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 a categorical 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. 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: 0.1.1
Depends: R (≥ 3.5.0)
Imports: MCMCpack, mvtnorm, stats, Rdpack
Suggests: knitr, rmarkdown, alluvial, grDevices, RColorBrewer, testthat (≥ 2.1.0)
Published: 2019-10-30
Author: Emmeke Aarts [aut, cre]
Maintainer: Emmeke Aarts <e.aarts at>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: mHMMbayes results


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


Package source: mHMMbayes_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mHMMbayes_0.1.1.tgz, r-release (x86_64): mHMMbayes_0.1.1.tgz, r-oldrel: mHMMbayes_0.1.1.tgz
Old sources: mHMMbayes archive


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