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 gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	PAMhm results

Documentation:

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

Downloads:

Package source:	PAMhm_0.1.2.tar.gz
Windows binaries:	r-devel: PAMhm_0.1.2.zip, r-release: PAMhm_0.1.2.zip, r-oldrel: PAMhm_0.1.2.zip
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

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