Mixture Hidden Markov Models for Social Sequence Data and Other
Multivariate, Multichannel Categorical Time Series
Description
Designed for fitting hidden (latent) Markov models and mixture
hidden Markov models for social sequence data and other categorical time series.
Also some more restricted versions of these type of models are available: Markov
models, mixture Markov models, and latent class models. The package supports
models for one or multiple subjects with one or multiple parallel sequences
(channels). External covariates can be added to explain cluster membership in
mixture models. The package provides functions for evaluating and comparing
models, as well as functions for visualizing of multichannel sequence data and
hidden Markov models. Models are estimated using maximum likelihood via the EM
algorithm and/or direct numerical maximization with analytical gradients. All
main algorithms are written in C++ with support for parallel computation.
Documentation is available via several vignettes in this page, and the
paper by Helske and Helske (2019, ).