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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, 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'.
Getting started
Browse package contents
Package details |
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| Author | Nicole S. Erler [aut, cre] (<https://orcid.org/0000-0002-9370-6832>) |
| Maintainer | Nicole S. Erler <n.erler@erasmusmc.nl> |
| License | GPL (>= 2) |
| Version | 1.0.5 |
| URL | https://nerler.github.io/JointAI/ |
| Package repository | View on CRAN |
| Installation |
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