A novel generalized Random Forest method, that can improve on
    RFs by borrowing the strength of penalized parametric regression. Based on
    Zhang et al. (2019) <doi:10.48550/arXiv.1904.10416>.

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.

Extracts information from text using lookup tables of regular
    expressions. Each text entry is compared against all patterns, and all
    matching patterns and their corresponding substrings are returned. If a
    text entry matches multiple patterns, multiple rows are generated to
    capture each match. This approach enables comprehensive pattern coverage
    when processing large or complex text datasets.

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>).

Provides methods to calculate approximate regional
    consistency probabilities using Method 1 and Method 2 proposed by the Japanese
    Ministry of Health, Labor and Welfare (2007) <https://www.pmda.go.jp/files/000153265.pdf>. These methods
    are useful for assessing regional consistency in multi-regional
    clinical trials.  The package can calculate unconditional, joint, and
    conditional regional consistency probabilities.  For technical
    details, please see Homma (2024) <doi:10.1002/pst.2358>.