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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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.