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

Keywords: duration density , EM algorithm , hidden Markov model , R , sojourn time , Viterbi algorithm

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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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