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mcclust (version 1.0.1)

Process an MCMC Sample of Clusterings

Description

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.

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Version

Install

install.packages('mcclust')

Monthly Downloads

650

Version

1.0.1

License

GPL (>= 2)

Maintainer

Arno Fritsch

Last Published

May 2nd, 2022

Functions in mcclust (1.0.1)

medv

Clustering Method of Medvedovic
vi.dist

Variation of Information Distance for Clusterings
relabel

Stephens' Relabelling Algorithm for Clusterings
norm.label

Norm Labelling of a Clustering
minbinder

Minimize/Compute Posterior Expectation of Binders Loss Function
comp.psm

Estimate Posterior Similarity Matrix
mcclust-package

Process MCMC Sample of Clusterings.
arandi

(Adjusted) Rand Index for Clusterings
cls.draw2

Sample of Clusterings from Posterior Distribution of Bayesian Cluster Model
Ysim1.5

Simulated 3-dimensional Normal Data Containing 8 Clusters
cltoSim

Compute Similarity Matrix for a Clustering and vice versa
cls.draw1.5

Sample of Clusterings from Posterior Distribution of Bayesian Cluster Model
maxpear

Maximize/Compute Posterior Expected Adjusted Rand Index
Ysim2

Simulated 3-dimensional Normal Data Containing 8 Clusters