CRAN/E | kml3d

kml3d

K-Means for Joint Longitudinal Data

Installation

About

An implementation of k-means specifically design to cluster joint trajectories (longitudinal data on several variable-trajectories). Like 'kml', 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. In addition, the 3D graph representing the mean joint-trajectories of each cluster can be exported through LaTeX in a 3D dynamic rotating PDF graph.

Citation kml3d citation info
www.r-project.org

Key Metrics

Version 2.4.6.1
R ≥ 2.10
Published 2023-12-13 129 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks kml3d results

Downloads

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Maintainer

Maintainer

Christophe Genolini

christophe.genolini@u-paris10.fr

Authors

Christophe Genolini

cre / aut

Bruno Falissard

ctb

Patrice Kiener

ctb

Jean-Baptiste Pingault

ctb

Material

NEWS
Reference manual
Package source

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

kml3d archive

Depends

R ≥ 2.10
methods
clv
rgl
misc3d
longitudinalData ≥2.4.2
kml ≥ 2.4.1