is_valid_covariance_prior

This function checks if the specified covariance structure and prior combination
is valid.

internal

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>.

Craig Gower-Page

rbmi

Reference Based Multiple Imputation

Isaac Gravestock

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

is_valid_covariance_prior function

<dl><dt>prior_cov</dt>
<dd>A character string indicating the prior covariance type.</dd>
<dt>covariance</dt>
<dd>A character string indicating the covariance structure.</dd></dl>

Arguments

Check for Valid Covariance and Prior Combination — is_valid_covariance_prior

<dl>

<dt>prior_cov</dt>
<dd>A character string indicating the prior covariance type.</dd>


<dt>covariance</dt>
<dd>A character string indicating the covariance structure.</dd>

</dl>

is_valid_covariance_prior: Check for Valid Covariance and Prior Combination

Description

Usage

Value

Arguments