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regmhmm  

'regmhmm' Fits Hidden Markov Models with Regularization
View on CRAN: Click here


Download and install regmhmm package within the R console
Install from CRAN:
install.packages("regmhmm")

Install from Github:
library("remotes")
install_github("cran/regmhmm")

Install by package version:
library("remotes")
install_version("regmhmm", "1.0.0")



Attach the package and use:
library("regmhmm")
Maintained by
Man Chong Leong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-12-04
Latest Update: 2023-12-04
Description:
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).
How to cite:
Man Chong Leong (2023). regmhmm: 'regmhmm' Fits Hidden Markov Models with Regularization. R package version 1.0.0, https://cran.r-project.org/web/packages/regmhmm
Previous versions and publish date:
No previous versions
Other packages that cited regmhmm R package
View regmhmm citation profile
Other R packages that regmhmm depends, imports, suggests or enhances
Functions, R codes and Examples using the regmhmm R package
Some associated functions: HMM . HMM_C_raw . HMM_one_step . IRLS_EM . backward . compute_joint_state . compute_loglikelihood . compute_state . forward . forward_backward . print.HMM . rHMM . rHMM_one_step . simulate_HMM_data . 
Some associated R codes: HMM.R . RcppExports.R . documentation.R . print_HMM.R . rHMM.R . rHMM_one_step.R . simulate_HMM_data.R .  Full regmhmm package functions and examples
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