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.
|Author||Christophe Genolini [cre, aut], Bruno Falissard [ctb]|
|Date of publication||2016-02-16 23:12:45|
|Maintainer||Christophe Genolini <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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