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

Documentation:

Reference manual:

RRMLRfMC.pdf

Downloads:

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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