A collection of personal helper functions to avoid redundancy
    in the spirit of the "Don't repeat yourself" principle of software
    development (<https://en.wikipedia.org/wiki/Don%27t_repeat_yourself>).

Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data (described in Arsalane Chouaib Guidoum (2020) <arXiv:2012.06102> [stat.CO]).

Provides arrays with flexible control over dimension dropping when subscripting.

Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.

Uses Bessel functions to calculate the
    fundamental and complementary analytic solutions to the
    Kelvin differential equation.

Computes the Kendall rank correlation and Mann-Kendall
        trend test. See documentation for use of block bootstrap when
        there is autocorrelation.

The computational complexity of the implemented algorithm for
    Kendall's correlation is O(n log(n)), which is faster than the base R
    implementation with a computational complexity of O(n^2). For small vectors
    (i.e., less than 100 observations), the time difference is negligible.
    However, for larger vectors, the speed difference can be substantial and the
    numerical difference is minimal. The references are
    Knight (1966) <doi:10.2307/2282833>,
    Abrevaya (1999) <doi:10.1016/S0165-1765(98)00255-9>,
    Christensen (2005) <doi:10.1007/BF02736122> and
    Emara (2024) <https://learningcpp.org/>.
    This implementation is described in
    Vargas Sepulveda (2025) <doi:10.1371/journal.pone.0326090>.

Kendall random walks are a continuous-space Markov chains generated
             by the Kendall generalized convolution. This package provides tools
             for simulating these random walks and studying distributions
             related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) <arXiv:1412.0220>.