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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>.

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rustcargo

4.55 score 5 dependents 148 scripts 16k downloads

caviarpd - Cluster Analysis via Random Partition Distributions

Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates. An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.

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rustcargo

1.89 score 7.8k downloads

fangs - Feature Allocation Neighborhood Greedy Search Algorithm

A neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" <doi:10.1080/10618600.2023.2204136>.

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rustcargo

1.85 score 1 scripts 7.1k downloads

gourd - Shrinkage Partition Distribution

Implementation of the distribution introduced in our paper "Dependent Random Partitions by Shrinking Toward an Anchor".

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rustcargo

1.00 score 6 scripts