depmixS4: An R Package for Hidden Markov Models

Ingmar Visser and Maarten Speekenbrink

Journal of Statistical Software, 2010, vol. 036, issue i07

Abstract: depmixS4 implements a general framework for defining and estimating dependent mixture models in the R programming language. This includes standard Markov models, latent/hidden Markov models, and latent class and finite mixture distribution models. The models can be fitted on mixed multivariate data with distributions from the glm family, the (logistic) multinomial, or the multivariate normal distribution. Other distributions can be added easily, and an example is provided with the exgaus distribution. Parameters are estimated by the expectation-maximization (EM) algorithm or, when (linear) constraints are imposed on the parameters, by direct numerical optimization with the Rsolnp or Rdonlp2 routines.

Date: 2010-08-05
References: View complete reference list from CitEc
Citations: View citations in EconPapers (33)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v036i07/v36i07.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... epmixS4_1.0-0.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v036i07/v36i07.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:036:i07

DOI: 10.18637/jss.v036.i07

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().