FITTING HIDDEN MARKOV MODELS IN R

depmixS4 provides a framework for specifying and fitting hidden Markov
models.  The observation densities use an interface to the glm
distributions, most of which are now implemented.  The vignette that
accompanies the package has a table with available response distributions
Besides these, observations can be modelled using the multinomial
distribution with identity or logistic link function.  The latter provides
functionality for multinomial logistic responses with covariates.  The
transition matrix and the initial state probabilities are also modeled as
multinomial logistics (or multinomials with identity link, which is the
default when no covariates are present) with the possibility of including
covariates.

Optimization is by default done using the EM algorithm.  When (linear)
constraints are included, package Rsolnp is used for optimization (there is
also support for using Rdonlp2 as optimizer, see USING RDONLP2 below).  New
response distributions can be added by extending the response-class and
writing appropriate methods for it (dens, and getpars and setpars); an
example of this is provided on the ?makeDepmix help page.  depmixS4 also
fits latent class and mixture models, see ?mix for an example. 

The latest development versions of depmixS4 (and depmix) can be found at: 
https://r-forge.r-project.org/projects/depmix/


FOR DEPMIX USERS

depmixS4 is a completely new implementation of the depmix package using S4
classes.  Model specification now uses formulae and family objects,
familiar from the lm and glm functions.  Moreover, the transition matrix
and the initial state probabilities (as well as multinomial responses) are
now modeled as multinomial logistics with a baseline.  Specification of
linear constraints uses the same mechanism as was used in depmix, with the
only difference that constraints are passed as arguments to the fit
function rather than the model specification function.  See the help files
for further details. NOTE: most of the functionality of depmix is now 
also provided in depmixS4; in the future therefor I may stop updating 
depmix. 


USING RDONLP2

Optimization of models with (general) linear (in-)equality constraint is
done using Rsolnp (available from CRAN).  Optionally the Rdonlp2 package
can be used; Rdonlp2 was written by Ryuichi Tamura(ry.tamura @ gmail.com),
and can currently be installed using:

install.packages("Rdonlp2", repos= c("http://R-Forge.R-project.org", getOption("repos")))

Note the licence information which says, among other things: "The free use
of donlp2 and parts of it is restricted for research purposes ..."