
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.  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) with the possibility of including
covariates.  Optimization is by default done by the EM algorithm.  When
linear constraints are included, package Rsolnp is used for optimization
(there is also support for using Rdonlp2 as optimizer, see details 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.

The latest development version of 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.


USING RDONLP2

Optimization of models with (general) linear (in-)equality constraints can
be done using the Rdonlp2 package, written Ryuichi Tamura(ry.tamura @
gmail.com), which is available from: http://arumat.net/Rdonlp2/

Optimization with Rdonlp2 is automatically selected when constraints are
specified in the fit function.
