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 (2022) <doi:10.1080/10618600.2022.2069779>.

Version:	0.3.78
Depends:	R (≥ 4.3)
Published:	2026-04-16
DOI:	10.32614/CRAN.package.salso
Author:	David B. Dahl [aut, cre], Devin J. Johnson [aut], Peter Müller [aut], Andrés Felipe Barrientos [aut], Garritt Page [aut], David Dunson [aut], Authors of the dependency Rust crates [ctb] (see inst/AUTHORS file for details) salso author details
Maintainer:	David B. Dahl <dahl at stat.byu.edu>
BugReports:	https://github.com/dbdahl/salso/issues
License:	MIT + file LICENSE | Apache License 2.0
URL:	https://github.com/dbdahl/salso
NeedsCompilation:	yes
SystemRequirements:	Cargo (Rust's package manager), rustc (>= 1.85.1)
Materials:	NEWS, INSTALL
CRAN checks:	salso results

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