Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data

Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge: https://r-forge.r-project.org/R/?group_id=156

Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge.

Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The 'Rdonlp2' package can optionally be used for optimization of the log-likelihood and is available from R-forge. See Visser et al. (2009, <DOI:10.1007/978-0-387-95922-1_13>) for examples and applications.

Fit (multigroup) mixtures of latent Markov models on mixed
categorical and continuous (timeseries) data

Ingmar Visser <i.visser@uva.nl>, Maarten Speekenbrink <m.speekenbrink@ucl.ac.uk>
Fit latent (hidden) Markov models on mixed categorical and continuous (timeseries)
data, otherwise known as dependent mixture models

Ingmar Visser <i.visser@uva.nl>, Maarten Speekenbrink
<m.speekenbrink@ucl.ac.uk>
Fit latent (hidden) Markov models on mixed categorical and
continuous (timeseries) data, otherwise known as dependent
mixture models

R (>= 2.9.1), stats, nnet, methods, MASS, MCMCpack

R (>= 2.10.1), stats, nnet, methods, MASS, Rsolnp

Dependent Mixture Models - Hidden Markov Models of GLMs and
Other Distributions in S4
R (>= 2.11.1), stats, nnet, methods, MASS, Rsolnp

R (>= 2.12.2), stats, nnet, methods, MASS, Rsolnp, stats4

R (>= 2.13.1), stats, nnet, methods, MASS, Rsolnp, stats4

R (>= 2.14.2), stats, nnet, methods, MASS, Rsolnp, stats4

R (>= 2.15.0), stats, nnet, methods, MASS, Rsolnp, stats4

R (>= 2.15.2), stats, nnet, methods, MASS, Rsolnp, stats4

Fit latent (hidden) Markov models on mixed categorical and continuous (time series)
data, otherwise known as dependent mixture models