Package: PAMhm 0.1.2

Vidal Fey

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.

Authors:Vidal Fey [aut, cre], Henri Sara [aut]

PAMhm_0.1.2.tar.gz
PAMhm_0.1.2.tar.gz(r-4.5-noble)PAMhm_0.1.2.tar.gz(r-4.4-noble)
PAMhm_0.1.2.tgz(r-4.4-emscripten)PAMhm_0.1.2.tgz(r-4.3-emscripten)
PAMhm.pdf |PAMhm.html✨
PAMhm/json (API)

# Install 'PAMhm' in R:
install.packages('PAMhm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 2 scripts 84 downloads 1 exports 54 dependencies

Last updated 3 years agofrom:af3755892d. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-linuxOKNov 02 2024

Exports:PAM.hm

Dependencies:BiobaseBiocGenericscellrangercliclustercolorspacecpp11crayonDEoptimRfansifarvergenericsggplot2gluegtableheatmapFlexHeatplushmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmeperrypillarpkgconfigplyrprettyunitsprogressR.methodsS3R.ooR.utilsR6RColorBrewerRcppRcppArmadilloreadmoRereadxlrematchrlangrobustbaserobustHDscalestibbleutf8vctrsviridisLitewithrxml2

Generate Heatmaps Based on Partitioning Around Medoids (PAM)

Rendered fromPAMhm-vignette.Rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2021-09-06
Started: 2021-09-06