Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.

Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.

Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.

Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering.  See Einbeck, Tutz and Evers (2005) <doi:10.1007/s11222-005-4073-8> and Ameijeiras-Alonso and Einbeck (2023) <doi:10.1007/s11634-023-00575-1>.

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.
    Mathematical details behind the method are published in Nueda, et al. (2022) "LPDA: A new classification method based on linear programming".
    <doi:10.1371/journal.pone.0270403>.

Implements local polynomial distribution and density methods for point estimation, inference and bandwidth selection, documented in Cattaneo, Jansson and Ma (2020) <doi:10.1080/01621459.2019.1635480>, Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>, and Cattaneo, Jansson and Ma (2024) <doi:10.1016/j.jeconom.2021.01.006>. lpdensity() constructs local polynomial distribution and density estimators with robust bias-corrected inference, and lpbwdensity() implements data-driven bandwidth selection.

It uses 'phenological' and productivity-related variables derived from time series of vegetation 
    indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which 
    eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator 
    is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing 
    productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description 
    of the methods used in the package to calculate the indicator.