KmL is an implementation of k-means specifically design to
cluster longitudinal data. 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.
Christophe Genolini [cre, aut], Bruno Falissard [ctb]

KmL is an implementation of k-means specifically design
to cluster longitudinal data. 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.
global.R clusterLongData.R parKml.R parChoice.R kml.R
Christophe Genolini [cre, aut],
Bruno Falissard [ctb]

An implementation of k-means specifically design
to cluster longitudinal data. 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.

KmL3D is an implementation of k-means specificaly design
to deal with joint longitudinal data (longitudinal data on
several variable). It provide facilities to deal with missing
value and propose a graphical interphace for chosing the
correct number of clusters.
global.r function.r partition.r parLongData.r parWindows.r
longData.r longDataPlot.r distance.r clustering.r
listClustering.r clusterLongData.r parKml.r parChoice.r kml.r

KmL3D is an implementation of k-means specificaly design
to deal with joint trajectories (longitudinal data on several
variable-trajectories).  It provide facilities to deal with
missing value, compute several quality criterion (Calinski and
Harabatz, Ray and Turie, Davies and Bouldin) and propose a
graphical interphace for chosing the 'best' number of clusters.

KmL3D is 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.
Christophe Genolini [cre, aut], Bruno Falissard [ctb],
Jean-Baptiste Pingault [ctb]