Package: kmed 0.4.2

kmed: Distance-Based k-Medoids

Algorithms of distance-based k-medoids clustering: simple and fast k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. Calculate distances for mixed variable data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and relative criteria. The internal criteria includes silhouette index and shadow values. The relative criterium applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages. The cluster result can be plotted in a marked barplot or pca biplot.

Authors:Weksi Budiaji [aut, cre]

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NEWS

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16 exports 0.00 score 28 dependencies 76 scripts 560 downloads

Last updated 2 years agofrom:524e990059. Checks:OK: 7. Indexed: yes.

Exports:barplotnumclustbootclustheatmapconsensusmatrixcooccurcsvdistmixdistNumericfastkmedinckmedmatchingmsvpcabiplotrankkmedsilskm

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr