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: |
2.4.6.1 |
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 u-paris10.fr> |
License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: |
https://www.r-project.org |
NeedsCompilation: |
no |
Citation: |
kml3d citation info |
Materials: |
NEWS |
CRAN checks: |
kml3d results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=kml3d
to link to this page.