Performs goodness-of-fit tests for model-based clustering 
    based on the methodology developed in  <doi:10.48550/arXiv.2511.04206>. 
    It implements a test statistic 
    derived from empirical likelihood to verify the distribution of 
    clusters in multivariate Gaussian mixtures and other latent 
    class models.

Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.

Implementation of several goodness-of-fit tests for functional
    data. Currently, mostly related with the functional linear model with
    functional/scalar response and functional/scalar predictor. The package
    allows for the replication of the data applications considered in
    García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga
    (2021) <doi:10.1111/sjos.12486>.