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>).

A tool to conquer the difficulties to convert various region names and administration division codes of Chinese regions. The current version enables seamlessly converting Chinese regions' formal names, common-used names, and codes between each other at the city level from 1986 to 2019.

Validating sub-national statistical typologies, re-coding across 
    standard typologies of sub-national statistics, and making valid aggregate
    level imputation, re-aggregation, re-weighting and projection down to 
    lower hierarchical levels to create meaningful data panels and time series.