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Release v1.3.1 #464

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Dec 10, 2024
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Release v1.3.1 #464

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bd1c428
updated version number
gowerc Dec 10, 2024
7e7431b
misc updates
gowerc Dec 10, 2024
9c3b73e
remove BH from package check
gowerc Dec 10, 2024
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3 changes: 2 additions & 1 deletion DESCRIPTION
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Package: rbmi
Title: Reference Based Multiple Imputation
Version: 1.3.0.9000
Version: 1.3.1
Authors@R: c(
person("Craig", "Gower-Page", email = "[email protected]", role = c("aut", "cre")),
person("Alessandro", "Noci", email = "[email protected]", role = c("aut")),
person("Marcel", "Wolbers", email = "[email protected]", role = "ctb"),
person("Isaac", "Gravestock", email = "[email protected]", role = "aut"),
person("F. Hoffmann-La Roche AG", role = c("cph", "fnd"))
)
Description: Implements standard and reference based multiple imputation methods for continuous
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1 change: 1 addition & 0 deletions NAMESPACE
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Expand Up @@ -104,3 +104,4 @@ importFrom(stats,var)
importFrom(stats,vcov)
importFrom(utils,capture.output)
importFrom(utils,relist)
importFrom(utils,sessionInfo)
4 changes: 2 additions & 2 deletions NEWS.md
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# rbmi (development version)
# rbmi 1.3.1

- No new items yet...
* Fixed bug where stale caches of the `rstan` model were not being correctly cleared (#459)

# rbmi 1.3.0

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3 changes: 2 additions & 1 deletion R/utilities.R
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Expand Up @@ -535,10 +535,11 @@ ensure_rstan <- function() {
#' Get session hash
#'
#' Gets a unique string based on the current R version and relevant packages.
#' @importFrom utils sessionInfo
#' @keywords internal
get_session_hash <- function() {
pkg_versions <- vapply(
sessionInfo(c("rbmi", "rstan", "Rcpp", "RcppEigen", "BH"))[["otherPkgs"]],
sessionInfo(c("rbmi", "rstan", "Rcpp", "RcppEigen"))[["otherPkgs"]],
function(x) x[["Version"]],
character(1L)
)
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2 changes: 1 addition & 1 deletion cran-comments.md
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## Summary of Submission

This version of the package adds two new vignettes as well as additional parallel processing support. It also includes a number of minor bug fixes and updates to the documentation.
This version of the package fixes an issue that was causing the unit tests to crash on CRAN. The issue was due to the package not correctly clearing out the cache of previously compiled `rstan` models.


## R CMD check results
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1 change: 1 addition & 0 deletions man/rbmi-package.Rd
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2 changes: 1 addition & 1 deletion vignettes/advanced.html
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Expand Up @@ -714,7 +714,7 @@ <h1><span class="header-section-number">6</span> Custom imputation strategies</h
<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a><span class="co">#&gt; pars &lt;- list(mu = mu, sigma = sigma)</span></span>
<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a><span class="co">#&gt; return(pars)</span></span>
<span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a><span class="co">#&gt; }</span></span>
<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#&gt; &lt;bytecode: 0x7fd89cc34720&gt;</span></span>
<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#&gt; &lt;bytecode: 0x7fd7e885e158&gt;</span></span>
<span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a><span class="co">#&gt; &lt;environment: namespace:rbmi&gt;</span></span></code></pre></div>
<p>To further illustrate this for a simple example, assume that a new strategy is to be implemented as follows:
- The marginal mean of the imputation distribution is equal to the marginal mean trajectory for the subject according to their assigned group and covariates up to the ICE.
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40 changes: 20 additions & 20 deletions vignettes/quickstart.html
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Expand Up @@ -456,7 +456,7 @@ <h1><span class="header-section-number">3</span> Draws</h1>
<span id="cb4-57"><a href="#cb4-57" tabindex="-1"></a> <span class="at">method =</span> method,</span>
<span id="cb4-58"><a href="#cb4-58" tabindex="-1"></a> <span class="at">quiet =</span> <span class="cn">TRUE</span></span>
<span id="cb4-59"><a href="#cb4-59" tabindex="-1"></a>)</span>
<span id="cb4-60"><a href="#cb4-60" tabindex="-1"></a><span class="co">#&gt; Warning in fit_mcmc(designmat = model_df_scaled[, -1, drop = FALSE], outcome = model_df_scaled[, : The largest R-hat is 1.06, indicating chains have not mixed.</span></span>
<span id="cb4-60"><a href="#cb4-60" tabindex="-1"></a><span class="co">#&gt; Warning in fit_mcmc(designmat = model_df_scaled[, -1, drop = FALSE], outcome = model_df_scaled[, : The largest R-hat is 1.05, indicating chains have not mixed.</span></span>
<span id="cb4-61"><a href="#cb4-61" tabindex="-1"></a><span class="co">#&gt; Running the chains for more iterations may help. See</span></span>
<span id="cb4-62"><a href="#cb4-62" tabindex="-1"></a><span class="co">#&gt; https://mc-stan.org/misc/warnings.html#r-hat</span></span>
<span id="cb4-63"><a href="#cb4-63" tabindex="-1"></a>drawObj</span>
Expand Down Expand Up @@ -697,33 +697,33 @@ <h1><span class="header-section-number">6</span> Pool</h1>
<span id="cb9-20"><a href="#cb9-20" tabindex="-1"></a><span class="co">#&gt; trt_4 -0.092 0.683 -1.439 1.256 0.893 </span></span>
<span id="cb9-21"><a href="#cb9-21" tabindex="-1"></a><span class="co">#&gt; lsm_ref_4 -1.616 0.486 -2.576 -0.656 0.001 </span></span>
<span id="cb9-22"><a href="#cb9-22" tabindex="-1"></a><span class="co">#&gt; lsm_alt_4 -1.708 0.475 -2.645 -0.77 &lt;0.001 </span></span>
<span id="cb9-23"><a href="#cb9-23" tabindex="-1"></a><span class="co">#&gt; trt_5 1.328 0.924 -0.497 3.153 0.153 </span></span>
<span id="cb9-24"><a href="#cb9-24" tabindex="-1"></a><span class="co">#&gt; lsm_ref_5 -4.14 0.66 -5.443 -2.838 &lt;0.001 </span></span>
<span id="cb9-25"><a href="#cb9-25" tabindex="-1"></a><span class="co">#&gt; lsm_alt_5 -2.812 0.646 -4.088 -1.537 &lt;0.001 </span></span>
<span id="cb9-26"><a href="#cb9-26" tabindex="-1"></a><span class="co">#&gt; trt_6 1.939 1 -0.037 3.915 0.054 </span></span>
<span id="cb9-27"><a href="#cb9-27" tabindex="-1"></a><span class="co">#&gt; lsm_ref_6 -6.085 0.718 -7.503 -4.667 &lt;0.001 </span></span>
<span id="cb9-28"><a href="#cb9-28" tabindex="-1"></a><span class="co">#&gt; lsm_alt_6 -4.146 0.699 -5.527 -2.766 &lt;0.001 </span></span>
<span id="cb9-29"><a href="#cb9-29" tabindex="-1"></a><span class="co">#&gt; trt_7 2.136 1.12 -0.078 4.35 0.058 </span></span>
<span id="cb9-30"><a href="#cb9-30" tabindex="-1"></a><span class="co">#&gt; lsm_ref_7 -6.982 0.812 -8.587 -5.377 &lt;0.001 </span></span>
<span id="cb9-31"><a href="#cb9-31" tabindex="-1"></a><span class="co">#&gt; lsm_alt_7 -4.846 0.789 -6.405 -3.287 &lt;0.001 </span></span>
<span id="cb9-23"><a href="#cb9-23" tabindex="-1"></a><span class="co">#&gt; trt_5 1.334 0.926 -0.494 3.162 0.151 </span></span>
<span id="cb9-24"><a href="#cb9-24" tabindex="-1"></a><span class="co">#&gt; lsm_ref_5 -4.151 0.661 -5.457 -2.846 &lt;0.001 </span></span>
<span id="cb9-25"><a href="#cb9-25" tabindex="-1"></a><span class="co">#&gt; lsm_alt_5 -2.817 0.648 -4.097 -1.537 &lt;0.001 </span></span>
<span id="cb9-26"><a href="#cb9-26" tabindex="-1"></a><span class="co">#&gt; trt_6 1.934 1 -0.042 3.91 0.055 </span></span>
<span id="cb9-27"><a href="#cb9-27" tabindex="-1"></a><span class="co">#&gt; lsm_ref_6 -6.088 0.715 -7.501 -4.676 &lt;0.001 </span></span>
<span id="cb9-28"><a href="#cb9-28" tabindex="-1"></a><span class="co">#&gt; lsm_alt_6 -4.155 0.698 -5.533 -2.777 &lt;0.001 </span></span>
<span id="cb9-29"><a href="#cb9-29" tabindex="-1"></a><span class="co">#&gt; trt_7 2.177 1.138 -0.073 4.426 0.058 </span></span>
<span id="cb9-30"><a href="#cb9-30" tabindex="-1"></a><span class="co">#&gt; lsm_ref_7 -7.002 0.827 -8.638 -5.367 &lt;0.001 </span></span>
<span id="cb9-31"><a href="#cb9-31" tabindex="-1"></a><span class="co">#&gt; lsm_alt_7 -4.826 0.783 -6.373 -3.278 &lt;0.001 </span></span>
<span id="cb9-32"><a href="#cb9-32" tabindex="-1"></a><span class="co">#&gt; --------------------------------------------------</span></span></code></pre></div>
<p>The table of values shown in the print message for <code>poolObj</code> can also be extracted using the <code>as.data.frame()</code> function:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="fu">as.data.frame</span>(poolObj)</span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a><span class="co">#&gt; parameter est se lci uci pval</span></span>
<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a><span class="co">#&gt; 1 trt_4 -0.09180645 0.6826279 -1.43949684 1.2558839 8.931772e-01</span></span>
<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a><span class="co">#&gt; 2 lsm_ref_4 -1.61581996 0.4862316 -2.57577141 -0.6558685 1.093708e-03</span></span>
<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a><span class="co">#&gt; 3 lsm_alt_4 -1.70762640 0.4749573 -2.64531931 -0.7699335 4.262148e-04</span></span>
<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a><span class="co">#&gt; 4 trt_5 1.32800107 0.9239991 -0.49687491 3.1528770 1.526144e-01</span></span>
<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a><span class="co">#&gt; 5 lsm_ref_5 -4.14031255 0.6595847 -5.44302381 -2.8376013 3.163421e-09</span></span>
<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a><span class="co">#&gt; 6 lsm_alt_5 -2.81231148 0.6459122 -4.08807336 -1.5365496 2.396574e-05</span></span>
<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a><span class="co">#&gt; 7 trt_6 1.93891419 1.0001460 -0.03694571 3.9147741 5.438468e-02</span></span>
<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a><span class="co">#&gt; 8 lsm_ref_6 -6.08530002 0.7176967 -7.50335519 -4.6672448 1.946811e-14</span></span>
<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a><span class="co">#&gt; 9 lsm_alt_6 -4.14638583 0.6985434 -5.52650481 -2.7662668 1.911219e-08</span></span>
<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a><span class="co">#&gt; 10 trt_7 2.13609482 1.1201125 -0.07781920 4.3500088 5.849971e-02</span></span>
<span id="cb10-13"><a href="#cb10-13" tabindex="-1"></a><span class="co">#&gt; 11 lsm_ref_7 -6.98181990 0.8117268 -8.58678186 -5.3768579 1.511791e-14</span></span>
<span id="cb10-14"><a href="#cb10-14" tabindex="-1"></a><span class="co">#&gt; 12 lsm_alt_7 -4.84572508 0.7885999 -6.40477980 -3.2866704 7.793320e-09</span></span></code></pre></div>
<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a><span class="co">#&gt; 4 trt_5 1.33436741 0.9255820 -0.49370018 3.1624350 1.513737e-01</span></span>
<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a><span class="co">#&gt; 5 lsm_ref_5 -4.15140080 0.6608110 -5.45658456 -2.8462170 3.110832e-09</span></span>
<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a><span class="co">#&gt; 6 lsm_alt_5 -2.81703340 0.6478134 -4.09662455 -1.5374422 2.460646e-05</span></span>
<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a><span class="co">#&gt; 7 trt_6 1.93372720 1.0000881 -0.04213496 3.9095894 5.502527e-02</span></span>
<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a><span class="co">#&gt; 8 lsm_ref_6 -6.08849645 0.7148756 -7.50096014 -4.6760328 1.548722e-14</span></span>
<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a><span class="co">#&gt; 9 lsm_alt_6 -4.15476925 0.6975559 -5.53298494 -2.7765536 1.739819e-08</span></span>
<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a><span class="co">#&gt; 10 trt_7 2.17651184 1.1379189 -0.07335404 4.4263777 5.784145e-02</span></span>
<span id="cb10-13"><a href="#cb10-13" tabindex="-1"></a><span class="co">#&gt; 11 lsm_ref_7 -7.00205125 0.8268086 -8.63751537 -5.3665871 4.132364e-14</span></span>
<span id="cb10-14"><a href="#cb10-14" tabindex="-1"></a><span class="co">#&gt; 12 lsm_alt_7 -4.82553941 0.7830124 -6.37333317 -3.2777456 6.903910e-09</span></span></code></pre></div>
<p>These outputs gives an estimated difference of
2.136 (95% CI -0.078 to 4.350)
2.177 (95% CI -0.073 to 4.426)
between the two groups at the last visit with an associated p-value of 0.058.</p>
</div>
<div id="code" class="section level1" number="7">
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