Depends: R (>= 2.13), utils, tools, stats
Description: Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.

Depends: R (>= 2.10), utils, tools, stats

Package: mvbutils
Version: 2.7.4
Author: Mark V. Bravington <mark.bravington@csiro.au>
Maintainer: Mark V. Bravington <mark.bravington@csiro.au>
Depends: R (>= 2.13), utils, tools, stats
Date: 22/2/2012
Title: Workspace organization, code and documentation editing, package
prep and editing, etc.
Description: Hierarchical workspace tree, code editing and backup, easy
package prep, editing of packages while loaded, per-object
lazy-loading, easy documentation, macro functions, and
miscellaneous utilities. Needed by debug package.
License: GPL (>= 2)
Packaged: 2013-02-04 00:03:10 UTC; bra381
Repository: CRAN
Date/Publication: 2013-02-07 13:35:37

Title: Workspace Organization, Code and Documentation Editing, Package

Description: An implementation of Multi-View Clustering (Bickel and
Scheffer, 2004). Documents currently only support words drawn
without replacement, i.e. the process of word selection is
binary. Thus, it implements Mixture of Binomials EM (as opposed
to the more general Mixture of Multinomials EM developed in the

Scheffer, 2004). Documents are generated by drawing word values
from a categorical distribution for each word, given the
cluster. This means words are not counted (multinomial, as in
the paper), but words take on different values from a finite
set of values (categorical). Thus, it implements Mixture of
Categoricals EM (as opposed to Mixture of Multinomials
developed in the paper), and Spherical k-Means. The latter
represents documents as vectors in the categorical space.

Description: An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.