Imports: desc, dplyr (>= 0.7.7), fs, ggplot2 (>= 3.1.0), Hmisc (>=

Suggests: covr (>= 3.1.0), spelling, survival, testthat (>= 3.0.0),

Description: Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data
Packaged: Wed Jun  1 14:56:12 2005; ivisser

Packaged: Wed Jul 12 18:42:03 2006; ivisser

Description: 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

Description: 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.

Description: 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.

Packaged: Sun Oct 28 10:32:54 2007; hornik

Packaged: Mon Nov 26 10:29:03 2007; ivisser

Packaged: Thu Aug  7 12:47:50 2008; ivisser

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

Author: Ingmar Visser <i.visser@uva.nl>, Maarten Speekenbrink <m.speekenbrink@ucl.ac.uk>
Description: Fit latent (hidden) Markov models on mixed categorical and continuous (timeseries)
data, otherwise known as dependent mixture models
Packaged: Wed Mar 26 04:34:43 2008; theussl

Packaged: Tue Jun 17 04:49:03 2008; theussl

Depends: R (>= 2.6.0), nnet, methods, MASS
Packaged: Thu Jul  3 04:40:08 2008; theussl

Depends: R (>= 2.8.1), nnet, methods, MASS
Packaged: Mon Feb 23 05:08:40 2009; rforge

Author: Ingmar Visser <i.visser@uva.nl>, Maarten Speekenbrink
Description: Fit latent (hidden) Markov models on mixed categorical and
continuous (timeseries) data, otherwise known as dependent