Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.

Latent Class Analysis of
        phenotypic measurements in pedigrees and model selection
        based on one of two methods: likelihood-based cross-validation
        and Bayesian Information Criterion. Computation of individual
        and triplet child-parents weights in a pedigree is performed using an
        upward-downward algorithm. The model takes into account the familial
        dependence defined by the pedigree structure by considering
        that a class of a child depends on his parents classes via
        triplet-transition probabilities of the classes. The package
        handles the case where measurements are available on all
        subjects and the case where measurements are available only on
        symptomatic (i.e. affected) subjects. Distributions for
        discrete (or ordinal) and continuous data are currently
        implemented. The package can deal with missing data.

Provides Shiny widgets and theme that support a 'Library Computer Access/Retrieval System' (LCARS) aesthetic for Shiny apps. 
    The package also includes functions for adding a minimal LCARS theme to static 'ggplot2' graphs. 
    More details about LCARS can be found at <https://en.wikipedia.org/wiki/LCARS>.

Variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package implements a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative. The variable selection method is based on the approach of Fop et al. (2017) <doi:10.1214/17-AOAS1061> and Dean and Raftery (2010) <doi:10.1007/s10463-009-0258-9>. Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search. Concomitant covariates used to predict the class membership probabilities can also be included in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.