Package: LLAhclust
Version: 0.2-2
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: ORPHANED
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
URL: http://www.stat.auckland.ac.nz/~ivan/LLAhclust
Packaged: Fri Feb 27 08:33:40 2009; hornik
X-CRAN-Comment: Orphaned on 2012-06-03 as requested by the original
maintainer, ivan.kojadinovic@univ-pau.fr
Repository: CRAN
Date/Publication: 2009-02-27 07:40:31

Title: Logit Leaf Model Classifier for Binary Classification
Authors@R: c(person("Arno", "De Caigny",role = c("aut", "cre"),
person("Kristof", "Coussement", role = c("aut"),
person("Koen W.", "De Bock", role = c("aut"),
Maintainer: Arno De Caigny <a.de-caigny@ieseg.fr>
Description: Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).
Imports: partykit, stats, stringr, RWeka

Imports: partykit, stats, stringr, RWeka, survey, reghelper, scales

Author: Charles Bouveyron & Hugh Chipman
Description: This is a new framework in which graph-structured data are used to train a classifier in a latent space, and then classify new nodes. During the learning phase, a latent representation of the network is first learned and a supervised classifier is then built in the learned latent space. In order to classify new nodes, the positions of these nodes in the learned latent space are estimated using the existing links between the new nodes and the learning set nodes.  It is then possible to apply the supervised classifier to assign each new node to one of the classes.
Packaged: Mon Aug 27 12:23:32 2007; charles