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>.

Methods for assessing agreement between repeated 
    measurements obtained by two or more methods using the longitudinal 
    concordance correlation coefficient (LCC). Polynomial mixed-effects 
    models (via 'nlme') describe how concordance, Pearson correlation 
    and accuracy evolve over time. Functions are provided for model 
    fitting, diagnostic plots, extraction of summaries, and non-parametric 
    bootstrap confidence intervals (including parallel computation), 
    following Oliveira et al. (2018) <doi:10.1007/s13253-018-0321-1>.

Implements the kK-NN algorithm, an adaptive k-nearest neighbor classifier that adjusts the neighborhood size based on local data curvature. The method estimates local Gaussian curvature by approximating the shape operator of the data manifold. This approach aims to improve classification performance, particularly in datasets with limited samples.