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
