33" y="5086.50" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.28 (-1.00, 1.55)</text>
-	<text x="4959.33" y="5179.38" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.23 (-0.25, 0.70)</text>
-	<text x="4959.33" y="5272.53" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.08 (-1.19, 1.36)</text>
-	<text x="4959.33" y="5365.68" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.04 (-0.52, 0.60)</text>
-	<text x="4959.33" y="5458.83" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.03 (-1.04, 1.11)</text>
-	<text x="4959.33" y="5551.71" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.00 (-1.60, 1.60)</text>
-	<text x="4959.33" y="5644.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.02 (-0.66, 0.62)</text>
-	<text x="4959.33" y="5738.01" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.12 (-1.05, 0.82)</text>
-	<text x="4959.33" y="5831.16" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.13 (-1.43, 1.18)</text>
-	<text x="4959.33" y="5924.04" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.16 (-1.02, 0.69)</text>
-	<text x="4959.33" y="6017.19" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.18 (-2.01, 1.66)</text>
-	<text x="4959.33" y="6110.34" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.22 (-1.19, 0.75)</text>
-	<text x="4959.33" y="6203.49" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.29, 0.78)</text>
-	<text x="4959.33" y="6296.37" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.24, 0.73)</text>
-	<text x="4959.33" y="6389.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.27 (-1.24, 0.71)</text>
-	<text x="4959.33" y="6482.67" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.44, 0.81)</text>
-	<text x="4959.33" y="6575.82" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.15, 0.50)</text>
-	<text x="4959.33" y="6668.70" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.33 (-1.58, 0.91)</text>
-	<text x="4959.33" y="6761.85" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.35 (-0.95, 0.26)</text>
-	<text x="4959.33" y="6855.00" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.43 (-1.68, 0.81)</text>
-	<text x="4959.33" y="6947.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.60 (-1.89, 0.69)</text>
-	<text x="4959.33" y="7041.03" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.15 (-2.33, 0.04)</text>
-	<text x="4959.33" y="7134.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.42 (-3.01, 0.17)</text>
-	<text x="4959.33" y="7227.33" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.96 (-3.36, -0.57)</text>
-	<text x="4959.33" y="7320.21" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-3.35 (-5.11, -1.59)</text>
-	<text x="4959.33" y="7599.66" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.48 (0.41, 0.56)</text>
-	<text x="4959.33" y="7692.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.41, 0.82)</text>
-	<text x="4959.33" y="339.09" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">Estimate (95% CI)</text>
-	<text x="4959.33" y="618.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.84 (0.98, 2.71)</text>
-	<text x="4959.33" y="711.42" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.83 (0.51, 3.16)</text>
-	<text x="4959.33" y="804.57" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.71 (0.94, 2.48)</text>
-	<text x="4959.33" y="897.72" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.46 (0.89, 2.03)</text>
-	<text x="4959.33" y="990.87" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.38 (0.36, 2.39)</text>
-	<text x="4959.33" y="1083.75" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.32 (0.18, 2.46)</text>
-	<text x="4959.33" y="1176.90" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.27 (0.45, 2.09)</text>
-	<text x="4959.33" y="1270.05" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.20 (-0.10, 2.50)</text>
-	<text x="4959.33" y="1363.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.12 (-0.41, 2.65)</text>
-	<text x="4959.33" y="1456.08" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.04 (0.51, 1.57)</text>
-	<text x="4959.33" y="1549.23" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.02 (0.50, 1.53)</text>
-	<text x="4959.33" y="1642.38" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.92 (0.42, 1.41)</text>
-	<text x="4959.33" y="1735.53" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.90 (0.15, 1.64)</text>
-	<text x="4959.33" y="1828.41" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.88 (-0.05, 1.81)</text>
-	<text x="4959.33" y="1921.56" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.85 (-0.55, 2.24)</text>
-	<text x="4959.33" y="2014.71" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.82 (-0.05, 1.69)</text>
-	<text x="4959.33" y="2107.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.78 (-0.39, 1.94)</text>
-	<text x="4959.33" y="2200.74" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.76 (-0.19, 1.71)</text>
-	<text x="4959.33" y="2293.89" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.75 (0.35, 1.16)</text>
-	<text x="4959.33" y="2387.04" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.73 (-0.33, 1.79)</text>
-	<text x="4959.33" y="2480.19" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.71 (-0.54, 1.96)</text>
-	<text x="4959.33" y="2573.07" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.69 (-0.11, 1.50)</text>
-	<text x="4959.33" y="2666.22" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.67 (-0.39, 1.72)</text>
-	<text x="4959.33" y="2759.37" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.65 (-0.38, 1.69)</text>
-	<text x="4959.33" y="2852.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.05, 1.18)</text>
-	<text x="4959.33" y="2945.40" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.28, 0.90)</text>
-	<text x="4959.33" y="3038.55" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (-0.38, 1.56)</text>
-	<text x="4959.33" y="3131.70" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.37, 0.80)</text>
-	<text x="4959.33" y="3224.85" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.57 (-0.35, 1.49)</text>
-	<text x="4959.33" y="3317.73" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.50 (-0.51, 1.51)</text>
-	<text x="4959.33" y="3410.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.47 (-0.65, 1.58)</text>
-	<text x="4959.33" y="3504.03" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (0.19, 0.73)</text>
-	<text x="4959.33" y="3597.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (-0.17, 1.08)</text>
-	<text x="4959.33" y="3690.06" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (-0.20, 1.10)</text>
-	<text x="4959.33" y="3783.21" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (0.07, 0.84)</text>
-	<text x="4959.33" y="3876.36" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.44 (-0.35, 1.24)</text>
-	<text x="4959.33" y="3969.51" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.43 (-0.64, 1.50)</text>
-	<text x="4959.33" y="4062.39" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.52, 1.36)</text>
-	<text x="4959.33" y="4155.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.77, 1.61)</text>
-	<text x="4959.33" y="4248.69" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.39 (-0.10, 0.89)</text>
-	<text x="4959.33" y="4341.84" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.38 (-0.65, 1.40)</text>
-	<text x="4959.33" y="4434.72" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-0.45, 1.18)</text>
-	<text x="4959.33" y="4527.87" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-1.02, 1.73)</text>
-	<text x="4959.33" y="4621.02" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.35 (-0.63, 1.34)</text>
-	<text x="4959.33" y="4714.17" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.31 (-0.42, 1.05)</text>
-	<text x="4959.33" y="4807.05" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (0.04, 0.54)</text>
-	<text x="4959.33" y="4900.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.82, 1.40)</text>
-	<text x="4959.33" y="4993.35" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.04, 0.62)</text>
-	<text x="4959.33" y="5086.50" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.28 (-1.00, 1.55)</text>
-	<text x="4959.33" y="5179.38" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.23 (-0.25, 0.70)</text>
-	<text x="4959.33" y="5272.53" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.08 (-1.19, 1.36)</text>
-	<text x="4959.33" y="5365.68" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.04 (-0.52, 0.60)</text>
-	<text x="4959.33" y="5458.83" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.03 (-1.04, 1.11)</text>
-	<text x="4959.33" y="5551.71" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.00 (-1.60, 1.60)</text>
-	<text x="4959.33" y="5644.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.02 (-0.66, 0.62)</text>
-	<text x="4959.33" y="5738.01" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.12 (-1.05, 0.82)</text>
-	<text x="4959.33" y="5831.16" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.13 (-1.43, 1.18)</text>
-	<text x="4959.33" y="5924.04" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.16 (-1.02, 0.69)</text>
-	<text x="4959.33" y="6017.19" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.18 (-2.01, 1.66)</text>
-	<text x="4959.33" y="6110.34" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.22 (-1.19, 0.75)</text>
-	<text x="4959.33" y="6203.49" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.29, 0.78)</text>
-	<text x="4959.33" y="6296.37" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.24, 0.73)</text>
-	<text x="4959.33" y="6389.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.27 (-1.24, 0.71)</text>
-	<text x="4959.33" y="6482.67" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.44, 0.81)</text>
-	<text x="4959.33" y="6575.82" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.15, 0.50)</text>
-	<text x="4959.33" y="6668.70" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.33 (-1.58, 0.91)</text>
-	<text x="4959.33" y="6761.85" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.35 (-0.95, 0.26)</text>
-	<text x="4959.33" y="6855.00" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.43 (-1.68, 0.81)</text>
-	<text x="4959.33" y="6947.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.60 (-1.89, 0.69)</text>
-	<text x="4959.33" y="7041.03" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.15 (-2.33, 0.04)</text>
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+	<text x="508.16" y="7168.74" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">rs6489818</text>
+	<text x="508.16" y="7261.08" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">rs653178</text>
+	<text x="508.16" y="7445.76" style="font-family:'Helvetica';font-weight:bold;font-size:80.73px;fill:#000000">Summary</text>
+	<text x="508.16" y="7538.10" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">IVW</text>
+	<text x="508.16" y="7630.17" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">MR-Egger</text>
+	<text x="4962.03" y="615.30" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.84 (0.98, 2.71)</text>
+	<text x="4962.03" y="707.64" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.83 (0.51, 3.16)</text>
+	<text x="4962.03" y="799.98" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.71 (0.94, 2.48)</text>
+	<text x="4962.03" y="892.32" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.46 (0.89, 2.03)</text>
+	<text x="4962.03" y="984.66" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.38 (0.36, 2.39)</text>
+	<text x="4962.03" y="1076.73" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.32 (0.18, 2.46)</text>
+	<text x="4962.03" y="1169.07" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.27 (0.45, 2.09)</text>
+	<text x="4962.03" y="1261.41" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.20 (-0.10, 2.50)</text>
+	<text x="4962.03" y="1353.75" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.12 (-0.41, 2.65)</text>
+	<text x="4962.03" y="1446.09" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.04 (0.51, 1.57)</text>
+	<text x="4962.03" y="1538.43" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.02 (0.50, 1.53)</text>
+	<text x="4962.03" y="1630.77" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.92 (0.42, 1.41)</text>
+	<text x="4962.03" y="1722.84" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.90 (0.15, 1.64)</text>
+	<text x="4962.03" y="1815.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.88 (-0.05, 1.81)</text>
+	<text x="4962.03" y="1907.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.85 (-0.55, 2.24)</text>
+	<text x="4962.03" y="1999.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.82 (-0.05, 1.69)</text>
+	<text x="4962.03" y="2092.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.78 (-0.39, 1.94)</text>
+	<text x="4962.03" y="2184.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.76 (-0.19, 1.71)</text>
+	<text x="4962.03" y="2276.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.75 (0.35, 1.16)</text>
+	<text x="4962.03" y="2368.95" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.73 (-0.33, 1.79)</text>
+	<text x="4962.03" y="2461.29" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.71 (-0.54, 1.96)</text>
+	<text x="4962.03" y="2553.63" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.69 (-0.11, 1.50)</text>
+	<text x="4962.03" y="2645.97" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.67 (-0.39, 1.72)</text>
+	<text x="4962.03" y="2738.31" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.65 (-0.38, 1.69)</text>
+	<text x="4962.03" y="2830.65" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.05, 1.18)</text>
+	<text x="4962.03" y="2922.99" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (-0.38, 1.56)</text>
+	<text x="4962.03" y="3015.06" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.28, 0.90)</text>
+	<text x="4962.03" y="3107.40" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.37, 0.80)</text>
+	<text x="4962.03" y="3199.74" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.57 (-0.35, 1.49)</text>
+	<text x="4962.03" y="3292.08" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.50 (-0.51, 1.51)</text>
+	<text x="4962.03" y="3384.42" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.47 (-0.65, 1.58)</text>
+	<text x="4962.03" y="3476.76" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (0.19, 0.73)</text>
+	<text x="4962.03" y="3569.10" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (-0.17, 1.08)</text>
+	<text x="4962.03" y="3661.44" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (-0.20, 1.10)</text>
+	<text x="4962.03" y="3753.51" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (0.07, 0.84)</text>
+	<text x="4962.03" y="3845.85" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.44 (-0.35, 1.24)</text>
+	<text x="4962.03" y="3938.19" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.43 (-0.64, 1.50)</text>
+	<text x="4962.03" y="4030.53" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.52, 1.36)</text>
+	<text x="4962.03" y="4122.87" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.77, 1.61)</text>
+	<text x="4962.03" y="4215.21" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.39 (-0.10, 0.89)</text>
+	<text x="4962.03" y="4307.55" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.38 (-0.65, 1.40)</text>
+	<text x="4962.03" y="4399.62" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-0.45, 1.18)</text>
+	<text x="4962.03" y="4491.96" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-1.02, 1.73)</text>
+	<text x="4962.03" y="4584.30" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.35 (-0.63, 1.34)</text>
+	<text x="4962.03" y="4676.64" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.31 (-0.42, 1.05)</text>
+	<text x="4962.03" y="4768.98" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (0.04, 0.54)</text>
+	<text x="4962.03" y="4861.32" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.82, 1.40)</text>
+	<text x="4962.03" y="4953.66" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.04, 0.62)</text>
+	<text x="4962.03" y="5045.73" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.28 (-1.00, 1.55)</text>
+	<text x="4962.03" y="5138.07" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.23 (-0.25, 0.70)</text>
+	<text x="4962.03" y="5230.41" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.08 (-1.19, 1.36)</text>
+	<text x="4962.03" y="5322.75" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.04 (-0.52, 0.60)</text>
+	<text x="4962.03" y="5415.09" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.03 (-1.04, 1.11)</text>
+	<text x="4962.03" y="5507.43" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.00 (-1.60, 1.60)</text>
+	<text x="4962.03" y="5599.77" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.02 (-0.66, 0.62)</text>
+	<text x="4962.03" y="5691.84" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.12 (-1.05, 0.82)</text>
+	<text x="4962.03" y="5784.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.13 (-1.43, 1.18)</text>
+	<text x="4962.03" y="5876.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.16 (-1.02, 0.69)</text>
+	<text x="4962.03" y="5968.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.18 (-2.01, 1.66)</text>
+	<text x="4962.03" y="6061.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.22 (-1.19, 0.75)</text>
+	<text x="4962.03" y="6153.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.29, 0.78)</text>
+	<text x="4962.03" y="6245.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.25 (-1.24, 0.73)</text>
+	<text x="4962.03" y="6337.95" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.27 (-1.24, 0.71)</text>
+	<text x="4962.03" y="6430.29" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.44, 0.81)</text>
+	<text x="4962.03" y="6522.63" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.32 (-1.15, 0.50)</text>
+	<text x="4962.03" y="6614.97" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.33 (-1.58, 0.91)</text>
+	<text x="4962.03" y="6707.31" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.35 (-0.95, 0.26)</text>
+	<text x="4962.03" y="6799.65" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.43 (-1.68, 0.81)</text>
+	<text x="4962.03" y="6891.99" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.60 (-1.89, 0.69)</text>
+	<text x="4962.03" y="6984.06" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.15 (-2.33, 0.04)</text>
+	<text x="4962.03" y="7076.40" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.42 (-3.01, 0.17)</text>
+	<text x="4962.03" y="7168.74" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-1.96 (-3.36, -0.57)</text>
+	<text x="4962.03" y="7261.08" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-3.35 (-5.11, -1.59)</text>
+	<text x="4962.03" y="7538.10" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.48 (0.41, 0.56)</text>
+	<text x="4962.03" y="7630.17" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.41, 0.82)</text>
+	<text x="4962.03" y="338.55" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">Estimate (95% CI)</text>
+	<text x="4962.03" y="615.30" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.84 (0.98, 2.71)</text>
+	<text x="4962.03" y="707.64" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.83 (0.51, 3.16)</text>
+	<text x="4962.03" y="799.98" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.71 (0.94, 2.48)</text>
+	<text x="4962.03" y="892.32" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.46 (0.89, 2.03)</text>
+	<text x="4962.03" y="984.66" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.38 (0.36, 2.39)</text>
+	<text x="4962.03" y="1076.73" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.32 (0.18, 2.46)</text>
+	<text x="4962.03" y="1169.07" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.27 (0.45, 2.09)</text>
+	<text x="4962.03" y="1261.41" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.20 (-0.10, 2.50)</text>
+	<text x="4962.03" y="1353.75" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.12 (-0.41, 2.65)</text>
+	<text x="4962.03" y="1446.09" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.04 (0.51, 1.57)</text>
+	<text x="4962.03" y="1538.43" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">1.02 (0.50, 1.53)</text>
+	<text x="4962.03" y="1630.77" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.92 (0.42, 1.41)</text>
+	<text x="4962.03" y="1722.84" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.90 (0.15, 1.64)</text>
+	<text x="4962.03" y="1815.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.88 (-0.05, 1.81)</text>
+	<text x="4962.03" y="1907.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.85 (-0.55, 2.24)</text>
+	<text x="4962.03" y="1999.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.82 (-0.05, 1.69)</text>
+	<text x="4962.03" y="2092.20" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.78 (-0.39, 1.94)</text>
+	<text x="4962.03" y="2184.54" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.76 (-0.19, 1.71)</text>
+	<text x="4962.03" y="2276.88" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.75 (0.35, 1.16)</text>
+	<text x="4962.03" y="2368.95" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.73 (-0.33, 1.79)</text>
+	<text x="4962.03" y="2461.29" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.71 (-0.54, 1.96)</text>
+	<text x="4962.03" y="2553.63" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.69 (-0.11, 1.50)</text>
+	<text x="4962.03" y="2645.97" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.67 (-0.39, 1.72)</text>
+	<text x="4962.03" y="2738.31" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.65 (-0.38, 1.69)</text>
+	<text x="4962.03" y="2830.65" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.62 (0.05, 1.18)</text>
+	<text x="4962.03" y="2922.99" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (-0.38, 1.56)</text>
+	<text x="4962.03" y="3015.06" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.28, 0.90)</text>
+	<text x="4962.03" y="3107.40" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.59 (0.37, 0.80)</text>
+	<text x="4962.03" y="3199.74" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.57 (-0.35, 1.49)</text>
+	<text x="4962.03" y="3292.08" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.50 (-0.51, 1.51)</text>
+	<text x="4962.03" y="3384.42" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.47 (-0.65, 1.58)</text>
+	<text x="4962.03" y="3476.76" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (0.19, 0.73)</text>
+	<text x="4962.03" y="3569.10" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.46 (-0.17, 1.08)</text>
+	<text x="4962.03" y="3661.44" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (-0.20, 1.10)</text>
+	<text x="4962.03" y="3753.51" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.45 (0.07, 0.84)</text>
+	<text x="4962.03" y="3845.85" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.44 (-0.35, 1.24)</text>
+	<text x="4962.03" y="3938.19" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.43 (-0.64, 1.50)</text>
+	<text x="4962.03" y="4030.53" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.52, 1.36)</text>
+	<text x="4962.03" y="4122.87" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.42 (-0.77, 1.61)</text>
+	<text x="4962.03" y="4215.21" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.39 (-0.10, 0.89)</text>
+	<text x="4962.03" y="4307.55" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.38 (-0.65, 1.40)</text>
+	<text x="4962.03" y="4399.62" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-0.45, 1.18)</text>
+	<text x="4962.03" y="4491.96" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.36 (-1.02, 1.73)</text>
+	<text x="4962.03" y="4584.30" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.35 (-0.63, 1.34)</text>
+	<text x="4962.03" y="4676.64" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.31 (-0.42, 1.05)</text>
+	<text x="4962.03" y="4768.98" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (0.04, 0.54)</text>
+	<text x="4962.03" y="4861.32" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.82, 1.40)</text>
+	<text x="4962.03" y="4953.66" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.29 (-0.04, 0.62)</text>
+	<text x="4962.03" y="5045.73" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.28 (-1.00, 1.55)</text>
+	<text x="4962.03" y="5138.07" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.23 (-0.25, 0.70)</text>
+	<text x="4962.03" y="5230.41" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.08 (-1.19, 1.36)</text>
+	<text x="4962.03" y="5322.75" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.04 (-0.52, 0.60)</text>
+	<text x="4962.03" y="5415.09" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.03 (-1.04, 1.11)</text>
+	<text x="4962.03" y="5507.43" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">0.00 (-1.60, 1.60)</text>
+	<text x="4962.03" y="5599.77" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.02 (-0.66, 0.62)</text>
+	<text x="4962.03" y="5691.84" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.12 (-1.05, 0.82)</text>
+	<text x="4962.03" y="5784.18" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.13 (-1.43, 1.18)</text>
+	<text x="4962.03" y="5876.52" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.16 (-1.02, 0.69)</text>
+	<text x="4962.03" y="5968.86" style="font-family:'Helvetica';font-size:80.73px;fill:#000000">-0.18 (-2.01, 1.66)</text>
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+	<line x1="5075.45" y1="2289.87" x2="5614.37" y2="2289.87" style="stroke:#000000;stroke-width:24.73"/>
+	<line x1="5007.95" y1="2540.16" x2="5541.74" y2="2540.16" style="stroke:#000000;stroke-width:24.73"/>
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+	<text x="575.10" y="3615.88" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs4240624</text>
+	<text x="575.10" y="3866.17" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs2954022</text>
+	<text x="575.10" y="4116.19" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs579459</text>
+	<text x="575.10" y="4366.48" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs1535</text>
+	<text x="575.10" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs10790162</text>
+	<text x="575.10" y="4866.79" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs2000999</text>
+	<text x="575.10" y="5116.81" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs6511720</text>
+	<text x="575.10" y="5367.10" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs688</text>
+	<text x="575.10" y="5617.12" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs10401969</text>
+	<text x="575.10" y="5867.41" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs6859</text>
+	<text x="575.10" y="6117.70" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs7254892</text>
+	<text x="575.10" y="6367.72" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rs6065311</text>
+	<text x="575.10" y="6868.03" style="font-family:'Helvetica';font-weight:bold;font-size:214.38px;fill:#000000">All genotypes</text>
+	<text x="575.10" y="7118.32" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">IVW</text>
+	<text x="575.10" y="613.75" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">rsid</text>
+	<text x="5714.54" y="1364.35" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.62)</text>
+	<text x="5714.54" y="1614.64" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
+	<text x="5714.54" y="1864.66" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.41, 0.61)</text>
+	<text x="5714.54" y="2114.95" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.45, 0.62)</text>
+	<text x="5714.54" y="2364.97" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.56 (0.46, 0.65)</text>
+	<text x="5714.54" y="2615.26" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.63)</text>
+	<text x="5714.54" y="2865.28" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="3115.57" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.62)</text>
+	<text x="5714.54" y="3365.86" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.61)</text>
+	<text x="5714.54" y="3615.88" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.62)</text>
+	<text x="5714.54" y="3866.17" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="4116.19" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.50 (0.41, 0.59)</text>
+	<text x="5714.54" y="4366.48" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
+	<text x="5714.54" y="4616.50" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.59)</text>
+	<text x="5714.54" y="4866.79" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.43, 0.61)</text>
+	<text x="5714.54" y="5116.81" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.43, 0.61)</text>
+	<text x="5714.54" y="5367.10" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="5617.12" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
+	<text x="5714.54" y="5867.41" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.45, 0.62)</text>
+	<text x="5714.54" y="6117.70" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
+	<text x="5714.54" y="6367.72" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.61)</text>
+	<text x="5714.54" y="7118.32" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.52 (0.44, 0.61)</text>
+	<text x="5714.54" y="613.75" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">Estimate (95% CI)</text>
+	<text x="5714.54" y="1364.35" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.62)</text>
+	<text x="5714.54" y="1614.64" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.54 (0.45, 0.63)</text>
+	<text x="5714.54" y="1864.66" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.41, 0.61)</text>
+	<text x="5714.54" y="2114.95" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.45, 0.62)</text>
+	<text x="5714.54" y="2364.97" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.56 (0.46, 0.65)</text>
+	<text x="5714.54" y="2615.26" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.53 (0.44, 0.63)</text>
+	<text x="5714.54" y="2865.28" style="font-family:'Helvetica';font-size:214.38px;fill:#000000">0.51 (0.42, 0.60)</text>
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diff --git a/site/examples/rmarkdown-call-stata-example/index.qmd b/site/examples/rmarkdown-call-stata-example/index.qmd
index 6586db8c..6d0a0e7d 100644
--- a/site/examples/rmarkdown-call-stata-example/index.qmd
+++ b/site/examples/rmarkdown-call-stata-example/index.qmd
@@ -6,11 +6,10 @@ engine: knitr
 
 ## Introduction
 
-This example shows how to run R and Stata code within the same R Markdown (`.Rmd`) file using the **Statamarkdown** R package. More information about this package is available [here](https://github.com/Hemken/Statamarkdown) and [here](https://www.ssc.wisc.edu/~hemken/Stataworkshops/Statamarkdown/stata-and-r-markdown.html). To install this package and load it into your current R session run the following in R.
+This example shows how to run R and Stata code within the same R Markdown (_.Rmd_) or Quarto (_.qmd_) file using the **Statamarkdown** R package. More information about this package is available [here](https://github.com/Hemken/Statamarkdown) and [here](https://www.ssc.wisc.edu/~hemken/Stataworkshops/Statamarkdown/stata-and-r-markdown.html). To install this package run the following in R.
 
 ```r
-# install.packages("Statamarkdown") # uncomment on first run
-library(Statamarkdown)
+install.packages("Statamarkdown")
 ```
 
 Note when writing our Stata code chunks we need to be careful when we specify the `collectcode=TRUE` code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.
@@ -34,7 +33,7 @@ install.packages("foreign")
 
 ## Example R Markdown or Quarto document
 
-Save the following code chunk as an _.Rmd_ or _.qmd_ file.
+Save the following code chunk as an _.Rmd_ or _.qmd_ file, e.g. _myanalysis.Rmd_.
 
 ````{verbatim, lang='markdown'}
 ---
@@ -56,6 +55,7 @@ which we will use to save the data in Stata format.
 library(TwoSampleMR)
 library(MRInstruments)
 library(foreign)
+library(Statamarkdown)
 ```
 
 We can access the data using the **MRInstruments** package.
diff --git a/site/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg b/site/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg
index 66c3dbf0..df539194 100644
--- a/site/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg
+++ b/site/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg
@@ -1,461 +1,621 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!-- This is a Stata 17.0 generated SVG file (http://www.stata.com) -->
+<!-- This is a Stata 18.5 generated SVG file (http://www.stata.com) -->
 
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