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label.switching (version 1.8)

Relabelling MCMC Outputs of Mixture Models

Description

The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.

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Version

Install

install.packages('label.switching')

Monthly Downloads

1,139

Version

1.8

License

GPL-2

Last Published

July 1st, 2019

Functions in label.switching (1.8)

label.switching

Main calling function
aic

Artificial Identifiability Constraints
ecr.iterative.1

ECR algorithm (iterative version 1)
data_list

Simulated MCMC sample and related information
ecr

ECR algorithm (default version)
dataBased

Data-based labelling
lamb

Fetal lamb dataset
ecr.iterative.2

ECR algorithm (iterative version 2)
compare.clust

Make all estimated clusters agree with a pivot allocation
permute.mcmc

Reorder MCMC samples
pra

PRA algorithm
label.switching-package

Algorithms for solving the label switching problem
stephens

Stephens' algorithm
sjw

Probabilistic relabelling algorithm