caviarpd: Cluster Analysis via Random Partition Distributions

Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates. An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids clustering methods. The method is currently under peer review at a journal.

Version: 0.2.28
Depends: R (≥ 4.0.0)
Suggests: salso (≥ 0.3.0)
Published: 2022-03-21
Author: David B. Dahl ORCID iD [aut, cre], Jacob Andros ORCID iD [aut], J. Brandon Carter ORCID iD [aut]
Maintainer: David B. Dahl <dahl at stat.byu.edu>
License: MIT + file LICENSE | Apache License 2.0
NeedsCompilation: yes
SystemRequirements: Cargo (>= 1.58.1) for installation from sources: see INSTALL file
Materials: NEWS INSTALL
CRAN checks: caviarpd results

Documentation:

Reference manual: caviarpd.pdf

Downloads:

Package source: caviarpd_0.2.28.tar.gz
Windows binaries: r-devel: caviarpd_0.2.28.zip, r-release: caviarpd_0.2.28.zip, r-oldrel: caviarpd_0.2.28.zip
macOS binaries: r-release (arm64): caviarpd_0.2.28.tgz, r-oldrel (arm64): caviarpd_0.2.28.tgz, r-release (x86_64): caviarpd_0.2.28.tgz, r-oldrel (x86_64): caviarpd_0.2.28.tgz
Old sources: caviarpd archive

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