Anchored Kmedoids for Longitudinal Data Clustering

Advances a novel adaptation of longitudinal k-means clustering technique (Genolini et al. (2015) ) for grouping trajectories based on the similarities of their long-term trends and determines the optimal solution based on the Calinski-Harabatz criterion (Calinski and Harabatz (1974) ). Includes functions to extract descriptive statistics and generate a visualisation of the resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016) ). The package also includes a number of other useful functions for exploring and manipulating longitudinal data prior to the clustering process.


Reference manual

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0.1.0 by Monsuru Adepeju, a month ago

Browse source code at

Authors: Monsuru Adepeju [cre, aut] , Samuel Langton [aut] , Jon Bannister [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports kml, Hmisc, ggplot2, utils, reshape2, longitudinalData

Suggests knitr, rmarkdown

See at CRAN