Mixture Hidden Markov Models for Sequence Data : The seqHMM Package in R
Helske, S., & Helske, J. (2019). Mixture Hidden Markov Models for Sequence Data : The seqHMM Package in R. Journal of Statistical Software, 88, Article 3. https://doi.org/10.18637/jss.v088.i03
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Journal of Statistical SoftwareDate
2019Copyright
© 2019 the Authors
Sequence analysis is being more and more widely used for the analysis of social 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 structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates. The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM. Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.
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Foundation for Open Access StatisticsISSN Search the Publication Forum
1548-7660Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/28925314
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Research Council of FinlandFunding program(s)
Research costs of Academy Research Fellow, AoFAdditional information about funding
Satu Helske is grateful for support for this research from the John Fell Oxford University Press (OUP) Research Fund and the Department of Mathematics and Statistics at the University of Jyväskylä, Finland, and Jouni Helske for the Emil Aaltonen Foundation and the Academy of Finland (research grants 284513 and 312605).License
Except where otherwise noted, this item's license is described as Creative Commons Attribution 3.0 Unported License
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