Packaged: Thu Feb 19 14:22:53 2009; hornik

Author: Nema Dean <nd29c@stats.gla.ac.uk> and Adrian E. Raftery <raftery@stat.washington.edu>
Packaged: Tue Mar 10 22:39:48 2009; nema

Package: clustvarsel
Title: Variable Selection for Model-Based Clustering
Version: 1.3
Author: Nema Dean <nd29c@stats.gla.ac.uk> and Adrian E. Raftery
<raftery@stat.washington.edu>
Description: The selection method uses either a greedy search or
headlong search. The greedy search at each step either checks
all variables not currently included in the set of clustering
variables singly for inclusion into the set or checks all
variables in the set of clustering variables singly for
exclusion.The headlong search only checks until a variable is
included or excluded (i.e. does not necessarily check all
possible variables for inclusion/exclusion at each step) and
any variable with evidence of clustering below a certain level
at any stage is removed from consideration for the remainder of
the algorithm. Each variable's evidence for being useful to the
clustering given the currently selected clustering variables is
given by the difference between the BIC for the model with
clustering (allowed to vary over 2 to a maximum number of
groups and any of the different covariance parameterizations
allowed in mclust) using the set of clustering variables
including the variable being checked and the sum of BICs for
the model with clustering (allowed to vary over 2 to a maximum
number of groups and any of the different covariance
parameterizations allowed in mclust) using the set of
clustering variables without the variable being checked and the
model for the variable being checked being conditionally
independent of the clustering given the other clustering
variables (this is modeled as a regression of the variable
being checked on the other clustering variables).
Maintainer: Nema Dean <nd29c@stats.gla.ac.uk>
Depends: mclust
License: GPL
Packaged: Sun Aug 2 20:30:10 2009; nema
Repository: CRAN
Date/Publication: 2009-08-04 12:24:01

Description: A function which implements variable selection methodology for model-based clustering which allows to find the (locally) optimal subset of variables  in a dataset that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting Mclust models. By default the algorithm uses a sequential search, but parallelization is also available.
Author: Nema Dean, Adrian E. Raftery, and Luca Scrucca