regmhmm: 'regmhmm' Fits Hidden Markov Models with Regularization

Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).

Version: 1.0.0
Imports: glmnet, glmnetUtils, MASS, Rcpp, stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: covr, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-12-04
Author: Man Chong Leong ORCID iD [cre, aut]
Maintainer: Man Chong Leong <mc.leong26 at gmail.com>
BugReports: https://github.com/HenryLeongStat/regmhmm/issues
License: GPL (≥ 3)
URL: https://github.com/HenryLeongStat/regmhmm
NeedsCompilation: yes
Language: en-US
Materials: README
CRAN checks: regmhmm results

Documentation:

Reference manual: regmhmm.pdf
Vignettes: regmhmm

Downloads:

Package source: regmhmm_1.0.0.tar.gz
Windows binaries: r-prerel: regmhmm_1.0.0.zip, r-release: regmhmm_1.0.0.zip, r-oldrel: regmhmm_1.0.0.zip
macOS binaries: r-prerel (arm64): regmhmm_1.0.0.tgz, r-release (arm64): regmhmm_1.0.0.tgz, r-oldrel (arm64): regmhmm_1.0.0.tgz, r-prerel (x86_64): regmhmm_1.0.0.tgz, r-release (x86_64): regmhmm_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=regmhmm to link to this page.