Drift-Diffusion Model (DDM) has been widely used to model binary
    decision-making tasks, and many research studies the relationship between
    DDM parameters and other characteristics of the subject. This package uses
    'RStan' to perform generalized liner regression analysis over DDM parameters
    via a single Bayesian Hierarchical model. Compared to estimating DDM
    parameters followed by a separate regression model, 'RegDDM' reduces bias
    and improves statistical power.

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>).

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>.