Classification method obtained through linear programming.
    It is advantageous with respect to the classical developments when the distribution of the variables
    involved is unknown or when the number of variables is much greater than the number of individuals.
    LPDA method is published in Nueda, et al. (2022) "LPDA: A new classification method based on linear programming".
    <doi:10.1371/journal.pone.0270403>.

Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.

A nonparametric method to approximate Laplacian graph spectra of a network with 
    ordered vertices. This provides a computationally efficient algorithm for obtaining an 
	accurate and smooth estimate of the graph Laplacian basis. The approximation results can 
	then be used for tasks like change point detection, k-sample testing, and so on. The 
	primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).

Provides a bunch of algorithms based on linear programming for estimating, under 
    the homogeneity hypothesis, RxC ecological contingency tables (or vote transition matrices) 
    using mainly aggregate data (from voting units). 
    References: 
    Pavía and Romero (2022) <doi:10.1177/00491241221092725>.
    Pavía (2023) <doi:10.1007/s43545-023-00658-y>.
    Pavía and Romero (2024) <doi:10.1093/jrsssa/qnae013>.
    Pavía (2024) A local convergent ecological inference algorithm for RxC tables.
    Pavía and Penadés (2024). A bottom-up approach for ecological inference.
    Romero, Pavía, Martín and Romero (2020) <doi:10.1080/02664763.2020.1804842>.
    Acknowledgements:
    The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257) and Ministerio de Economía e Innovación (grant PID2021-128228NB-I00) for supporting this research.