Maintainer: Christophe M. Genolini <genolini@u-paris10.fr>
Description: K-Means for Longitudinal data (kml) is a non parametric algorithm for clusterizing longitudinal data.
Collate: function.r partition.r longData.r imputation.r artificialLongData.r clusterization.r clusterizLongData.r
Packaged: Tue Jun 17 20:57:01 2008; Christophe

Description: K-Means for Longitudinal data (KmL) is a version of k-means for longitudinal able to deal with missing value and proposing a graphical interphace for cloosing the correct number of clusters.
Collate: global.r function.r longData.r partition.r clusterization.r longData-Partition.r imputation.r clusterizLongData.r artificialLongData.r
Packaged: Thu Feb 19 18:57:53 2009; Christophe

Title: K-Means for Longitudinal Data (KmL)
Description: KmL is an implementation of k-means specificaly design to
deal with longitudinal data. It provide facilities to deal with
missing value and propose a graphical interphace for chosing
Collate: global.r clusterization.r clusterizLongData.r kml.r
Packaged: 2009-11-12 22:49:10 UTC; Christophe

Collate: global.r clusterization.r clusterizLongData.r kml.r clean.r
Packaged: 2010-03-01 10:22:13 UTC; Christophe

Packaged: 2010-05-03 20:07:10 UTC; Christophe

Packaged: 2011-07-28 10:08:26 UTC; Christophe

Packaged: 2010-02-18 10:50:54 UTC; Christophe

missing value, compute several quality criterion (Calinski and
Harabatz, Ray and Turie, Davies and Bouldin, ...) and propose
various a graphical interphace for chosing the 'best' number of
Collate: global.r clusterLongData.r parKml.r parChoice.r kml.r
Packaged: 2012-03-28 08:42:10 UTC; GENOLLINI

Description: KmL is an implementation of k-means specifically design to
cluster longitudinal data. It provides facilities to deal with
Harabatz, Ray and Turie, Davies and Bouldin, BIC, ...) and
propose a graphical interface for choosing the 'best' number of
Packaged: 2012-11-19 07:11:02 UTC; GENOLLINI
Author: Christophe Genolini [cre, aut], Bruno Falissard [ctb]

Packaged: 2012-08-20 15:36:43 UTC; GENOLLINI