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, 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.