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, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. 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' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.
Version: 1.0.6 Imports: rjags, mcmcse, coda, rlang, future, mathjaxr, survival, MASS Suggests: knitr, rmarkdown, bookdown, foreign, ggplot2, ggpubr, testthat, covr Published: 2024-04-02 DOI: 10.32614/CRAN.package.JointAI Author: Nicole S. Erler [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 (https://mcmc-jags.sourceforge.io/) Language: en-GB Citation: JointAI citation info Materials: README NEWS In views: MissingData, MixedModels CRAN checks: JointAI results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=JointAI to link to this page.
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