  This contribution presents a guide to the R package multilevLCA, which offers
a complete and innovative set of technical tools for the latent class analysis
of single-level and multilevel categorical data. We describe the available
model specifications, mainly falling within the fixed-effect or random-effect
approaches. Maximum likelihood estimation of the model parameters, enhanced by
a refined initialization strategy, is implemented either simultaneously, i.e.,
in one-step, or by means of the more advantageous two-step estimator. The
package features i) semi-automatic model selection when a priori information on
the number of classes is lacking, ii) predictors of class membership, and iii)
output visualization tools for any of the available model specifications. All
functionalities are illustrated by means of a real application on citizenship
norms data, which are available in the package.
