Package news (see below for general description of functionality)
depmixS4 version 1.3-0 has been released on CRAN. See the NEWS file
for an overview of all changes. The most important user-visible
1) more compact pretty-printing of parameters in print/summary of
(dep)mix objects (following lm/glm style of presenting results)
2) some speed improvements in the EM algorithm, most notable in large
3) EM has an optional argument to use the classification likelihood
instead of the usual likelihood; this can be useful as a means of
starting value generation; use with caution as results are often
Best, happy mixing, Ingmar & Maarten
Package general information
depmixS4 is a framework for specifying and fitting dependent mixture
models, otherwise known as hidden or latent Markov models.
Optimization is done with the EM algorithm or optionally with Rdonlp2
when (general linear (in-)equality) constraints on the parameters need
to be incorporated. Models can be fitted on (multiple) sets of
observations. The response densities for each state may be chosen
from the GLM family, or a multinomial. User defined response
densities are easy to add; for the latter an example is given for the
ex-gauss distribution as well as the multivariate normal distribution.
Mixture or latent class (regression) models can also be fitted; these
are the limit case in which the length of observed time series is 1
for all cases.