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.


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install.packages("PAMhm")

0.1.2 by Vidal Fey, 21 days ago


Browse source code at https://github.com/cran/PAMhm


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