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banditpam: Almost Linear-Time k-Medoids Clustering

Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.

Version: 1.0-1
Depends: R (≥ 3.5.0)
Imports: R6, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: ggplot2, knitr, MASS, rmarkdown, tinytest
Published: 2023-03-15
DOI: 10.32614/CRAN.package.banditpam
Author: Balasubramanian Narasimhan [aut, cre], Mo Tiwari [aut] (https://motiwari.com)
Maintainer: Balasubramanian Narasimhan <naras at stanford.edu>
BugReports: https://github.com/motiwari/BanditPAM
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: C++17
Materials: README NEWS
CRAN checks: banditpam results

Documentation:

Reference manual: banditpam.pdf
Vignettes: Almost Linear-Time k-Medoids Clustering

Downloads:

Package source: banditpam_1.0-1.tar.gz
Windows binaries: r-devel: banditpam_1.0-1.zip, r-release: banditpam_1.0-1.zip, r-oldrel: banditpam_1.0-1.zip
macOS binaries: r-release (arm64): banditpam_1.0-1.tgz, r-oldrel (arm64): banditpam_1.0-1.tgz, r-release (x86_64): banditpam_1.0-1.tgz, r-oldrel (x86_64): banditpam_1.0-1.tgz
Old sources: banditpam archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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