Package: regmhmm 1.0.0

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
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regmhmm_1.0.0.tgz(r-4.6-x86_64)regmhmm_1.0.0.tgz(r-4.6-arm64)regmhmm_1.0.0.tgz(r-4.5-x86_64)regmhmm_1.0.0.tgz(r-4.5-arm64)
regmhmm_1.0.0.tar.gz(r-4.7-arm64)regmhmm_1.0.0.tar.gz(r-4.7-x86_64)regmhmm_1.0.0.tar.gz(r-4.6-arm64)regmhmm_1.0.0.tar.gz(r-4.6-x86_64)
regmhmm_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
regmhmm/json (API)

# Install 'regmhmm' in R:

install.packages('regmhmm', repos = c('https://henryleongstat.r-universe.dev', 'https://cloud.r-project.org'))

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

On CRAN:

openblas cpp openmp

3.70 score 276 downloads 15 exports 22 dependencies

Last updated from:0a0de5ef1e. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	206
linux-devel-x86_64	NOTE	184
source / vignettes	OK	283
linux-release-arm64	NOTE	201
linux-release-x86_64	NOTE	199
macos-release-arm64	NOTE	272
macos-release-x86_64	NOTE	334
macos-oldrel-arm64	NOTE	214
macos-oldrel-x86_64	NOTE	311
windows-devel	NOTE	212
windows-release	NOTE	206
windows-oldrel	NOTE	171
wasm-release	OK	161

Exports:backward compute_joint_state compute_loglikelihood compute_state forward forward_backward HMM HMM_C_raw HMM_one_step IRLS_EM plot.HMM print.HMM rHMM rHMM_one_step simulate_HMM_data

Dependencies:cli codetools cpp11 foreach glmnet glmnetUtils glue igraph iterators lattice lifecycle magrittr MASS Matrix pkgconfig Rcpp RcppArmadillo RcppEigen rlang shape survival vctrs

regmhmm

Rendered fromregmhmm.Rmdusingknitr::rmarkdown

Last update: 2023-12-01
Started: 2023-12-01

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Probability Calculation using the Backward Algorithm for Hidden Markov Models	backward
Posterior Joint Probability Calculation for Hidden States in a Hidden Markov Model	compute_joint_state
Log-Likelihood Calculation for Hidden Markov Models (Forward Algorithm)	compute_loglikelihood
Posterior Probability Estimation for Hidden States in Hidden Markov Models	compute_state
Forward Algorithm for Probability Calculation in Hidden Markov Models	forward
Probability Calculation in Hidden Markov Models using Forward-Backward Algorithm	forward_backward
Fitting Hidden Markov Models using Expectation-Maximization (EM) Algorithm	HMM
Fit Hidden Markov Model (HMM)	HMM_C_raw
Single EM Iteration for Fitting Hidden Markov Models (HMM)	HMM_one_step
Iterative Reweighted Least Squares for the EM algorithm	IRLS_EM
Plot Hidden State Transitions in Hidden Markov Model (HMM)	plot.HMM
Print Outputs from a Hidden Markov Model (HMM)	print.HMM
Fit Regularized Hidden Markov Models (rHMM) with Modified CCD	rHMM
Single Iteration of EM Algorithm for Fitting Regularized Hidden Markov Model (rHMM)	rHMM_one_step
Simulate Hidden Markov Model (HMM) Data	simulate_HMM_data