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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences. University of Oxford, United Kingdom; University of Jyväskylä, Finland.ORCID iD: 0000-0003-0532-0153
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. University of Jyväskylä, Finland.ORCID iD: 0000-0001-7130-793x
2019 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 88, no 3, p. 32p. 1-32Article in journal (Refereed) Published
Abstract [en]

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.less thanbr /greater thanComment: 33 pages, 8 figures

Place, publisher, year, edition, pages
Alexandria, VA, United States: American Statistical Association , 2019. Vol. 88, no 3, p. 32p. 1-32
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-154355DOI: 10.18637/jss.v088.i03ISI: 000457019000001OAI: oai:DiVA.org:liu-154355DiVA, id: diva2:1286495
Available from: 2019-02-07 Created: 2019-02-07 Last updated: 2019-03-07Bibliographically approved

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Helske, SatuHelske, Jouni

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