Process an MCMC Sample of Clusterings
(Adjusted) Rand Index for Clusterings
Compute Similarity Matrix for a Clustering and vice versa
Estimate Posterior Similarity Matrix
Maximize/Compute Posterior Expected Adjusted Rand Index
Process MCMC Sample of Clusterings.
Clustering Method of Medvedovic
Minimize/Compute Posterior Expectation of Binders Loss Function
Norm Labelling of a Clustering
Stephens' Relabelling Algorithm for Clusterings
Variation of Information Distance for Clusterings
Implements methods for processing a sample of (hard) clusterings, e.g. the MCMC output of a Bayesian clustering model. Among them are methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabelling algorithm.