Regularised discriminant analysis functions. The classical regularised discriminant analysis proposed by Friedman in 1989, including cross-validation, of which the linear and quadratic discriminant analyses are special cases. Further, the regularised maximum likelihood linear discriminant analysis, including cross-validation. References: Friedman J.H. (1989): "Regularized Discriminant Analysis". Journal of the American Statistical Association 84(405): 165--175. <doi:10.2307/2289860>. Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>. Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>.

Performs regularization of differential item functioning (DIF) parameters 
    in item response theory (IRT) models (Belzak & Bauer, 2020)
    <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization
    algorithm.

Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.

Provides wrappers around base::grep() where the first argument
    is standardized to take the data object. This makes it less of a pain to use
    regular expressions with 'magrittr' or other pipe operators.

'shiny' extension that adds regular expression filtering capabilities to 
  the choice vector of the select list.

A set of functions used to automate commonly used methods in
    regression analysis. This includes plotting interactions, and calculating
    simple slopes, standardized coefficients, regions of significance
    (Johnson & Neyman, 1936; cf. Spiller et al., 2012), etc. See the reghelper
    documentation for more information, documentation, and examples.

Calculates intra-regional and inter-regional similarities based on user-provided
    spatial vector objects (regions) and spatial raster objects (cells with values).
    Implemented metrics include inhomogeneity, isolation 
    (Haralick and Shapiro (1985) <doi:10.1016/S0734-189X(85)90153-7>, 
    Jasiewicz et al. (2018) <doi:10.1016/j.cageo.2018.06.003>), 
    and distinction (Nowosad (2021) <doi:10.1080/13658816.2021.1893324>).