JointAI: Joint Analysis and Imputation of Incomplete Data

Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data. Incomplete covariates, if present, are automatically imputed. 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'.

Version: 1.0.2
Imports: rjags, mcmcse, coda, rlang, future, foreach, mathjaxr, survival, MASS
Suggests: knitr, rmarkdown, bookdown, foreign, ggplot2, ggpubr, testthat, covr, doFuture
Published: 2021-01-13
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-GB
Citation: JointAI citation info
Materials: README NEWS
In views: MissingData
CRAN checks: JointAI results


Reference manual: JointAI.pdf
Vignettes: After Fitting
MCMC Settings
Model Specification
Parameter Selection


Package source: JointAI_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): JointAI_1.0.2.tgz, r-release (x86_64): JointAI_1.0.2.tgz, r-oldrel: JointAI_1.0.2.tgz
Old sources: JointAI archive

Reverse dependencies:

Reverse enhances: mdmb


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