par_lapply

Simple wrapper around <code>lapply</code> and <code><a href="/link/parallel%3A%3AclusterApplyLB?package=rbmi&version=1.4.1" data-mini-rdoc="rbmi::parallel::clusterApplyLB">parallel::clusterApplyLB</a></code> to abstract away
the logic of deciding which one to use

Implements standard and reference based multiple imputation methods for continuous
longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
this package supports deterministic conditional mean imputation and jackknifing as described
in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Isaac Gravestock

rbmi

Reference Based Multiple Imputation

Craig Gower-Page

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

par_lapply function

<dl><dt>cl</dt>
<dd>Cluster created by <code><a href="/link/parallel%3A%3AmakeCluster()?package=rbmi&version=1.4.1" data-mini-rdoc="rbmi::parallel::makeCluster()">parallel::makeCluster()</a></code> or <code>NULL</code></dd>
<dt>fun</dt>
<dd>Function to be run</dd>
<dt>x</dt>
<dd>object to be looped over</dd>
<dt>...</dt>
<dd>extra arguements passed to <code>fun</code></dd></dl>

Arguments

Simple wrapper around <code>lapply</code> and <code><a href='https://rdrr.io/r/parallel/clusterApply.html'>parallel::clusterApplyLB</a></code> to abstract away
the logic of deciding which one to use

Parallelise Lapply — par_lapply

<dl>

<dt>cl</dt>
<dd>Cluster created by <code><a href='https://rdrr.io/r/parallel/makeCluster.html'>parallel::makeCluster()</a></code> or <code>NULL</code></dd>


<dt>fun</dt>
<dd>Function to be run</dd>


<dt>x</dt>
<dd>object to be looped over</dd>


<dt>...</dt>
<dd>extra arguements passed to <code>fun</code></dd>

</dl>

par_lapply: Parallelise Lapply

Description

Usage

Arguments