Reduced-Rank Multinomial Logistic Regression for Markov Chains R package RRMLRfMC version 0.4.0 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. Software Depends: R (>= 3.5.0) Imports: nnet Suggests: rmarkdown, knitr Pei Wang <wangp33@miamioh.edu> Comprehensive R Archive Network (CRAN) Pei Wang [aut, cre], Richard Kryscio [aut] GPL-2 2021-06-07 application/tgz https://CRAN.R-project.org/package=RRMLRfMC doi:10.32614/CRAN.package.RRMLRfMC