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.

Depends: R (≥ 2.10), methods, clv, rgl, misc3d, longitudinalData (≥ 2.4.2), kml (≥ 2.4.1)
Published: 2023-12-13
DOI: 10.32614/CRAN.package.kml3d
Author: Christophe Genolini [cre, aut], Bruno Falissard [ctb], Patrice Kiener [ctb], Jean-Baptiste Pingault [ctb]
Maintainer: Christophe Genolini <christophe.genolini at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: kml3d citation info
Materials: NEWS
CRAN checks: kml3d results


Reference manual: kml3d.pdf


Package source: kml3d_2.4.6.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): kml3d_2.4.6.1.tgz, r-oldrel (arm64): kml3d_2.4.6.1.tgz, r-release (x86_64): kml3d_2.4.6.1.tgz, r-oldrel (x86_64): kml3d_2.4.6.1.tgz
Old sources: kml3d archive


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