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.

Page views:: 9481. Submitted: 2009-03-02. Published: 2011-03-09.
Paper: Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R     Download PDF (Downloads: 13700)
Supplements:
mhsmm_0.4.0.tar.gz: R source package Download (Downloads: 655; 318KB)
v39i04.R: R example code from the paper Download (Downloads: 799; 9KB)
DOI: 10.18637/jss.v039.i04

by
This work is licensed under the licenses
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.