R port by Kurt Hornik <Kurt.Hornik@ci.tuwien.ac.at> and Martin Maechler

Packaged: Thu Mar 11 23:56:18 2004; maechler

Packaged: Fri Jun 11 17:56:07 2004; maechler

Packaged: Fri Jun 18 16:29:18 2004; maechler

Packaged: Fri Jun 25 18:24:57 2004; maechler

R port by Kurt Hornik <Kurt.Hornik@R-project.org> and Martin Maechler
Packaged: Tue Aug  3 15:39:44 2004; maechler

Packaged: Fri Aug 20 23:42:12 2004; maechler

Packaged: Fri Jan 21 18:44:56 2005; maechler

Packaged: Mon Apr  4 19:03:47 2005; maechler

Description: Cluster analysis methods.  Much extended the original from

Title: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et
Description: Methods for Cluster analysis.  Much extended the original from
based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

SuggestsNote: two examples using MASS' cov.rob() and mvrnorm(); Matrix

,person("Mia", "Hubert", role="aut", email= "Mia.Hubert@uia.ua.ac.be",
,person("Erich",  "Schubert", role="ctb", comment = c("fastpam options for pam()",
SuggestsNote: MASS: two examples using cov.rob() and mvrnorm(); Matrix
Peter Rousseeuw [aut] (Fortran original,
Mia Hubert [aut] (S original, <https://orcid.org/0000-0001-6398-4850>),
Kurt Hornik [trl, ctb] (port to R; maintenance(1999-2000),
Erich Schubert [ctb] (fastpam options for pam(),

,person("Keefe",  "Murphy", role="ctb", comment = "volume.ellipsoid({d >= 3})")
Keefe Murphy [ctb] (volume.ellipsoid({d >= 3}))

Description: A collection of data sets for teaching cluster analysis.

Title: Calculate the consensus clustering result from re-sampled
clustering experiments with the option of using multiple
Author: Dr. T. Ian Simpson, University of Edinburgh
Maintainer: Dr. T. Ian Simpson <ian.simpson@ed.ac.uk>
Description: clusterCons is a package containing functions that
generate robustness measures for clusters and cluster
membership based on generating consensus matrices from
bootstrapped clustering experiments in which a random
proportion of rows of the data set are used in each individual
clustering. This allows the user to prioritise clusters and the
members of clusters based on their consistency in this regime.
The functions allow the user to select several algorithms to
use in the re-sampling scheme and with any of the parameters