  The identification of groups' prototypes, i.e. elements of a dataset that
represent different groups of data points, may be relevant to the tasks of
clustering, classification and mixture modeling. The R package pivmet presented
in this paper includes different methods for extracting pivotal units from a
dataset. One of the main applications of pivotal methods is a Markov Chain
Monte Carlo (MCMC) relabelling procedure to solve the label switching in
Bayesian estimation of mixture models. Each method returns posterior estimates,
and a set of graphical tools for visualizing the output. The package offers
JAGS and Stan sampling procedures for Gaussian mixtures, and allows for
user-defined priors' parameters. The package also provides functions to perform
consensus clustering based on pivotal units, which may allow to improve
classical techniques (e.g. k-means) by means of a careful seeding. The paper
provides examples of applications to both real and simulated datasets.
