JointAI

Joint Analysis and Imputation of Incomplete Data

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 to export imputed values.

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Description file content

Package
JointAI
Version
0.5.1
Title
Joint Analysis and Imputation of Incomplete Data
Description
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' with the help of the package 'rjags'. It also provides summary and plotting functions for the output and allows to export imputed values.
URL
License
GPL (>= 2)
Date
2019-05-06
BugReports
https://github.com/nerler/JointAI/issues
LazyData
TRUE
RoxygenNote
6.1.1
Depends
rjags (>= 4-6)
Imports
MASS, mcmcse, coda, rlang, foreach, doParallel
SystemRequirements
JAGS (http://mcmc-jags.sourceforge.net)
Suggests
knitr, rmarkdown, foreign, ggplot2, ggpubr, testthat
VignetteBuilder
knitr
Encoding
UTF-8
Language
en-US
NeedsCompilation
no
Packaged
2019-05-07 06:36:10 UTC; 228015
Author
Nicole S. Erler [aut, cre] ()
Maintainer
Nicole S. Erler
Repository
CRAN
Date/Publication
2019-05-07 07:10:03 UTC

install.packages('JointAI')

0.5.1

13 days ago

https://nerler.github.io/JointAI

Nicole S. Erler

GPL (>= 2)

Depends on

rjags (>= 4-6)

Imports

MASS, mcmcse, coda, rlang, foreach, doParallel

Suggests

knitr, rmarkdown, foreign, ggplot2, ggpubr, testthat

Discussions