Package: mix
Version: 1.0-5
Date: 2006-09-05
Title:  Estimation/multiple Imputation for Mixed Categorical and Continuous Data
Author: Original by Joseph L. Schafer <jls@stat.psu.edu>.
Maintainer: Brian Ripley <ripley@stats.ox.ac.uk>
Depends: stats
Description:  Estimation/multiple imputation programs for mixed categorical
and continuous data
License: The software may be distributed free of charge and
used by anyone if credit is given. It has been tested
fairly well, but it comes with no guarantees and the
authors assume no liability for its use or misuse.
URL: http://www.stat.psu.edu/~jls/misoftwa.html
Packaged: Tue Sep  5 13:49:56 2006; ripley

Package: mix
Version: 1.0-6
Date: 2007-06-15
Title:  Estimation/multiple Imputation for Mixed Categorical and Continuous Data
Author: Original by Joseph L. Schafer <jls@stat.psu.edu>.
Maintainer: Brian Ripley <ripley@stats.ox.ac.uk>
Depends: stats
Description:  Estimation/multiple imputation programs for mixed categorical
and continuous data
License: The software may be distributed free of charge and
used by anyone if credit is given. It has been tested
fairly well, but it comes with no guarantees and the
authors assume no liability for its use or misuse.
LazyData: yes
URL: http://www.stat.psu.edu/~jls/misoftwa.html
Packaged: Fri Jun 15 17:02:37 2007; ripley

Title: Estimation/multiple Imputation for Mixed Categorical and
Description: Estimation/multiple imputation programs for mixed

Title: Mixture of methods including mixtures
Description: This package contains a mixture of statistical methods
including the MCMC methods to analyze normal mixtures

Depends: R (>= 2.0.0), coda, colorspace, lme4

Depends: R (>= 2.0.0), colorspace, lme4, mnormt, coda

Depends: R (>= 2.0.0), splines, colorspace, lme4, mnormt, coda

Depends: R (>= 2.10.0), splines, colorspace, lme4, mnormt, coda, snow,
Description: This package contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures

Depends: R (>= 2.10.0), splines, colorspace, lme4, mnormt, coda,

Title: Multivariate normal mixture models and mixtures of generalized
linear mixed models including model based clustering
including the MCMC methods to analyze normal mixtures.
Additionally, model based clustering methods are implemented to
perform classification based on (multivariate) longitudinal (or
otherwise correlated) data. The basis for such clustering is a
mixture of multivariate generalized linear mixed models.