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K-Means for Longitudinal Data

Installation

About

An implementation of k-means specifically design to cluster longitudinal data. It provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical interface for choosing the 'best' number of clusters.

Citation kml citation info
www.r-project.org

Key Metrics

Version 2.4.6.1
Published 2023-12-13 137 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks kml results

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Maintainer

Maintainer

Christophe Genolini

christophe.genolini@u-paris10.fr

Authors

Christophe Genolini

cre / aut

Bruno Falissard

ctb

Patrice Kiener

ctb

Material

NEWS
Reference manual
Package source

In Views

Cluster

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

kml archive

Depends

methods
clv
longitudinalData ≥ 2.4

Reverse Depends

kml3d

Reverse Suggests

latrend