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.

Functions to estimate the intensity function and its derivative of a given order of a multiplicative counting process using the local polynomial method.

Provides functions to estimate and visualize linear as well as nonlinear impulse 
             responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>.
             The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.

LP nonparametric high-dimensional K-sample comparison method that includes 
    (i) confirmatory test, (ii) exploratory analysis, and (iii) options to output a 
	data-driven LP-transformed matrix for classification. The primary reference is 
	Mukhopadhyay, S. and Wang, K. (2020, Biometrika); <arXiv:1810.01724>.