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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 |
DOI: | 10.32614/CRAN.package.regmhmm |
Author: | Man Chong Leong |
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 |
Reference manual: | regmhmm.pdf |
Vignettes: |
regmhmm |
Package source: | regmhmm_1.0.0.tar.gz |
Windows binaries: | r-devel: regmhmm_1.0.0.zip, r-release: regmhmm_1.0.0.zip, r-oldrel: regmhmm_1.0.0.zip |
macOS binaries: | r-release (arm64): regmhmm_1.0.0.tgz, r-oldrel (arm64): regmhmm_1.0.0.tgz, r-release (x86_64): regmhmm_1.0.0.tgz, r-oldrel (x86_64): regmhmm_1.0.0.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.
Health stats visible at Monitor.