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)
- Version0.3.42
- R version≥ 4.2.0
- LicenseMIT
- Licensefile LICENSE
- LicenseApache License 2.0
- Needs compilation?Yes
- Last release09/16/2024
Documentation
Team
David B. Dahl
Devin J. Johnson
Peter Müller
Show author detailsRolesAuthorAlex Crichton
Show author detailsRolesContributorBrendan Zabarauskas
Show author detailsRolesContributorDavid Tolnay
Show author detailsRolesContributorJim Turner
Show author detailsRolesContributorJosh Stone
Show author detailsRolesContributorR. Janis Goldschmidt
Show author detailsRolesContributorSean McArthur
Show author detailsRolesContributorStefan Lankes
Show author detailsRolesContributorThe Cranelift Project Developers
Show author detailsRolesContributorThe CryptoCorrosion Contributors
Show author detailsRolesContributorThe Rand Project Developers
Show author detailsRolesContributorThe Rust Project Developers
Show author detailsRolesContributorUlrik Sverdrup "bluss"
Show author detailsRolesContributorbluss
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