imputation_df

Create a valid <code>imputation_df</code> object

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 

imputation_df function

<dl><dt>...</dt>
<dd>a list of <code>imputation_single</code>.</dd></dl>

Arguments

Create a valid imputation_df object — imputation_df

<dl>

<dt>...</dt>
<dd>a list of <code>imputation_single</code>.</dd>

</dl>

imputation_df: Create a valid `imputation_df` object

Description

Usage

Arguments