diff --git a/dev/articles/bpbounds.html b/dev/articles/bpbounds.html
index 7ff2bc9..0d17af5 100644
--- a/dev/articles/bpbounds.html
+++ b/dev/articles/bpbounds.html
@@ -92,7 +92,7 @@ <h4 data-toc-skip class="author">Vanessa
 Bremen<br><h4 data-toc-skip class="author">Nuala
 Sheehan</h4>
             <address class="author_afil">
-      University of Leicester<br><h4 data-toc-skip class="date">2023-12-20</h4>
+      University of Leicester<br><h4 data-toc-skip class="date">2024-01-11</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/remlapmot/bpbounds/blob/HEAD/vignettes/bpbounds.Rmd" class="external-link"><code>vignettes/bpbounds.Rmd</code></a></small>
       <div class="d-none name"><code>bpbounds.Rmd</code></div>
diff --git a/dev/news/index.html b/dev/news/index.html
index 11d6ed9..3172cec 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -57,6 +57,7 @@
 <h2 class="pkg-version" data-toc-text="0.1.5.9000" id="bpbounds-0159000">bpbounds 0.1.5.9000<a class="anchor" aria-label="anchor" href="#bpbounds-0159000"></a></h2>
 <ul><li><p>Tweak formatting of code in helpfile examples and vignette</p></li>
 <li><p>Bumped minimum required version of R to be at least 3.6.0 because this is now required by <strong>testthat</strong> version 3.2.0</p></li>
+<li><p>Bumped version of roxygen2 used to create package documentation</p></li>
 </ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.1.5" id="bpbounds-015">bpbounds 0.1.5<a class="anchor" aria-label="anchor" href="#bpbounds-015"></a></h2><p class="text-muted">CRAN release: 2023-05-03</p>
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index b5e7076..3ddb350 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   bpbounds: bpbounds.html
-last_built: 2023-12-20T10:53Z
+last_built: 2024-01-11T14:54Z
 urls:
   reference: https://remlapmot.github.io/bpbounds/reference
   article: https://remlapmot.github.io/bpbounds/articles
diff --git a/dev/reference/bpbounds-package.html b/dev/reference/bpbounds-package.html
index 686c681..3477582 100644
--- a/dev/reference/bpbounds-package.html
+++ b/dev/reference/bpbounds-package.html
@@ -1,9 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This package implements the nonparametric bounds for the average causal effect defined by
-Balke and Pearl, Bounds on Treatment Effects from Studies with Imperfect Compliance,
-JASA, 1997; and some extensions."><title>bpbounds: Nonparametric bounds for the Average Causal Effect due to Balke and Pearl and extensions — bpbounds-package • bpbounds</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="bpbounds: Nonparametric bounds for the Average Causal Effect due to Balke and Pearl and extensions — bpbounds-package"><meta property="og:description" content="This package implements the nonparametric bounds for the average causal effect defined by
-Balke and Pearl, Bounds on Treatment Effects from Studies with Imperfect Compliance,
-JASA, 1997; and some extensions."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data)."><title>bpbounds: Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions — bpbounds-package • bpbounds</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="bpbounds: Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions — bpbounds-package"><meta property="og:description" content="Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=UA-156933160-2"></script><script>
@@ -53,30 +49,33 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>bpbounds: Nonparametric bounds for the Average Causal Effect due to Balke and Pearl and extensions</h1>
+      <img src="" class="logo" alt=""><h1>bpbounds: Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions</h1>
       <small class="dont-index">Source: <a href="https://github.com/remlapmot/bpbounds/blob/HEAD/R/bpbounds-package.r" class="external-link"><code>R/bpbounds-package.r</code></a></small>
       <div class="d-none name"><code>bpbounds-package.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>This package implements the nonparametric bounds for the average causal effect defined by
-Balke and Pearl, Bounds on Treatment Effects from Studies with Imperfect Compliance,
-JASA, 1997; and some extensions.</p>
+    <p>Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data).</p>
     </div>
 
 
     <div class="section level2">
-    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>The functions implement bounds for the situation where each of the outcome,
-treatment/phenotype, and instrumental variable are binary; and additionally for when the
-instrument has 3 categories (e.g. a single genotype under an additive model in a
-Mendelian randomization study).</p>
-<p>The package implements bounds for when the three variables are measured in the same study
-(trivariate data) and when the outcome and instrument are measured in one study and the
-treatment/phenotype and instrument in another sample (bivariate/two sample data).</p>
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Useful links:</p><ul><li><p><a href="https://github.com/remlapmot/bpbounds" class="external-link">https://github.com/remlapmot/bpbounds</a></p></li>
+<li><p><a href="https://remlapmot.github.io/bpbounds/">https://remlapmot.github.io/bpbounds/</a></p></li>
+<li><p>Report bugs at <a href="https://github.com/remlapmot/bpbounds/issues" class="external-link">https://github.com/remlapmot/bpbounds/issues</a></p></li>
+</ul></div>
     </div>
-
-  </main></div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p><strong>Maintainer</strong>: Tom Palmer <a href="mailto:remlapmot@hotmail.com">remlapmot@hotmail.com</a> (<a href="https://orcid.org/0000-0003-4655-4511" class="external-link">ORCID</a>)</p>
+<p>Authors:</p><ul><li><p>Roland Ramsahai (<a href="https://orcid.org/0000-0002-7349-1977" class="external-link">ORCID</a>)</p></li>
+<li><p>Vanessa Didelez (<a href="https://orcid.org/0000-0001-8587-7706" class="external-link">ORCID</a>)</p></li>
+<li><p>Nuala Sheehan (<a href="https://orcid.org/0000-0001-9094-2605" class="external-link">ORCID</a>)</p></li>
+</ul></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
diff --git a/dev/reference/index.html b/dev/reference/index.html
index cf5b2bc..6ea0fa7 100644
--- a/dev/reference/index.html
+++ b/dev/reference/index.html
@@ -65,11 +65,6 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 
       <dl><dt>
           
-          <code><a href="bpbounds-package.html">bpbounds-package</a></code> 
-        </dt>
-        <dd>bpbounds: Nonparametric bounds for the Average Causal Effect due to Balke and Pearl and extensions</dd>
-      </dl><dl><dt>
-          
           <code><a href="bpbounds.html">bpbounds()</a></code> 
         </dt>
         <dd>Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl.</dd>
diff --git a/dev/search.json b/dev/search.json
index e730c41..b9d8ccc 100644
--- a/dev/search.json
+++ b/dev/search.json
@@ -1 +1 @@
-[{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"short vignette demonstrates use bpbounds package. R implementation nonparametric bounds average causal effect Balke Pearl (1997) extensions. Currently R package port bpbounds Stata package (Palmer et al. (2011)). code implements approach calculating bounds outlined Ramsahai (2007), Ramsahai (2008), Ramsahai (2012). start loading package others needed code vignette.","code":"library(bpbounds) library(dplyr) #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union library(tidyr)"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"features-of-the-bpbounds-package","dir":"Articles","previous_headings":"","what":"Features of the bpbounds package","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"Currently package one function, bpbounds(), can accommodate four scenarios implemented Stata command. calculate bounds binary outcome, binary treatment/phenotype, : binary instrumental variable; 3 category instrumental variable, e.g. genotype Mendelian randomization; Bounds scenarios can calculated either trivariate data (three variables measured one dataset); bivariate data (treatment/phenotype instrument measured one dataset outcome instrument measured another sample).","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"vitamin-a-supplementation-example","dir":"Articles","previous_headings":"","what":"Vitamin A supplementation example","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"example taken Table 1 Balke Pearl (1997). study Vitamin supplementation consisting children 450 villages. , 221 villages assigned control 229 treatment. outcome mortality. First setup data.frame cell counts convert table. follow notation x received treatment, y outcome, z randomized treatment (instrumental variable). Next use prop.table() calculate conditional probabilities, \\(P(Y,X|Z)\\), run bpbounds() assuming data trivariate. Although call bpbounds() using table cell counts xt directly, .e. bpbounds(xt). Therefore, average causal effect bounded -0.1946 0.0054. estimate ACE found \\[\\text{ACE} = \\frac{\\text{cov}(Y,Z)}{\\text{cov}(X,Z)}\\] follows.","code":"tab1dat <- data.frame(   z = c(0, 0, 1, 1, 1, 1, 0, 0),   x = c(0, 0, 0, 0, 1, 1, 1, 1),   y = c(0, 1, 0, 1, 0, 1, 0, 1),   freq = c(74, 11514, 34, 2385, 12, 9663, 0, 0) )  tab1inddat <- uncount(tab1dat, freq) xt         <- xtabs(~ x + y + z, data = tab1inddat) xt #> , , z = 0 #>  #>    y #> x       0     1 #>   0    74 11514 #>   1     0     0 #>  #> , , z = 1 #>  #>    y #> x       0     1 #>   0    34  2385 #>   1    12  9663 p <- prop.table(xt, margin = 3) p #> , , z = 0 #>  #>    y #> x              0            1 #>   0 0.0063859165 0.9936140835 #>   1 0.0000000000 0.0000000000 #>  #> , , z = 1 #>  #>    y #> x              0            1 #>   0 0.0028113114 0.1972052257 #>   1 0.0009922276 0.7989912353 bpres <- bpbounds(p) sbp   <- summary(bpres) print(sbp) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 covyz <- cov(tab1inddat$y, tab1inddat$z) covxz <- cov(tab1inddat$x, tab1inddat$z) ace   <- covyz / covxz ace #> [1] 0.003228039"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"entering-the-data-as-conditional-probabilities","dir":"Articles","previous_headings":"Vitamin A supplementation example","what":"Entering the data as conditional probabilities","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"already know conditional probabilities pass bpbounds() follows.","code":"condprob <- c(.0064, 0, .9936, 0, .0028, .001, .1972, .799) tabp <- array(condprob,              dim = c(2, 2, 2),              dimnames = list(                x = c(0, 1),                y = c(0, 1),                z = c(0, 1)              )) %>%   as.table() bpbounds(tabp) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"treating-the-data-as-bivariate","dir":"Articles","previous_headings":"Vitamin A supplementation example","what":"Treating the data as bivariate","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"demonstrate features command can treat data bivariate.","code":"gtab <- xtabs( ~ y + z, data = tab1inddat) gp   <- prop.table(gtab, margin = 2) gp #>    z #> y             0           1 #>   0 0.006385916 0.003803539 #>   1 0.993614084 0.996196461  ttab <- xtabs( ~ x + z, data = tab1inddat) tp   <- prop.table(ttab, margin = 2) tp #>    z #> x           0         1 #>   0 1.0000000 0.2000165 #>   1 0.0000000 0.7999835  bpres2 <- bpbounds(p = gp, t = tp, fmt = \"bivariate\") sbp2   <- summary(bpres2) print(sbp2) #>  #> Data:                    bivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1974342 0.006385916 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7961799 1.196212998 #>               CRR   0.8012969 1.203901009 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1974342 0.006385916 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7961799 1.002582378 #>               CRR   0.8012969 1.009025933"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"mendelian-randomization-example","dir":"Articles","previous_headings":"","what":"Mendelian randomization example","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"Mendelian randomization approach epidemiology due Davey Smith Ebrahim (2003) genotypes established robustly associated phenotypes used instrumental variables order better estimate causal effect phenotype disease outcome. example uses data Meleady et al. (2003). trivariate data 3 category instrument binary phenotype outcomes. instrument 677CT polymorphism (rs1801133) Methylenetetrahydrofolate Reductase gene, involved folate metabolism, instrumental variable investigate effect homocysteine cardiovascular disease. data presented us conditional probabilities, take care enter correct position vectors. see example monotonicity inequality satisfied.","code":"mt3 <- c(.83, .05, .11, .01, .88, .06, .05, .01, .72, .05, .20, .03) p3 = array(mt3,            dim = c(2, 2, 3),            dimnames = list(              x = c(0, 1),              y = c(0, 1),              z = c(0, 1, 2)            )) p3     <- as.table(p3) bpres3 <- bpbounds(p3) sbp3   <- summary(bpres3) print(sbp3) #>  #> Data:                    trivariate #> Instrument categories:   3 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE       -0.09     0.74000 #>      P(Y|do(X=0))        0.06     0.12000 #>      P(Y|do(X=1))        0.03     0.80000 #>               CRR        0.25    13.33333 #>  #> Monotonicity inequality: FALSE"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"simulated-example-that-does-not-satisy-the-iv-conditions","dir":"Articles","previous_headings":"","what":"Simulated example that does not satisy the IV conditions","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"example recreates found section 8.3 Stata Journal paper uses simulated data show IV inequality always detect violations underlying model assumptions. simulate two outcomes, y1 y2, assumptions violated direct effect instrument, z, outcome. However, direct effect y2 much smaller magnitude direct effect y1. Due strong direct effect instrument outcome, instrumental variable monotonicity inequalities satisifed. However, weaker direct effect see satisfied true underlying data generating model satisfy instrumental variable assumptions.","code":"set.seed(2232011) n      <- 10000 z      <- rbinom(n, 1, .5) u      <- rbinom(n, 1, .5) px     <- .05 + .1 * z + .1 * u x      <- rbinom(n, 1, px) p1     <- .1 + .2 * z + .05 * x + .1 * u y1     <- rbinom(n, 1, p1) p2     <- .1 + .05 * z + .05 * x + .1 * u y2     <- rbinom(n, 1, p2)  tab1   <- xtabs(~ x + y1 + z) p1     <- prop.table(tab1, margin = 3)  bpres1 <- bpbounds(p1) sbp1   <- summary(bpres1) print(sbp1) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: FALSE  #>  #> Monotonicity inequality: FALSE tab2   <- xtabs(~ x + y2 + z) p2     <- prop.table(tab2, margin = 3)  bpres2 <- bpbounds(p2) sbp2   <- summary(bpres2) print(sbp2) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE -0.18711965   0.6700688 #>      P(Y|do(X=0))  0.17159525   0.2384172 #>      P(Y|do(X=1))  0.05129753   0.8416640 #>               CRR  0.21515868   4.9049378 #>  #> Monotonicity inequality: FALSE"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"conclusion, demonstrated use bpbounds package implementing nonparametric bounds average causal effect range data scenarios.","code":""},{"path":[]},{"path":"https://remlapmot.github.io/bpbounds/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tom Palmer. Author, maintainer. Roland Ramsahai. Author. Vanessa Didelez. Author. Nuala Sheehan. Author.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Palmer T, Ramsahai R, Didelez V, Sheehan N (2018). bpbounds: R package implementing Balke-Pearl bounds average causal effect. https://github.com/remlapmot/bpbounds.","code":"@Manual{bpbounds-package,   author = {T.M. Palmer and R. Ramsahai and V. Didelez and N.A. Sheehan},   title = {bpbounds: R package implementing Balke-Pearl bounds for the average causal effect},   year = {2018},   url = {https://github.com/remlapmot/bpbounds}, }"},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"bpbounds","dir":"","previous_headings":"","what":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"R package implementing nonparametric bounds average causal effect Balke Pearl (1997).","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Install released version bpbounds CRAN: install development version GitHub : update development version package, simply run command .","code":"install.packages(\"bpbounds\") # Uncomment the next command if you don't have the remotes packages installed. # The tidyverse have moved some functions out of the devtools package. # install.packages(\"remotes\")   # I call install_github() with these options to build the vignette. remotes::install_github(\"remlapmot/bpbounds\",                          build_opts = c(\"--no-resave-data\", \"--no-manual\"),                          build_vignettes = TRUE)"},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"shiny-app","dir":"","previous_headings":"","what":"Shiny App","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Shiny app demonstrating package : https://remlapmot.shinyapps.io/bpbounds.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"package-website","dir":"","previous_headings":"","what":"Package website","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"helpfiles vignette shown package website : https://remlapmot.github.io/bpbounds/.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"authors","dir":"","previous_headings":"","what":"Authors","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Tom Palmer (maintainer, tom.palmer@bristol.ac.uk), Roland Ramsahai, Vanessa Didelez, Nuala Sheehan.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Balke , Pearl J. Bounds Treatment Effects studies imperfect compliance. Journal American Statistical Association, 1997, 92, 439, 1171-1176, doi: 10.1080/01621459.1997.10474074.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds-package.html","id":null,"dir":"Reference","previous_headings":"","what":"bpbounds: Nonparametric bounds for the Average Causal Effect due to Balke and Pearl and extensions — bpbounds-package","title":"bpbounds: Nonparametric bounds for the Average Causal Effect due to Balke and Pearl and extensions — bpbounds-package","text":"package implements nonparametric bounds average causal effect defined Balke Pearl, Bounds Treatment Effects Studies Imperfect Compliance, JASA, 1997; extensions.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"bpbounds: Nonparametric bounds for the Average Causal Effect due to Balke and Pearl and extensions — bpbounds-package","text":"functions implement bounds situation outcome, treatment/phenotype, instrumental variable binary; additionally instrument 3 categories (e.g. single genotype additive model Mendelian randomization study). package implements bounds three variables measured study (trivariate data) outcome instrument measured one study treatment/phenotype instrument another sample (bivariate/two sample data).","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":null,"dir":"Reference","previous_headings":"","what":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"Nonparametric Bounds Average Causal Effect due Balke Pearl.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"","code":"bpbounds(p, t = NULL, fmt = \"trivariate\")"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"p Object class \"table\" containing either cell counts conditional probabilities. trivariate data phenotype/treatment-outcome association given Z, .e. P(X, Y | Z). Cell counts generated xtabs(~ x + y + z, data = data). conditional probabilities obatained calling prop.table(..., margins = 3) object xtabs(). know conditional probabilities can enter , e.g. Balke Pearl Vitamin example:   call bpbounds() using object. bivariate data object contains cell conditional probabilities outcome-instrument (Y|Z) association. t Specified bivariate data. Object treatment/phenotype-instrument cell counts conditional probabilities, .e. (X|Z). fmt character string sould either \"bivariate\" (.e. X, Z one dataset Y, Z another dataset) \"trivariate\" (X, Y, Z dataset).","code":"cp   <- c(.0064, 0, .9936, 0, .0028, .001, .1972, .799) tabp <- as.table(array(   cp,   dim = c(2, 2, 2),   dimnames = list(     x = c(0, 1),     y = c(0, 1),     z = c(0, 1)   ) ))"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"List following elements: fmt whether data bivariate trivariate nzcats 2 3, . instrument categories inequality Logical, indicating whether IV inquality satisfied bplb Lower bound ACE bpub Upper bound ACE bplower Vector lower bound probabilities bpupper Vector upper bound probabilities p11low Lower bound P(Y=1|(X=1)) p11upp Upper bound P(Y=1|(X=1)) p10low Lower bound P(Y=1|(X=0)) p10upp Upper bound P(Y=1|(X=0)) p11lower Vector probabilities lower bound P(Y=1|(X=1)) p11upper Vector probabilities upper bound P(Y=1|(X=1)) p10lower Vector probabilities lower bound P(Y=1|(X=0)) p10upper Vector probabilities upper bound P(Y=1|(X=0)) crrlb Lower bound CRR crrub Upper bound CRR monoinequality Logical, indicating whether monoticity inequality satisfied monobplb Lower bound ACE assuming monotonicity monobpub Upper bound ACE assuming monotonicity monobplower Vector probabilities lower bound ACE assuming monotonicity monobpupper Vector probabilities upper bound ACE assuming monotonicity monop11low Lower bound P(Y=1|(X=1)) assuming monotonicity monop11upp Upper bound P(Y=1|(X=1)) assuming monotonicity monop10low Lower bound P(Y=1|(X=0)) assuming monotonicity monop10upp Upper bound P(Y=1|(X=0)) assuming monotonicity monop11lower Vector corresponding bound monop11upper Vector corresponding bound monop10lower Vector corresponding bound monop10upper Vector corresponding bound monocrrlb Lower bound CRR assuming monotonicity monocrrub Upper bound CRR assuming monotonicity","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"","code":"# \\donttest{ # Vitamin A example, using cell counts  require(tidyr) #> Loading required package: tidyr require(bpbounds)  tab1dat <- data.frame(   z = c(0, 0, 1, 1, 1, 1, 0, 0),   x = c(0, 0, 0, 0, 1, 1, 1, 1),   y = c(0, 1, 0, 1, 0, 1, 0, 1),   freq = c(74, 11514, 34, 2385, 12, 9663, 0, 0) ) tab1inddat <- uncount(tab1dat, freq) xt         <- xtabs(~ x + y + z, data = tab1inddat) p          <- prop.table(xt, margin = 3) bpres      <- bpbounds(p) sbpres     <- summary(bpres) print(sbpres) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 #>  # }  # \\donttest{ # Vitamin A example, using conditional probabilities  require(bpbounds) cp = c(.0064, 0, .9936, 0, .0028, .001, .1972, .799) tabp = as.table(array(   cp,   dim = c(2, 2, 2),   dimnames = list(     x = c(0, 1),     y = c(0, 1),     z = c(0, 1)   ) )) bpbounds(tabp) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435 #>  # }"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":null,"dir":"Reference","previous_headings":"","what":"Run Shiny App demonstrating the package — runExample","title":"Run Shiny App demonstrating the package — runExample","text":"Run Shiny App demonstrating package","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run Shiny App demonstrating the package — runExample","text":"","code":"runExample(...)"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run Shiny App demonstrating the package — runExample","text":"... passed shiny::runApp(), e.g. port, launch.browser","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run Shiny App demonstrating the package — runExample","text":"","code":"if (interactive() && requireNamespace(\"shiny\", quietly = TRUE)) {   bpbounds::runExample() }"},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-0159000","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.5.9000","title":"bpbounds 0.1.5.9000","text":"Tweak formatting code helpfile examples vignette Bumped minimum required version R least 3.6.0 now required testthat version 3.2.0","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-015","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.5","title":"bpbounds 0.1.5","text":"CRAN release: 2023-05-03 Additional email address affiliation edits. Remove LazyData DESCRIPTION. Update roxygen2 version number DESCRIPTION. Simplify NAMESPACE importing functions packages. Improve accompanying pkgdown site","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-014","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.4","title":"bpbounds 0.1.4","text":"CRAN release: 2020-01-21 Fixed typo vignette. Changed email address.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-013","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.3","title":"bpbounds 0.1.3","text":"CRAN release: 2019-02-10 Added Nuala’s ORCID DESCRIPTION.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-012","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.2","title":"bpbounds 0.1.2","text":"Added Roland’s ORCID DESCRIPTION.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-011","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.1","title":"bpbounds 0.1.1","text":"CRAN release: 2018-12-19 Added runExample() launch Shiny App included ./inst/shiny-examples/myapp .","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-010","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.0","title":"bpbounds 0.1.0","text":"CRAN release: 2018-11-18 R version bpbounds Stata command distributed Palmer et al., Stata Journal, 2011, 11, 3, 345-367 https://www.stata-journal.com/article.html?article=st0232. First submission CRAN.","code":""}]
+[{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"short vignette demonstrates use bpbounds package. R implementation nonparametric bounds average causal effect Balke Pearl (1997) extensions. Currently R package port bpbounds Stata package (Palmer et al. (2011)). code implements approach calculating bounds outlined Ramsahai (2007), Ramsahai (2008), Ramsahai (2012). start loading package others needed code vignette.","code":"library(bpbounds) library(dplyr) #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union library(tidyr)"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"features-of-the-bpbounds-package","dir":"Articles","previous_headings":"","what":"Features of the bpbounds package","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"Currently package one function, bpbounds(), can accommodate four scenarios implemented Stata command. calculate bounds binary outcome, binary treatment/phenotype, : binary instrumental variable; 3 category instrumental variable, e.g. genotype Mendelian randomization; Bounds scenarios can calculated either trivariate data (three variables measured one dataset); bivariate data (treatment/phenotype instrument measured one dataset outcome instrument measured another sample).","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"vitamin-a-supplementation-example","dir":"Articles","previous_headings":"","what":"Vitamin A supplementation example","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"example taken Table 1 Balke Pearl (1997). study Vitamin supplementation consisting children 450 villages. , 221 villages assigned control 229 treatment. outcome mortality. First setup data.frame cell counts convert table. follow notation x received treatment, y outcome, z randomized treatment (instrumental variable). Next use prop.table() calculate conditional probabilities, \\(P(Y,X|Z)\\), run bpbounds() assuming data trivariate. Although call bpbounds() using table cell counts xt directly, .e. bpbounds(xt). Therefore, average causal effect bounded -0.1946 0.0054. estimate ACE found \\[\\text{ACE} = \\frac{\\text{cov}(Y,Z)}{\\text{cov}(X,Z)}\\] follows.","code":"tab1dat <- data.frame(   z = c(0, 0, 1, 1, 1, 1, 0, 0),   x = c(0, 0, 0, 0, 1, 1, 1, 1),   y = c(0, 1, 0, 1, 0, 1, 0, 1),   freq = c(74, 11514, 34, 2385, 12, 9663, 0, 0) )  tab1inddat <- uncount(tab1dat, freq) xt         <- xtabs(~ x + y + z, data = tab1inddat) xt #> , , z = 0 #>  #>    y #> x       0     1 #>   0    74 11514 #>   1     0     0 #>  #> , , z = 1 #>  #>    y #> x       0     1 #>   0    34  2385 #>   1    12  9663 p <- prop.table(xt, margin = 3) p #> , , z = 0 #>  #>    y #> x              0            1 #>   0 0.0063859165 0.9936140835 #>   1 0.0000000000 0.0000000000 #>  #> , , z = 1 #>  #>    y #> x              0            1 #>   0 0.0028113114 0.1972052257 #>   1 0.0009922276 0.7989912353 bpres <- bpbounds(p) sbp   <- summary(bpres) print(sbp) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 covyz <- cov(tab1inddat$y, tab1inddat$z) covxz <- cov(tab1inddat$x, tab1inddat$z) ace   <- covyz / covxz ace #> [1] 0.003228039"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"entering-the-data-as-conditional-probabilities","dir":"Articles","previous_headings":"Vitamin A supplementation example","what":"Entering the data as conditional probabilities","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"already know conditional probabilities pass bpbounds() follows.","code":"condprob <- c(.0064, 0, .9936, 0, .0028, .001, .1972, .799) tabp <- array(condprob,              dim = c(2, 2, 2),              dimnames = list(                x = c(0, 1),                y = c(0, 1),                z = c(0, 1)              )) %>%   as.table() bpbounds(tabp) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"treating-the-data-as-bivariate","dir":"Articles","previous_headings":"Vitamin A supplementation example","what":"Treating the data as bivariate","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"demonstrate features command can treat data bivariate.","code":"gtab <- xtabs( ~ y + z, data = tab1inddat) gp   <- prop.table(gtab, margin = 2) gp #>    z #> y             0           1 #>   0 0.006385916 0.003803539 #>   1 0.993614084 0.996196461  ttab <- xtabs( ~ x + z, data = tab1inddat) tp   <- prop.table(ttab, margin = 2) tp #>    z #> x           0         1 #>   0 1.0000000 0.2000165 #>   1 0.0000000 0.7999835  bpres2 <- bpbounds(p = gp, t = tp, fmt = \"bivariate\") sbp2   <- summary(bpres2) print(sbp2) #>  #> Data:                    bivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1974342 0.006385916 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7961799 1.196212998 #>               CRR   0.8012969 1.203901009 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1974342 0.006385916 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7961799 1.002582378 #>               CRR   0.8012969 1.009025933"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"mendelian-randomization-example","dir":"Articles","previous_headings":"","what":"Mendelian randomization example","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"Mendelian randomization approach epidemiology due Davey Smith Ebrahim (2003) genotypes established robustly associated phenotypes used instrumental variables order better estimate causal effect phenotype disease outcome. example uses data Meleady et al. (2003). trivariate data 3 category instrument binary phenotype outcomes. instrument 677CT polymorphism (rs1801133) Methylenetetrahydrofolate Reductase gene, involved folate metabolism, instrumental variable investigate effect homocysteine cardiovascular disease. data presented us conditional probabilities, take care enter correct position vectors. see example monotonicity inequality satisfied.","code":"mt3 <- c(.83, .05, .11, .01, .88, .06, .05, .01, .72, .05, .20, .03) p3 = array(mt3,            dim = c(2, 2, 3),            dimnames = list(              x = c(0, 1),              y = c(0, 1),              z = c(0, 1, 2)            )) p3     <- as.table(p3) bpres3 <- bpbounds(p3) sbp3   <- summary(bpres3) print(sbp3) #>  #> Data:                    trivariate #> Instrument categories:   3 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE       -0.09     0.74000 #>      P(Y|do(X=0))        0.06     0.12000 #>      P(Y|do(X=1))        0.03     0.80000 #>               CRR        0.25    13.33333 #>  #> Monotonicity inequality: FALSE"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"simulated-example-that-does-not-satisy-the-iv-conditions","dir":"Articles","previous_headings":"","what":"Simulated example that does not satisy the IV conditions","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"example recreates found section 8.3 Stata Journal paper uses simulated data show IV inequality always detect violations underlying model assumptions. simulate two outcomes, y1 y2, assumptions violated direct effect instrument, z, outcome. However, direct effect y2 much smaller magnitude direct effect y1. Due strong direct effect instrument outcome, instrumental variable monotonicity inequalities satisifed. However, weaker direct effect see satisfied true underlying data generating model satisfy instrumental variable assumptions.","code":"set.seed(2232011) n      <- 10000 z      <- rbinom(n, 1, .5) u      <- rbinom(n, 1, .5) px     <- .05 + .1 * z + .1 * u x      <- rbinom(n, 1, px) p1     <- .1 + .2 * z + .05 * x + .1 * u y1     <- rbinom(n, 1, p1) p2     <- .1 + .05 * z + .05 * x + .1 * u y2     <- rbinom(n, 1, p2)  tab1   <- xtabs(~ x + y1 + z) p1     <- prop.table(tab1, margin = 3)  bpres1 <- bpbounds(p1) sbp1   <- summary(bpres1) print(sbp1) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: FALSE  #>  #> Monotonicity inequality: FALSE tab2   <- xtabs(~ x + y2 + z) p2     <- prop.table(tab2, margin = 3)  bpres2 <- bpbounds(p2) sbp2   <- summary(bpres2) print(sbp2) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE -0.18711965   0.6700688 #>      P(Y|do(X=0))  0.17159525   0.2384172 #>      P(Y|do(X=1))  0.05129753   0.8416640 #>               CRR  0.21515868   4.9049378 #>  #> Monotonicity inequality: FALSE"},{"path":"https://remlapmot.github.io/bpbounds/dev/articles/bpbounds.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Nonparametric bounds for the average causal effect: bpbounds examples","text":"conclusion, demonstrated use bpbounds package implementing nonparametric bounds average causal effect range data scenarios.","code":""},{"path":[]},{"path":"https://remlapmot.github.io/bpbounds/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tom Palmer. Author, maintainer. Roland Ramsahai. Author. Vanessa Didelez. Author. Nuala Sheehan. Author.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Palmer T, Ramsahai R, Didelez V, Sheehan N (2018). bpbounds: R package implementing Balke-Pearl bounds average causal effect. https://github.com/remlapmot/bpbounds.","code":"@Manual{bpbounds-package,   author = {T.M. Palmer and R. Ramsahai and V. Didelez and N.A. Sheehan},   title = {bpbounds: R package implementing Balke-Pearl bounds for the average causal effect},   year = {2018},   url = {https://github.com/remlapmot/bpbounds}, }"},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"bpbounds","dir":"","previous_headings":"","what":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"R package implementing nonparametric bounds average causal effect Balke Pearl (1997).","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Install released version bpbounds CRAN: install development version GitHub : update development version package, simply run command .","code":"install.packages(\"bpbounds\") # Uncomment the next command if you don't have the remotes packages installed. # The tidyverse have moved some functions out of the devtools package. # install.packages(\"remotes\")   # I call install_github() with these options to build the vignette. remotes::install_github(\"remlapmot/bpbounds\",                          build_opts = c(\"--no-resave-data\", \"--no-manual\"),                          build_vignettes = TRUE)"},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"shiny-app","dir":"","previous_headings":"","what":"Shiny App","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Shiny app demonstrating package : https://remlapmot.shinyapps.io/bpbounds.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"package-website","dir":"","previous_headings":"","what":"Package website","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"helpfiles vignette shown package website : https://remlapmot.github.io/bpbounds/.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"authors","dir":"","previous_headings":"","what":"Authors","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Tom Palmer (maintainer, tom.palmer@bristol.ac.uk), Roland Ramsahai, Vanessa Didelez, Nuala Sheehan.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Nonparametric Bounds for the Average Causal Effect Due to Balke and\n    Pearl and Extensions","text":"Balke , Pearl J. Bounds Treatment Effects studies imperfect compliance. Journal American Statistical Association, 1997, 92, 439, 1171-1176, doi: 10.1080/01621459.1997.10474074.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds-package.html","id":null,"dir":"Reference","previous_headings":"","what":"bpbounds: Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions — bpbounds-package","title":"bpbounds: Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions — bpbounds-package","text":"Implementation nonparametric bounds average causal effect instrumental variable model Balke Pearl (Bounds Treatment Effects Studies Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176). package can calculate bounds binary outcome, binary treatment/phenotype, instrument either 2 3 categories. package implements bounds situations 3 variables measured dataset (trivariate data) outcome instrument measured one study treatment/phenotype instrument measured another study (bivariate data).","code":""},{"path":[]},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"bpbounds: Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions — bpbounds-package","text":"Maintainer: Tom Palmer remlapmot@hotmail.com (ORCID) Authors: Roland Ramsahai (ORCID) Vanessa Didelez (ORCID) Nuala Sheehan (ORCID)","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":null,"dir":"Reference","previous_headings":"","what":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"Nonparametric Bounds Average Causal Effect due Balke Pearl.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"","code":"bpbounds(p, t = NULL, fmt = \"trivariate\")"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"p Object class \"table\" containing either cell counts conditional probabilities. trivariate data phenotype/treatment-outcome association given Z, .e. P(X, Y | Z). Cell counts generated xtabs(~ x + y + z, data = data). conditional probabilities obatained calling prop.table(..., margins = 3) object xtabs(). know conditional probabilities can enter , e.g. Balke Pearl Vitamin example:   call bpbounds() using object. bivariate data object contains cell conditional probabilities outcome-instrument (Y|Z) association. t Specified bivariate data. Object treatment/phenotype-instrument cell counts conditional probabilities, .e. (X|Z). fmt character string sould either \"bivariate\" (.e. X, Z one dataset Y, Z another dataset) \"trivariate\" (X, Y, Z dataset).","code":"cp   <- c(.0064, 0, .9936, 0, .0028, .001, .1972, .799) tabp <- as.table(array(   cp,   dim = c(2, 2, 2),   dimnames = list(     x = c(0, 1),     y = c(0, 1),     z = c(0, 1)   ) ))"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"List following elements: fmt whether data bivariate trivariate nzcats 2 3, . instrument categories inequality Logical, indicating whether IV inquality satisfied bplb Lower bound ACE bpub Upper bound ACE bplower Vector lower bound probabilities bpupper Vector upper bound probabilities p11low Lower bound P(Y=1|(X=1)) p11upp Upper bound P(Y=1|(X=1)) p10low Lower bound P(Y=1|(X=0)) p10upp Upper bound P(Y=1|(X=0)) p11lower Vector probabilities lower bound P(Y=1|(X=1)) p11upper Vector probabilities upper bound P(Y=1|(X=1)) p10lower Vector probabilities lower bound P(Y=1|(X=0)) p10upper Vector probabilities upper bound P(Y=1|(X=0)) crrlb Lower bound CRR crrub Upper bound CRR monoinequality Logical, indicating whether monoticity inequality satisfied monobplb Lower bound ACE assuming monotonicity monobpub Upper bound ACE assuming monotonicity monobplower Vector probabilities lower bound ACE assuming monotonicity monobpupper Vector probabilities upper bound ACE assuming monotonicity monop11low Lower bound P(Y=1|(X=1)) assuming monotonicity monop11upp Upper bound P(Y=1|(X=1)) assuming monotonicity monop10low Lower bound P(Y=1|(X=0)) assuming monotonicity monop10upp Upper bound P(Y=1|(X=0)) assuming monotonicity monop11lower Vector corresponding bound monop11upper Vector corresponding bound monop10lower Vector corresponding bound monop10upper Vector corresponding bound monocrrlb Lower bound CRR assuming monotonicity monocrrub Upper bound CRR assuming monotonicity","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/bpbounds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nonparametric Bounds for the Average Causal Effect due to Balke and Pearl. — bpbounds","text":"","code":"# \\donttest{ # Vitamin A example, using cell counts  require(tidyr) #> Loading required package: tidyr require(bpbounds)  tab1dat <- data.frame(   z = c(0, 0, 1, 1, 1, 1, 0, 0),   x = c(0, 0, 0, 0, 1, 1, 1, 1),   y = c(0, 1, 0, 1, 0, 1, 0, 1),   freq = c(74, 11514, 34, 2385, 12, 9663, 0, 0) ) tab1inddat <- uncount(tab1dat, freq) xt         <- xtabs(~ x + y + z, data = tab1inddat) p          <- prop.table(xt, margin = 3) bpres      <- bpbounds(p) sbpres     <- summary(bpres) print(sbpres) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946228 0.005393689 #>      P(Y|do(X=0))   0.9936141 0.993614084 #>      P(Y|do(X=1))   0.7989912 0.999007772 #>               CRR   0.8041263 1.005428354 #>  # }  # \\donttest{ # Vitamin A example, using conditional probabilities  require(bpbounds) cp = c(.0064, 0, .9936, 0, .0028, .001, .1972, .799) tabp = as.table(array(   cp,   dim = c(2, 2, 2),   dimnames = list(     x = c(0, 1),     y = c(0, 1),     z = c(0, 1)   ) )) bpbounds(tabp) #>  #> Data:                    trivariate #> Instrument categories:   2 #>  #> Instrumental inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435 #>  #> Monotonicity inequality: TRUE  #>  Causal parameter Lower bound Upper bound #>               ACE  -0.1946000    0.005400 #>      P(Y|do(X=0))   0.9936000    0.993600 #>      P(Y|do(X=1))   0.7990000    0.999000 #>               CRR   0.8041465    1.005435 #>  # }"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":null,"dir":"Reference","previous_headings":"","what":"Run Shiny App demonstrating the package — runExample","title":"Run Shiny App demonstrating the package — runExample","text":"Run Shiny App demonstrating package","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run Shiny App demonstrating the package — runExample","text":"","code":"runExample(...)"},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run Shiny App demonstrating the package — runExample","text":"... passed shiny::runApp(), e.g. port, launch.browser","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/reference/runExample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run Shiny App demonstrating the package — runExample","text":"","code":"if (interactive() && requireNamespace(\"shiny\", quietly = TRUE)) {   bpbounds::runExample() }"},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-0159000","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.5.9000","title":"bpbounds 0.1.5.9000","text":"Tweak formatting code helpfile examples vignette Bumped minimum required version R least 3.6.0 now required testthat version 3.2.0 Bumped version roxygen2 used create package documentation","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-015","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.5","title":"bpbounds 0.1.5","text":"CRAN release: 2023-05-03 Additional email address affiliation edits. Remove LazyData DESCRIPTION. Update roxygen2 version number DESCRIPTION. Simplify NAMESPACE importing functions packages. Improve accompanying pkgdown site","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-014","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.4","title":"bpbounds 0.1.4","text":"CRAN release: 2020-01-21 Fixed typo vignette. Changed email address.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-013","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.3","title":"bpbounds 0.1.3","text":"CRAN release: 2019-02-10 Added Nuala’s ORCID DESCRIPTION.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-012","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.2","title":"bpbounds 0.1.2","text":"Added Roland’s ORCID DESCRIPTION.","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-011","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.1","title":"bpbounds 0.1.1","text":"CRAN release: 2018-12-19 Added runExample() launch Shiny App included ./inst/shiny-examples/myapp .","code":""},{"path":"https://remlapmot.github.io/bpbounds/dev/news/index.html","id":"bpbounds-010","dir":"Changelog","previous_headings":"","what":"bpbounds 0.1.0","title":"bpbounds 0.1.0","text":"CRAN release: 2018-11-18 R version bpbounds Stata command distributed Palmer et al., Stata Journal, 2011, 11, 3, 345-367 https://www.stata-journal.com/article.html?article=st0232. First submission CRAN.","code":""}]
