sample_mvnorm

Sample random values from the multivariate normal distribution

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 

sample_mvnorm function

<dl><dt>mu</dt>
<dd>mean vector</dd>
<dt>sigma</dt>
<dd>covariance matrix
Samples multivariate normal variables by multiplying
univariate random normal variables by the cholesky
decomposition of the covariance matrix.
If mu is length 1 then just uses rnorm instead.</dd></dl>

Arguments

Sample random values from the multivariate normal distribution — sample_mvnorm

<dl>

<dt>mu</dt>
<dd>mean vector</dd>


<dt>sigma</dt>
<dd>covariance matrix
Samples multivariate normal variables by multiplying
univariate random normal variables by the cholesky
decomposition of the covariance matrix.
If mu is length 1 then just uses rnorm instead.</dd>

</dl>

sample_mvnorm: Sample random values from the multivariate normal distribution

Description

Usage

Arguments