Package: hopach
Version: 1.4.1
Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)
Author: Katherine S. Pollard <kpollard@wald.ucdavis.edu>, with
Mark J. van der Laan <laan@stat.berkeley.edu>.
Maintainer: Katherine S. Pollard <kpollard@wald.ucdavis.edu>
Depends: R (>= 1.8.0), cluster
Description: The HOPACH clustering algorithm builds a hierarchical tree
of clusters by recursively partitioning a data set, while ordering
and possibly collapsing clusters at each level. The algorithm uses
the Mean/Median Split Silhouette (MSS) criteria to identify the
level of the tree with maximally homogeneous clusters. It also runs
the tree down to produce a final ordered list of the elements. The
non-parametric bootstrap allows one to estimate the probability that
each element belongs to each cluster (fuzzy clustering).
License: GPL version 2 or newer
URL: http://www.r-project.org,
http://www.stat.berkeley.edu/~laan/,
http://lowelab.ucsc.edu/katie/
Packaged: Wed Apr  5 13:39:16 2006; kpollard

Package: hopach
Version: 1.4.3
Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)
Author: Katherine S. Pollard <kpollard@wald.ucdavis.edu>, with
Mark J. van der Laan <laan@stat.berkeley.edu>.
Maintainer: Katherine S. Pollard <kpollard@wald.ucdavis.edu>
Depends: R (>= 1.8.0), cluster
Description: The HOPACH clustering algorithm builds a hierarchical tree
of clusters by recursively partitioning a data set, while ordering
and possibly collapsing clusters at each level. The algorithm uses
the Mean/Median Split Silhouette (MSS) criteria to identify the
level of the tree with maximally homogeneous clusters. It also runs
the tree down to produce a final ordered list of the elements. The
non-parametric bootstrap allows one to estimate the probability that
each element belongs to each cluster (fuzzy clustering).
License: GPL version 2 or newer
URL: http://www.r-project.org,
http://www.stat.berkeley.edu/~laan/,
http://docpollard.com
Packaged: Sun Nov 26 17:12:46 2006; kpollard

Package: hopach
Version: 2.0.0
Title: Hierarchical Ordered Partitioning and Collapsing Hybrid
(HOPACH)
Author: Katherine S. Pollard, with Mark J. van der Laan <laan@stat.berkeley.edu> and Greg Wall
Maintainer: Katherine S. Pollard <kpollard@wald.ucdavis.edu>
Depends: R (>= 2.6.0), cluster, Biobase, methods
Description: The HOPACH clustering algorithm builds a hierarchical
tree of clusters by recursively partitioning a data set, while
ordering and possibly collapsing clusters at each level. The
algorithm uses the Mean/Median Split Silhouette (MSS) criteria
to identify the level of the tree with maximally homogeneous
clusters. It also runs the tree down to produce a final
ordered list of the elements. The non-parametric bootstrap
allows one to estimate the probability that each element
belongs to each cluster (fuzzy clustering).
License: GPL version 2 or newer
URL: http://www.r-project.org, http://www.stat.berkeley.edu/~laan/,
http://docpollard.com/
biocViews: Clustering
Packaged: Wed Jul 23 17:27:38 2008; kpollard