This package is considered a duplicate. The official version of this package is found at:https://henryleongstat.r-universe.dev/regmhmm

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-4.7-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://cran.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

2.70 score 274 downloads 13 exports 13 dependencies

Last updated from:354515cb38. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	185
linux-devel-x86_64	OK	185
source / vignettes	OK	283
linux-release-arm64	OK	181
linux-release-x86_64	OK	183
wasm-release	OK	192

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

Dependencies:codetools foreach glmnet glmnetUtils iterators lattice MASS Matrix Rcpp RcppArmadillo RcppEigen shape survival

regmhmm

Rendered fromregmhmm.Rmdusingknitr::rmarkdown

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

Citation

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
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

Package: regmhmm 1.0.0

regmhmm: 'regmhmm' Fits Hidden Markov Models with Regularization

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)