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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>.

copied from cf-pre-staging / r-salso
Type Size Name Uploaded Downloads Labels
conda 896.7 kB | osx-64/r-salso-0.3.29-r42h6dc245f_1.conda  2 years and 2 months ago 404 main
conda 893.6 kB | osx-64/r-salso-0.3.29-r43h6dc245f_1.conda  2 years and 2 months ago 415 main
conda 1.6 MB | linux-64/r-salso-0.3.29-r42h57805ef_1.conda  2 years and 2 months ago 1920 main
conda 1.6 MB | linux-64/r-salso-0.3.29-r43h57805ef_1.conda  2 years and 2 months ago 1961 main
conda 898.9 kB | osx-64/r-salso-0.3.29-r42h815d134_0.conda  2 years and 6 months ago 273 main
conda 1.6 MB | linux-64/r-salso-0.3.29-r42h133d619_0.conda  2 years and 6 months ago 2046 main

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