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