[aut, cre]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' <http://mcmc-jags.sourceforge.net> with the help of the package 'rjags'. It also provides summary and plotting functions for the output and allows the user to export imputed values.
| Version: | 0.6.1 |
| Depends: | rjags (≥ 4-6) |
| Imports: | MASS, mcmcse, coda, rlang, foreach, doParallel |
| Suggests: | knitr, rmarkdown, bookdown, foreign, ggplot2, ggpubr, survival, testthat |
| Published: | 2020-02-12 |
| 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 (http://mcmc-jags.sourceforge.net) |
| Language: | en-US |
| Citation: | JointAI citation info |
| 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 Theoretical Background Visualizing Incomplete Data |
| Package source: | JointAI_0.6.1.tar.gz |
| Windows binaries: | r-devel: JointAI_0.6.1.zip, r-release: JointAI_0.6.1.zip, r-oldrel: JointAI_0.6.1.zip |
| macOS binaries: | r-release: JointAI_0.6.1.tgz, r-oldrel: JointAI_0.6.1.tgz |
| Old sources: | JointAI archive |
| Reverse enhances: | mdmb |
Please use the canonical form https://CRAN.R-project.org/package=JointAI to link to this page.