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Sequence analysis is being more and more widely used for the analysis ofsocial sequences and other multivariate categorical time series data. However,it is often complex to describe, visualize, and compare large sequence data,especially when there are multiple parallel sequences per subject. Hidden(latent) Markov models (HMMs) are able to detect underlying latent structuresand they can be used in various longitudinal settings: to account formeasurement error, to detect unobservable states, or to compress informationacross several types of observations. Extending to mixture hidden Markov models(MHMMs) allows clustering data into homogeneous subsets, with or withoutexternal covariates.  The seqHMM package in R is designed for the efficient modeling of sequencesand other categorical time series data containing one or multiple subjects withone or multiple interdependent sequences using HMMs and MHMMs. Also otherrestricted variants of the MHMM can be fitted, e.g., latent class models,Markov models, mixture Markov models, or even ordinary multinomial regressionmodels with suitable parameterization of the HMM. Good graphical presentationsof data and models are useful during the whole analysis process from the firstglimpse at the data to model fitting and presentation of results. The packageprovides easy options for plotting parallel sequence data, and proposesvisualizing HMMs as directed graphs.

Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R