validate.ivars

Checks that the required variable names are defined within <code>vars</code> and
are of appropriate datatypes

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 

validate.ivars function

<dl><dt>x</dt>
<dd>named list indicating the names of key variables in the source dataset</dd>
<dt>...</dt>
<dd>not used</dd></dl>

Arguments

Validate inputs for vars — validate.ivars

<dl>

<dt>x</dt>
<dd>named list indicating the names of key variables in the source dataset</dd>


<dt>...</dt>
<dd>not used</dd>

</dl>

validate.ivars: Validate inputs for `vars`

Description

Usage

Arguments