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.

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>.

'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.

A unified framework for detecting regime changes (changepoints) in 
    time series data. Implements both frequentist methods including Cumulative Sum 
    (CUSUM, Page (1954) <doi:10.1093/biomet/41.1-2.100>), Pruned Exact Linear 
    Time (PELT, Killick, Fearnhead, and Eckley (2012) 
    <doi:10.1080/01621459.2012.737745>), Binary Segmentation, and Wild Binary 
    Segmentation, as well as Bayesian methods such as Bayesian Online Changepoint 
    Detection (BOCPD, Adams and MacKay (2007) <doi:10.48550/arXiv.0710.3742>
    and Shiryaev-Roberts. Supports offline analysis for retrospective detection 
    and online monitoring for real-time surveillance. Provides rigorous 
    uncertainty quantification through confidence intervals and posterior 
    distributions. Handles univariate and multivariate series with detection of 
    changes in mean, variance, trend, and distributional properties.