mHMMbayes: Multilevel Hidden Markov Models Using Bayesian Estimation

An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with either a categorical or Normal distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. Missing data (NA) in the dependent variables is accommodated assuming MAR. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.

Version: 1.0.0
Depends: R (≥ 3.6.0)
Imports: MCMCpack, mvtnorm, stats, Rdpack, Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, alluvial, grDevices, RColorBrewer, testthat (≥ 2.1.0)
Published: 2023-10-02
Author: Emmeke Aarts [aut, cre], Sebastian Mildiner Moraga [ctb]
Maintainer: Emmeke Aarts <e.aarts at uu.nl>
BugReports: https://github.com/emmekeaarts/mHMMbayes/issues
License: GPL-3
URL: https://CRAN.R-project.org/package=mHMMbayes
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README NEWS
CRAN checks: mHMMbayes results

Documentation:

Reference manual: mHMMbayes.pdf
Vignettes: Estimation of the multilevel hidden Markov model
Multilevel HMM tutorial

Downloads:

Package source: mHMMbayes_1.0.0.tar.gz
Windows binaries: r-devel: mHMMbayes_1.0.0.zip, r-release: mHMMbayes_1.0.0.zip, r-oldrel: mHMMbayes_1.0.0.zip
macOS binaries: r-release (arm64): mHMMbayes_1.0.0.tgz, r-oldrel (arm64): mHMMbayes_1.0.0.tgz, r-release (x86_64): mHMMbayes_1.0.0.tgz
Old sources: mHMMbayes archive

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