Depends: R (>= 3.5.0), SparseM (>= 1.78), methods, Matrix, data.table
Suggests: rmarkdown, pROC (>= 1.16.2), knitr, utils, caret, ggplot2

Title: Hierarchical clustering of variables or objects based on the likelihood linkage analysis method
Author: Ivan Kojadinovic, Isra�l-C�sar Lerman, Philippe Peter
Description:  The likelihood linkage analysis is a general agglomerative hierarchical clustering method developed in France by Lerman in a long series of research articles and books. Initially proposed in the framework of variable clustering, it has been progressively extended to allow the clustering of very general object descriptions. The approach mainly consists in replacing the value of the estimated similarity coefficient by the probability of finding a lower value under the hypothesis of 'absence of link'. The package LLAhclust contains routines for computing various types of probablistic similarity coefficients between variables or object descriptions. Once the similarity values between variables/objects are computed, a hierarchical clustering can be performed using several probabilistic and non-probabilistic aggregation criteria, and indices measuring the quality of the partitions compatible with the resulting hierarchy can be computed.
License: CeCILL 2 (GNU GPL 2 compatible)
Packaged: Wed Jul 11 20:01:50 2007; ikojadin

Package: LLAhclust
Version: 0.2-1
Date: 2007-31-08
Title: Hierarchical clustering of variables or objects based on the
likelihood linkage analysis method
Author: Ivan Kojadinovic, Isra�l-C�sar Lerman, Philippe Peter
Maintainer: Ivan Kojadinovic <ivan@stat.auckland.ac.nz>
Description: The likelihood linkage analysis is a general agglomerative
hierarchical clustering method developed in France by Lerman in
a long series of research articles and books. Initially
proposed in the framework of variable clustering, it has been
progressively extended to allow the clustering of very general
object descriptions. The approach mainly consists in replacing
the value of the estimated similarity coefficient by the
probability of finding a lower value under the hypothesis of
'absence of link'. The package LLAhclust contains routines for
computing various types of probablistic similarity coefficients
between variables or object descriptions. Once the similarity
values between variables/objects are computed, a hierarchical
clustering can be performed using several probabilistic and
non-probabilistic aggregation criteria, and indices measuring
the quality of the partitions compatible with the resulting
hierarchy can be computed.
Depends: R(>= 2.1.0)
Encoding: latin1
License: CeCILL 2 (GNU GPL 2 compatible)
URL: http://www.stat.auckland.ac.nz/~ivan/LLAhclust
Packaged: Fri Aug 31 11:42:11 2007; ivan