salso: Search Algorithms and Loss Functions for Bayesian Clustering

The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2021) <arXiv:2105.04451>.

Version: 0.2.23
Depends: R (≥ 4.0.0)
Published: 2021-08-13
Author: David B. Dahl ORCID iD [aut, cre], Devin J. Johnson ORCID iD [aut], Peter Müller [aut]
Maintainer: David B. Dahl <dahl at stat.byu.edu>
License: MIT + file LICENSE | Apache License 2.0
NeedsCompilation: yes
SystemRequirements: Cargo (>= 1.51.0) for installation from sources: see INSTALL file
Materials: NEWS INSTALL
CRAN checks: salso results

Documentation:

Reference manual: salso.pdf

Downloads:

Package source: salso_0.2.23.tar.gz
Windows binaries: r-devel: salso_0.2.23.zip, r-devel-UCRT: salso_0.2.23.zip, r-release: salso_0.2.23.zip, r-oldrel: salso_0.2.23.zip
macOS binaries: r-release (arm64): salso_0.2.23.tgz, r-release (x86_64): salso_0.2.23.tgz, r-oldrel: salso_0.2.23.tgz
Old sources: salso archive

Reverse dependencies:

Reverse depends: caviarpd
Reverse imports: AntMAN

Linking:

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