Implements a suite of tools for outlier detection and treatment 
    in data mining. It includes univariate methods (Z-score, Interquartile Range), 
    multivariate detection using Mahalanobis distance, and density-based 
    detection (Local Outlier Factor) via the 'dbscan' package. It also 
    provides functions for visualization using 'ggplot2' and data cleaning 
    via Winsorization.

A high-level plotting system, compatible with `ggplot2` objects, 
    maps from `sf`, `terra`, `raster`, `sp`. It is built primarily on the 
    'grid' package. The objective of the package is to provide a plotting system 
    that is built for speed and modularity. This is useful for quick visualizations 
    when testing code and for plotting multiple figures to the same device from 
    independent sources that may be independent of one another (i.e., different 
    function or modules the create the visualizations).

Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.

A collection of functions for constructing large pairwised comparisons 
             and rating them using Elo rating system with supporting parallel processing.
             The method of random sample pairs is based on Reservoir Sampling proposed by 
             JVitter (1985) <doi:10.1145/3147.3165>.

Compile R functions annotated with type and shape
    declarations for extremely fast performance and robust runtime
    type checking. Supports both just-in-time (JIT) and ahead-of-time
    (AOT) compilation. Compilation is performed by lowering R code to
    Fortran.

Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.

A high-level wrapper that simplifies text classification into three streamlined steps: preprocessing, 
      model training, and prediction. 
    It unifies the interface for multiple algorithms (including 'glmnet', 
    'ranger', and 'xgboost') and vectorization methods (Bag-of-Words, Term Frequency-Inverse Document Frequency (TF-IDF)), 
    allowing users to go from raw text to a trained sentiment model in two function 
    calls. The resulting model artifact automatically handles preprocessing for 
    new datasets in the third step, ensuring consistent prediction pipelines.