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mcclust (version 1.0)

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

481

Version

1.0

License

GPL (>= 2)

Maintainer

Arno Fritsch

Last Published

July 23rd, 2012

Functions in mcclust (1.0)

medv

Clustering Method of Medvedovic
arandi

(Adjusted) Rand Index for Clusterings
comp.psm

Estimate Posterior Similarity Matrix
norm.label

Norm Labelling of a Clustering
cls.draw1.5

Sample of Clusterings from Posterior Distribution of Bayesian Cluster Model
maxpear

Maximize/Compute Posterior Expected Adjusted Rand Index
mcclust-package

Process MCMC Sample of Clusterings.
cls.draw2

Sample of Clusterings from Posterior Distribution of Bayesian Cluster Model
vi.dist

Variation of Information Distance for Clusterings
cltoSim

Compute Similarity Matrix for a Clustering and vice versa
Ysim2

Simulated 3-dimensional Normal Data Containing 8 Clusters
minbinder

Minimize/Compute Posterior Expectation of Binders Loss Function
Ysim1.5

Simulated 3-dimensional Normal Data Containing 8 Clusters
relabel

Stephens' Relabelling Algorithm for Clusterings