An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.

Computes fast (relative to other implementations) approximate 
    Shapley values for any supervised learning model. Shapley values help to 
    explain the predictions from any black box model using ideas from game 
    theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> 
    for details.

Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.

It implements an improved and computationally faster version 
            of the original Stepwise Gaussian Graphical Algorithm for estimating 
            the Omega precision matrix from high-dimensional data. 
            Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) 
            <doi:10.52933/jdssv.v1i2.11>.

An interface to the 'fastText' <https://github.com/facebookresearch/fastText> library for efficient learning of word representations and sentence classification. The 'fastText' algorithm is explained in detail in (i) "Enriching Word Vectors with subword Information", Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, 2017, <doi:10.1162/tacl_a_00051>; (ii) "Bag of Tricks for Efficient Text Classification", Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov, 2017, <doi:10.18653/v1/e17-2068>; (iii) "FastText.zip: Compressing text classification models", Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, Tomas Mikolov, 2016, <arXiv:1612.03651>.