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salso (version 0.3.42)

Search Algorithms and Loss Functions for Bayesian Clustering

Description

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) .

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install.packages('salso')

Monthly Downloads

6,375

Version

0.3.42

License

MIT + file LICENSE | Apache License 2.0

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Maintainer

David B Dahl

Last Published

September 16th, 2024

Functions in salso (0.3.42)

iris.clusterings

Clusterings of the Iris Data
psm

Compute an Adjacency or Pairwise Similarity Matrix
bell

Compute the Bell Number
partition.loss

Compute Partition Loss or the Expectation of Partition Loss
chips

CHiPS Partition Greedy Search
plot.salso.summary

Heatmap, Multidimensional Scaling, Pairs, and Dendrogram Plotting for Partition Estimation
enumerate.permutations

Enumerate Permutations of Items
enumerate.partitions

Enumerate Partitions of a Set
dlso

Latent Structure Optimization Based on Draws
summary.salso.estimate

Summary of Partitions Estimated Using Posterior Expected Loss
salso

SALSO Greedy Search
salso-package

salso: Search Algorithms and Loss Functions for Bayesian Clustering