The package performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions.

Kaplan-Meier Multiple Imputation for the Analysis of Cumulative
Incidence Functions in the Competing Risks Setting
The kmi package performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions.

Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 <doi:10.1093/biostatistics/kxq018>).

K-Means for Longitudinal data (kml) is a non parametric algorithm for clusterizing longitudinal data.
function.r partition.r longData.r imputation.r artificialLongData.r clusterization.r clusterizLongData.r

K-Means for Longitudinal data (KmL) is a version of k-means for longitudinal able to deal with missing value and proposing a graphical interphace for cloosing the correct number of clusters.
global.r function.r longData.r partition.r clusterization.r longData-Partition.r imputation.r clusterizLongData.r artificialLongData.r

KmL is an implementation of k-means specificaly design to
deal with longitudinal data. It provide facilities to deal with
missing value and propose a graphical interphace for chosing
the correct number of clusters.

global.r clusterization.r clusterizLongData.r kml.r clean.r

KmL is an implementation of k-means specificaly design to
deal with longitudinal data. It provide facilities to deal with
missing value, compute several quality criterion (Calinski and
Harabatz, Ray and Turie, Davies and Bouldin, ...) and propose
various a graphical interphace for chosing the 'best' number of
clusters.
global.r clusterLongData.r parKml.r parChoice.r kml.r