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 |
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