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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 807.3 kB | osx-64/r-salso-0.3.35-r44h6b9d099_1.conda  1 year and 1 month ago 443 main
conda 800.9 kB | osx-64/r-salso-0.3.35-r43h6b9d099_1.conda  1 year and 1 month ago 408 main
conda 1.4 MB | linux-64/r-salso-0.3.35-r44hdb488b9_1.conda  1 year and 1 month ago 1441 main
conda 1.4 MB | linux-64/r-salso-0.3.35-r43hdb488b9_1.conda  1 year and 1 month ago 1463 main
conda 900.8 kB | osx-64/r-salso-0.3.35-r43h6dc245f_0.conda  2 years and 1 month ago 420 main
conda 901.1 kB | osx-64/r-salso-0.3.35-r42h6dc245f_0.conda  2 years and 1 month ago 421 main
conda 1.6 MB | linux-64/r-salso-0.3.35-r42h57805ef_0.conda  2 years and 1 month ago 1879 main
conda 1.6 MB | linux-64/r-salso-0.3.35-r43h57805ef_0.conda  2 years and 1 month ago 1854 main

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