kml3d: K-Means for Joint Longitudinal Data

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.

Version: 2.3
Depends: methods, clv, rgl, misc3d, longitudinalData (≥ 2.3), kml (≥ 2.3)
Published: 2015-05-21
Author: Christophe Genolini [cre, aut], Bruno Falissard [ctb], Jean-Baptiste Pingault [ctb]
Maintainer: Christophe Genolini <christophe.genolini at u-paris10.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http:www.r-project.org
NeedsCompilation: no
Citation: kml3d citation info
In views: Cluster
CRAN checks: kml3d results

Downloads:

Reference manual: kml3d.pdf
Package source: kml3d_2.3.tar.gz
Windows binaries: r-devel: kml3d_2.3.zip, r-release: kml3d_2.3.zip, r-oldrel: kml3d_2.3.zip
OS X Snow Leopard binaries: r-oldrel: kml3d_2.2.tgz
OS X Mavericks binaries: r-release: kml3d_2.3.tgz
Old sources: kml3d archive