Provides popular sampling distributions C++ routines based in 
    armadillo through a header file approach.

The GenDataSample() and GenDataPopulation() functions create, 
    respectively, a sample or population of multivariate nonnormal data 
    using methods described in Ruscio and Kaczetow (2008). Both of these 
    functions call a FactorAnalysis() function to reproduce a correlation 
    matrix. The EFACompData() function allows users to determine how many 
    factors to retain in an exploratory factor analysis of an empirical data
    set using a method described in Ruscio and Roche (2012). The latter 
    function uses populations of comparison data created by calling the 
    GenDataPopulation() function.
    <DOI: 10.1080/00273170802285693>.
    <DOI: 10.1037/a0025697>.

A method 'generate()' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the 'RMAWGEN' package for the analysis and generation of any
    environmental vector time series.

The method 'generate()' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .

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A genetic algorithm plus derivative optimizer.

Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, 
    Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code 
    of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis.
    The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.