PAMhm: 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) <doi:10.1002/9780470316801>. 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.

Version: 0.1.2
Depends: heatmapFlex, cluster, grDevices, graphics, stats
Imports: RColorBrewer, R.utils, readxl, readmoRe, utils, plyr, robustHD
Suggests: rmarkdown, knitr
Published: 2021-09-06
DOI: 10.32614/CRAN.package.PAMhm
Author: Vidal Fey [aut, cre], Henri Sara [aut]
Maintainer: Vidal Fey <vidal.fey at>
License: GPL-3
NeedsCompilation: no
CRAN checks: PAMhm results


Reference manual: PAMhm.pdf
Vignettes: Generate Heatmaps Based on Partitioning Around Medoids (PAM)


Package source: PAMhm_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PAMhm_0.1.2.tgz, r-oldrel (arm64): PAMhm_0.1.2.tgz, r-release (x86_64): PAMhm_0.1.2.tgz, r-oldrel (x86_64): PAMhm_0.1.2.tgz


Please use the canonical form to link to this page.