salso: Search Algorithms and Loss Functions for Bayesian Clustering

The SALSO algorithm is an efficient greedy search procedure to obtain a clustering estimate based on a partition loss function. 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. SALSO was first presented at the workshop "Bayesian Nonparametric Inference: Dependence Structures and their Applications" in Oaxaca, Mexico on December 6, 2017. See <>.

Version: 0.2.20
Depends: R (≥ 3.5.0)
LinkingTo: cargo (≥ 0.1.28)
Published: 2021-03-27
Author: David B. Dahl ORCID iD [aut, cre], Devin J. Johnson ORCID iD [aut], Peter Müller [aut]
Maintainer: David B. Dahl <dahl at>
License: MIT + file LICENSE | Apache License 2.0
NeedsCompilation: yes
SystemRequirements: Cargo (>= 1.42.0) for installation from sources: see file INSTALL
CRAN checks: salso results


Reference manual: salso.pdf
Package source: salso_0.2.20.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: salso_0.2.20.tgz, r-oldrel: salso_0.2.20.tgz
Old sources: salso archive


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