R: Search Algorithms and Loss Functions for Bayesian Clustering

# Search Algorithms and Loss Functions for Bayesian Clustering

## Documentation for package ‘salso’ version 0.3.78

- DESCRIPTION file.
- Code demos. Use demo() to run them.
- Package NEWS.

## Help Pages

ARI | Compute Partition Loss or the Expectation of Partition Loss
bell | Compute the Bell Number
binder | Compute Partition Loss or the Expectation of Partition Loss
canonicalize_cluster_labels | Canonicalize Cluster Labels
chips | CHIPS Partition Greedy Search
dlso | Latent Structure Optimization Based on Draws
enumerate.partitions | Enumerate Partitions of a Set
enumerate.permutations | Enumerate Permutations of Items
ID | Compute Partition Loss or the Expectation of Partition Loss
iris.clusterings | Clusterings of the Iris Data
lbell | Compute the Bell Number
NID | Compute Partition Loss or the Expectation of Partition Loss
NVI | Compute Partition Loss or the Expectation of Partition Loss
omARI | Compute Partition Loss or the Expectation of Partition Loss
omARI.approx | Compute Partition Loss or the Expectation of Partition Loss
partition.loss | Compute Partition Loss or the Expectation of Partition Loss
plot.salso.summary | Heatmap, Multidimensional Scaling, Pairs, and Dendrogram Plotting for Partition Estimation
psm | Compute an Adjacency or Pairwise Similarity Matrix
RI | Compute Partition Loss or the Expectation of Partition Loss
salso | SALSO Greedy Search
summary.salso.estimate | Summary of Partitions Estimated Using Posterior Expected Loss
synthetic | Synthetic Dataset for CHIPS Demo
threshold | Threshold CHIPS Output
VI | Compute Partition Loss or the Expectation of Partition Loss
VI.lb | Compute Partition Loss or the Expectation of Partition Loss