seqHMM-package: The seqHMM package

In seqHMM: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

The seqHMM package

Description

The seqHMM package is designed for fitting hidden (or latent) Markov models (HMMs) and mixture hidden Markov models (MHMMs) for social sequence data and other categorical time series. The package supports models for one or multiple subjects with one or multiple interdependent 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 easy plotting of multichannel sequences and hidden Markov models. Common restricted versions of (M)HMMs are also supported, namely Markov models, mixture Markov models, and latent class models.

Details

Maximum likelihood estimation via the EM algorithm and direct numerical maximization with analytical gradients is supported. All main algorithms are written in C++. Parallel computation is implemented via OpenMP for pre-2.0.0 functions, while estimation of non-homogenous models support parallelization via future package by parallelization of multistart optimizations and bootstrap sampling.

Author(s)

Maintainer: Jouni Helske [e-mail] (ORCID)

Authors:

• Satu Helske (ORCID)

References

Helske S. and Helske J. (2019). Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R, Journal of Statistical Software, 88(3), 1-32. doi:10.18637/jss.v088.i03

See Also

Useful links:

• Report bugs at https://github.com/helske/seqHMM/issues

seqHMM documentation built on June 8, 2025, 10:16 a.m.