kml3d: K-Means for Joint Longitudinal Data (original) (raw)
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: | 2.5.0 |
|---|---|
| Depends: | R (≥ 2.10), methods, clv, rgl, misc3d, longitudinalData, kml |
| Published: | 2024-10-23 |
| 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 free.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | kml3d citation info |
| Materials: | |
| CRAN checks: | kml3d results |
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