JointAI: Joint Analysis and Imputation of Incomplete Data

Provides joint analysis and imputation of (generalized) linear and cumulative logit regression models, (generalized) linear and cumulative logit mixed models and parametric (Weibull) as well as Cox proportional hazards survival models with incomplete (covariate) data in the Bayesian framework. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <> with the help of the package 'rjags'. It also provides summary and plotting functions for the output and allows to export imputed values.

Version: 0.5.2
Depends: rjags (≥ 4-6)
Imports: MASS, mcmcse, coda, rlang, foreach, doParallel
Suggests: knitr, rmarkdown, foreign, ggplot2, ggpubr, testthat
Published: 2019-06-06
Author: Nicole S. Erler ORCID iD [aut, cre]
Maintainer: Nicole S. Erler <n.erler at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
SystemRequirements: JAGS (
Language: en-US
Materials: README NEWS
In views: MissingData
CRAN checks: JointAI results


Reference manual: JointAI.pdf
Vignettes: After fitting
MCMC settings
Minimal Example
Model Specification
Parameter Selection
Visualizing Incomplete Data
Package source: JointAI_0.5.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: JointAI_0.5.2.tgz, r-oldrel: JointAI_0.5.2.tgz
Old sources: JointAI archive

Reverse dependencies:

Reverse enhances: mdmb


Please use the canonical form to link to this page.