Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jared O'Connell, Søren Højsgaard
Title: Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R
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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Paper: Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R     Download PDF (Downloads: 13214)
Supplements:
mhsmm_0.4.0.tar.gz: R source package Download (Downloads: 649; 318KB)
v39i04.R: R example code from the paper Download (Downloads: 782; 9KB)
DOI: 10.18637/jss.v039.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.