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kml (version 2.4.6.1)

K-Means for Longitudinal Data

Description

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.

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Version

Install

install.packages('kml')

Monthly Downloads

1,264

Version

2.4.6.1

License

GPL (>= 2)

Maintainer

Christophe Genolini

Last Published

December 13th, 2023

Functions in kml (2.4.6.1)

~ Data: epipageShort ~

ClusterLongData-class

~ Class: ClusterLongData ~

~ Functions: affectIndiv & affectIndivC ~

getBestPostProba

~ Function: getBestPostProba ~

affectFuzzyIndiv

~ Function: affectFuzzyIndiv ~

~ Algorithm fuzzy kml: Fuzzy k-means for Longitidinal data ~

generateArtificialLongData

~ Function: generateArtificialLongData (or gald) ~

~ Overview: K-means for Longitudinal data ~

~ Algorithm kml: K-means for Longitidinal data ~

~ Function: getClusters ~

~ Internal KmL objects and methods ~

plotMeans,ClusterLongData

~ Function: plotMeans for ClusterLongData ~

plotTraj,ClusterLongData

~ Function: plotTraj for ClusterLongData ~

~ Class: "ParKml" ~

~ Function: parKml ~

plot,ClusterLongData

~ Function: plot for ClusterLongData ~

calculTrajFuzzyMean

~ Function: calculTrajFuzzyMean ~

~ Function: choice ~

~ Functions: calculTrajMean & calculTrajMeanC ~

clusterLongData

~ Function: clusterLongData (or cld) ~