Package: regmhmm 1.0.0

Man Chong Leong

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).

Authors:Man Chong Leong [cre, aut]

regmhmm_1.0.0.tar.gz
regmhmm_1.0.0.tar.gz(r-4.5-noble)regmhmm_1.0.0.tar.gz(r-4.4-noble)
regmhmm_1.0.0.tgz(r-4.4-emscripten)regmhmm_1.0.0.tgz(r-4.3-emscripten)
regmhmm.pdf |regmhmm.html✨
regmhmm/json (API)

# Install 'regmhmm' in R:
install.packages('regmhmm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/henryleongstat/regmhmm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

2.70 score 147 downloads 13 exports 13 dependencies

Last updated 11 months agofrom:354515cb38. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKSep 30 2024
R-4.5-linux-x86_64OKSep 30 2024

Exports:backwardcompute_joint_statecompute_loglikelihoodcompute_stateforwardforward_backwardHMMHMM_C_rawHMM_one_stepIRLS_EMrHMMrHMM_one_stepsimulate_HMM_data

Dependencies:codetoolsforeachglmnetglmnetUtilsiteratorslatticeMASSMatrixRcppRcppArmadilloRcppEigenshapesurvival

regmhmm

Rendered fromregmhmm.Rmdusingknitr::rmarkdownon Sep 30 2024.

Last update: 2023-12-05
Started: 2023-12-05