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kmed
View on CRAN: Click
here
Download and install kmed package within the R console
Install from CRAN:
install.packages("kmed")
Install from Github:
library("remotes")
install_github("cran/kmed") Install by package version:
library("remotes")
install_version("kmed", "0.4.2") Attach the package and use:
library("kmed")
Maintained by
Weksi Budiaji
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-02-12
Latest Update: 2022-08-29
Description:
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.
How to cite:
Weksi Budiaji (2018). kmed: Distance-Based k-Medoids. R package version 0.4.2, https://cran.r-project.org/web/packages/kmed. Accessed 29 Oct. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for kmed
Functions, R codes and Examples using
the kmed R package
Some associated functions: barplotnum . clust4 . clust5 . clustboot . clustheatmap . consensusmatrix . cooccur . csv . distNumeric . distmix . fastkmed . globalfood . heart . inckmed . matching . msv . pcabiplot . rankkmed . sil . skm .
Some associated R codes: barplotnum.R . clustboot.R . clustheatmap.R . consensusmatrix.R . cooccur.R . csv.R . data.R . distNumeric.R . distmix.R . fastkmed.R . inckmed.R . matching.R . msv.R . pcabiplot.R . rankkmed.R . sil.R . skm.R . weightednum.R . Full kmed package functions and examples
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