RRMLRfMC: Reduced-Rank Multinomial Logistic Regression for Markov Chains

Fit the reduced-rank multinomial logistic regression model for Markov chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial logistic regression in Markov chains and reduced-rank. It is very useful in a study where multi-states model is assumed and each transition among the states is controlled by a series of covariates. The key advantage is to reduce the number of parameters to be estimated. The final coefficients for all the covariates and the p-values for the interested covariates will be reported. The p-values for the whole coefficient matrix can be calculated by two bootstrap methods.

Version: 0.4.0
Depends: R (≥ 3.5.0)
Imports: nnet
Suggests: rmarkdown, knitr
Published: 2021-06-07
DOI: 10.32614/CRAN.package.RRMLRfMC
Author: Pei Wang [aut, cre], Richard Kryscio [aut]
Maintainer: Pei Wang <wangp33 at miamioh.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: RRMLRfMC results


Reference manual: RRMLRfMC.pdf


Package source: RRMLRfMC_0.4.0.tar.gz
Windows binaries: r-devel: RRMLRfMC_0.4.0.zip, r-release: RRMLRfMC_0.4.0.zip, r-oldrel: RRMLRfMC_0.4.0.zip
macOS binaries: r-release (arm64): RRMLRfMC_0.4.0.tgz, r-oldrel (arm64): RRMLRfMC_0.4.0.tgz, r-release (x86_64): RRMLRfMC_0.4.0.tgz, r-oldrel (x86_64): RRMLRfMC_0.4.0.tgz


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