Packaged: 2012-11-19 07:11:06 UTC; GENOLLINI

Packaged: 2012-08-20 15:39:03 UTC; GENOLLINI

to cluster joint trajectories (longitudinal data on
Like KmL, it provides facilities to deal with missing
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: methods,clv,rgl,misc3d,longitudinalData(>= 2.2), kml(>= 2.2)
Packaged: 2014-03-13 18:12:05 UTC; GENOLLINI

Title: K-Means for Joint Longitudinal Data
Like 'kml', it provides facilities to deal with missing
Depends: methods,clv,rgl,misc3d,longitudinalData(>= 2.3), kml(>= 2.3)
Packaged: 2015-05-20 15:34:14 UTC; Christophe

Depends: methods,clv,rgl,misc3d,longitudinalData(>= 2.4), kml(>= 2.4)
Packaged: 2016-02-16 14:46:24 UTC; Christophe

Packaged: 2015-08-17 15:16:39 UTC; Christophe

Title: K-Means for Longitudinal Data using Shape-Respecting Distance
Description: K-means for longitudinal data using shape-respecting distance and shape-respecting means.
Collate: global.R plot.R clds.R reduceTraj.R distanceFrechet.R
Packaged: 2016-03-04 21:25:18 UTC; Christophe

Title: Clustering longitudinal data using the likelihood as a metric of
Authors@R: c(person("Mamoun O.", "Benghezal", role = c("aut", "cre"), email =
"mobenghezal@gmail.com"), person("Christophe", "Genolini", role = c("ctb"),
Description: 'kmlcov' Cluster longitudinal data using the likelihood as a
metric of distance. The generalised linear model allow the user to
introduce covariates with different level effects (2 levels).
Maintainer: Mamoun O. Benghezal <mobenghezal@gmail.com>

Title: K-Means with Simultaneous Outlier Detection
Authors@R: c(person("David Charles", "Howe", role = c("aut", "cre"),
Maintainer: David Charles Howe <kmodR@edgecondition.com>
Description: An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, "Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.