Generate Heatmaps Based on Partitioning Around Medoids (PAM)

Data are partitioned (clustered) into k clusters "around medoids", which is a more robust version of K-means implemented in the function pam() in the 'cluster' package. The PAM algorithm is described in Kaufman and Rousseeuw (1990) . Please refer to the pam() function documentation for more references. Clustered data is plotted as a split heatmap allowing visualisation of representative "group-clusters" (medoids) in the data as separated fractions of the graph while those "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.


Reference manual

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0.1.2 by Vidal Fey, 5 months ago

Browse source code at

Authors: Vidal Fey [aut, cre] , Henri Sara [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports RColorBrewer, R.utils, readxl, readmoRe, utils, plyr, robustHD

Depends on heatmapFlex, cluster, grDevices, graphics, stats

Suggests rmarkdown, knitr

See at CRAN