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Title: The mHMMbayes R package for fitting mixed hidden Markov models using Bayesian estimation Authors:  Emmeke Aarts - Utrecht University (Netherlands) [presenting]
Abstract: The mixed hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, tailored to accommodate (intense) sequential data of multiple individuals or objects simultaneously. Using a mixed (also known as multilevel) framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The package mHMMbayes a useful tool to estimate mixed HMMs in the programming language R, and the only CRAN package that allows fitting such and fixed Bayesian HMM models. The model has a great potential of application in many fields, such as the social sciences and medicine. 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 automated visualizations of the fitted model, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.