Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
        including KNN classification, regression and information measures are implemented.

Provides a collection of functions for testing various aspects of
	univariate time series including independence and neglected
	nonlinearities. Further provides functions to investigate the chaotic
	behavior of time series processes and to simulate different types of chaotic
	time series maps.

Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.

Provides a 'C++' API for routinely used numerical tools such as integration,
    root-finding, and optimization, where function arguments are given as
    lambdas. This facilitates 'Rcpp' programming, enabling the development of
    'R'-like code in 'C++' where functions can be defined on the fly and use
    variables in the surrounding environment.

Feature Ordering by Conditional Independence (FOCI) is a variable selection algorithm based on the measure of conditional dependence.
    For more information, see the paper: Azadkia and Chatterjee (2019),"A simple measure of conditional dependence" <arXiv:1910.12327>.

Methods and tools designed to improve the forecast accuracy for a linearly 
    constrained multiple time series, while fulfilling the linear/aggregation relationships 
    linking the components (Girolimetto and Di Fonzo, 2024 <doi:10.48550/arXiv.2412.03429>). 
    'FoCo2' offers multi-task forecast combination and reconciliation 
    approaches leveraging input from multiple forecasting models or experts and ensuring 
    that the resulting forecasts satisfy specified linear constraints. In addition, linear 
    inequality constraints (e.g., non-negativity of the forecasts) can be imposed, if needed.

Takes a distance matrix and plots it as an 
    interactive graph. One point is focused at the center of the graph,
    around which all other points are plotted in their exact distances as
    given in the distance matrix. All other non-focus points are plotted 
    as best as possible in relation to one another. Double click on any 
    point to choose a new focus point, and hover over points to see their
    ID labels. If color label categories are given, hover over colors in 
    the legend to highlight only those points and click on colors to 
    highlight multiple groups. For more information on the rationale 
    and mathematical background, as well as an interactive introduction,
    see <https://lea-urpa.github.io/focusedMDS.html>.