Multiple testing procedures for heterogeneous and discrete tests as
    described in Döhler and Roquain (2020) <doi:10.1214/20-EJS1771>. The main 
    algorithms of the paper are available as continuous, discrete and weighted
    versions. They take as input the results of a test procedure from package
    'DiscreteTests', or a set of observed p-values and their discrete support
    under their nulls. A shortcut function to obtain such p-values and supports
    is also provided, along with wrappers allowing to apply discrete procedures
    directly to data.

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.