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' <http://mcmc-jags.sourceforge.net/> with the help of the package 'rjags'.

Version: 1.0.1
Imports: rjags, mcmcse, coda, rlang, future, foreach, mathjaxr, survival, MASS
Suggests: knitr, rmarkdown, bookdown, foreign, ggplot2, ggpubr, testthat, covr, doFuture
Published: 2020-11-16
Author: Nicole S. Erler ORCID iD [aut, cre]
Maintainer: Nicole S. Erler <n.erler at erasmusmc.nl>
BugReports: https://github.com/nerler/JointAI/issues/
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://nerler.github.io/JointAI/
NeedsCompilation: no
SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net/)
Language: en-GB
Citation: JointAI citation info
Materials: README NEWS
In views: MissingData
CRAN checks: JointAI results

Downloads:

Reference manual: JointAI.pdf
Vignettes: After Fitting
MCMC Settings
Model Specification
Parameter Selection
Package source: JointAI_1.0.1.tar.gz
Windows binaries: r-devel: JointAI_1.0.1.zip, r-release: JointAI_1.0.1.zip, r-oldrel: JointAI_1.0.1.zip
macOS binaries: r-release: JointAI_1.0.1.tgz, r-oldrel: JointAI_1.0.1.tgz
Old sources: JointAI archive

Reverse dependencies:

Reverse enhances: mdmb

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