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Label switching is a well-known and fundamental problem in Bayesianestimation of mixture or hidden Markov models. In case that the priordistribution of the model parameters is the same for all states, then both thelikelihood and posterior distribution are invariant to permutations of theparameters. This property makes Markov chain Monte Carlo (MCMC) samplessimulated from the posterior distribution non-identifiable. In this paper, the\pkg{label.switching} package is introduced. It contains one probabilistic andseven deterministic relabelling algorithms in order to post-process a givenMCMC sample, provided by the user. Each method returns a set of permutationsthat can be used to reorder the MCMC output. Then, any parametric function ofinterest can be inferred using the reordered MCMC sample. A set of user-definedpermutations is also accepted, allowing the researcher to benchmark newrelabelling methods against the available ones

label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs