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mHMMbayes
Multilevel Hidden Markov Models Using Bayesian Estimation
View on CRAN: Click
here
Download and install mHMMbayes package within the R console
Install from CRAN:
install.packages("mHMMbayes")
Install from Github:
library("remotes")
install_github("cran/mHMMbayes") Install by package version:
library("remotes")
install_version("mHMMbayes", "1.1.1") Attach the package and use:
library("mHMMbayes")
Maintained by
Emmeke Aarts
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-25
Latest Update: 2025-07-11
Description:
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) . The
multilevel HMM is tailored to accommodate (intense) longitudinal data of
multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al.
. 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, normal, or Poisson
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.
How to cite:
Emmeke Aarts (2019). mHMMbayes: Multilevel Hidden Markov Models Using Bayesian Estimation. R package version 1.1.1, https://cran.r-project.org/web/packages/mHMMbayes. Accessed 11 Aug. 2026.
Previous versions and publish date:
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Complete documentation for mHMMbayes
Functions, R codes and Examples using
the mHMMbayes R package
Some associated functions: int_to_prob . mHMM . mHMMbayes-package . nonverbal . nonverbal_cov . obtain_emiss . obtain_gamma . pd_RW_emiss_cat . pd_RW_gamma . plot.mHMM . plot.mHMM_gamma . prior_emiss_cat . prior_emiss_cont . prior_gamma . prob_to_int . sim_mHMM . vit_mHMM .
Some associated R codes: RcppExports.R . data.R . forward_prob.R . forward_prob_cpp.R . int_to_prob.R . logl_mnl.R . mHMM.R . mHMMbayes-package.R . mnl_RW_once.R . mnl_hess.R . obtain_emiss.R . obtain_gamma.R . pd_RW_emiss_cat.R . pd_RW_gamma.R . plot.mHMM.R . plot.mHMM_gamma.R . print.mHMM.R . print.mHMM_.R . prior_emiss_cat.R . prior_emiss_cont.R . prior_gamma.R . sim_mHMM.R . summary.mHMM.R . utility_func_mHMM.R . vit_mHMM.R . Full mHMMbayes package functions and examples
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