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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.

Versions across snapshots

VersionRepositoryFileSize
0.1.2 rolling linux/jammy R-4.5 PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 rolling linux/noble R-4.5 PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 rolling source/ R- PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 latest linux/jammy R-4.5 PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 latest linux/noble R-4.5 PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 latest source/ R- PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 2026-04-26 source/ R- PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 2026-04-23 source/ R- PAMhm_0.1.2.tar.gz 245.6 KiB
0.1.2 2025-04-20 source/ R- PAMhm_0.1.2.tar.gz 245.6 KiB

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