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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-staging / r-salso
Type Size Name Uploaded Downloads Labels
conda 807.3 kB | osx-64/r-salso-0.3.35-r44h6b9d099_1.conda  7 months and 25 days ago 426 main
conda 800.9 kB | osx-64/r-salso-0.3.35-r43h6b9d099_1.conda  7 months and 25 days ago 388 main
conda 1.4 MB | linux-64/r-salso-0.3.35-r44hdb488b9_1.conda  7 months and 25 days ago 1053 main
conda 1.4 MB | linux-64/r-salso-0.3.35-r43hdb488b9_1.conda  7 months and 25 days ago 1078 main
conda 900.8 kB | osx-64/r-salso-0.3.35-r43h6dc245f_0.conda  1 year and 7 months ago 414 main
conda 901.1 kB | osx-64/r-salso-0.3.35-r42h6dc245f_0.conda  1 year and 7 months ago 414 main
conda 1.6 MB | linux-64/r-salso-0.3.35-r42h57805ef_0.conda  1 year and 7 months ago 1496 main
conda 1.6 MB | linux-64/r-salso-0.3.35-r43h57805ef_0.conda  1 year and 7 months ago 1475 main

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