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) . 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 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.