%0 Journal Article %T Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R %A Jared O'Connell %A S£¿ren H£¿jsgaard %J Journal of Statistical Software %D 2011 %I University of California, Los Angeles %X 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. %K duration density %K EM algorithm %K hidden Markov model %K R %K sojourn time %K Viterbi algorithm %U http://www.jstatsoft.org/v39/i04/paper