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Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R

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  • O'Connell, Jared
  • Højsgaard, Søren

Abstract

This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov models only allow a geometrically distributed sojourn time in a given state, while hidden semi-Markov models extend this by allowing an arbitrary sojourn distribution. We demonstrate the software with simulation examples and an application involving the modelling of the ovarian cycle of dairy cows.

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  • O'Connell, Jared & Højsgaard, Søren, 2011. "Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i04).
    • Handle: RePEc:jss:jstsof:v:039:i04
    DOI: http://hdl.handle.net/10.18637/jss.v039.i04
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    References listed on IDEAS

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    1. Bulla, Jan & Bulla, Ingo & Nenadic, Oleg, 2010. "hsmm -- An R package for analyzing hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 611-619, March.
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