is_absent

Returns true if a value is either NULL, NA or "".
In the case of a vector all values must be NULL/NA/""
for x to be regarded as absent.

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 

is_absent function

<dl><dt>x</dt>
<dd>a value to check if it is absent or not</dd>
<dt>na</dt>
<dd>do NAs count as absent</dd>
<dt>blank</dt>
<dd>do blanks i.e. "" count as absent</dd></dl>

Arguments

Is value absent — is_absent

<dl>

<dt>x</dt>
<dd>a value to check if it is absent or not</dd>


<dt>na</dt>
<dd>do NAs count as absent</dd>


<dt>blank</dt>
<dd>do blanks i.e. "" count as absent</dd>

</dl>

is_absent: Is value absent

Description

Usage

Arguments