Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.

These functions were developed to support statistical analysis on functional covariance operators.
  The package contains functions to:
  - compute 2-Wasserstein distances between Gaussian Processes as in
    Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
  - compute the Wasserstein barycenter (Frechet mean) as in Masarotto,
    Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
  - perform analysis of variance testing procedures for functional
    covariances and tangent space principal component analysis of
    covariance operators as in Masarotto, Panaretos & Zemel (2022)
    <arXiv:2212.04797>.
  - perform a soft-clustering based on the Wasserstein distance where
    functional data are classified based on their covariance structure
    as in Masarotto & Masarotto (2023) <doi:10.1111/sjos.12692>.

Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2019) <arXiv:1912.04607v1>. The main algorithms of the paper are available as continuous, discrete and weighted versions.

Finite element modeling of beam structures and 2D geometries using constant strain triangles. Applies material properties and boundary conditions (load and constraint) to generate a finite element model. The model produces stress, strain, and nodal displacements; a heat map is available to demonstrate regions where output variables are high or low.  Also provides options for creating a triangular mesh of 2D geometries. Package developed with reference to: Bathe, K. J. (1996). Finite Element Procedures.[ISBN 978-0-9790049-5-7] -- Seshu, P. (2012). Textbook of Finite Element Analysis. [ISBN-978-81-203-2315-5] -- Mustapha, K. B. (2018). Finite Element Computations in Mechanics with R. [ISBN 9781315144474].

For each feature, a score is computed that can be useful
        for feature selection. Several random subsets are sampled from
        the input data and for each random subset, various linear
        models are fitted using lars method. A score is assigned to
        each feature based on the tendency of LASSO in including that
        feature in the models.Finally, the average score and the models
        are returned as the output. The features with relatively low
        scores are recommended to be ignored because they can lead to
        overfitting of the model to the training data. Moreover, for
        each random subset, the best set of features in terms of global
        error is returned. They are useful for applying Bolasso, the
        alternative feature selection method that recommends the
        intersection of features subsets.