Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.

Pairwise Hamming distances are computed between the rows of a binary (0/1) matrix using highly optimized 'C' code. The input is an integer matrix where each row represents a binary feature vector and returns a symmetric integer matrix of pairwise distances. Internally, rows are bit-packed into 64-bit words for fast XOR-based comparisons, with hardware-accelerated popcount operations to count differences. 'OpenMP' parallelization ensures efficient performance for large matrices.

Implementation of FastICA algorithm to perform Independent
        Component Analysis (ICA) and Projection Pursuit.

TrainFastImputation() uses training data to describe a
    multivariate normal distribution that the data approximates or
    can be transformed into approximating and stores this information
    as an object of class 'FastImputationPatterns'. FastImputation()
    function uses this 'FastImputationPatterns' object to impute (make
    a good guess at) missing data in a single line or a whole data frame
    of data.  This approximates the process used by 'Amelia'
    <https://gking.harvard.edu/amelia> but is much faster when
    filling in values for a single line of data.

A joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>.
             The time-to-event data is modelled using a (cause-specific) Cox 
             proportional hazards regression model with time-fixed covariates. 
             The longitudinal biomarkers are modelled using a linear mixed 
             effects model. The association between the longitudinal submodel 
             and the survival submodel is captured through shared random 
             effects. It allows researchers to analyze large-scale data to 
             model biomarker trajectories, estimate their effects on event 
             outcomes, and dynamically predict future events from patients’ 
             past histories. A function for simulating survival and longitudinal 
             data for multiple biomarkers is also included alongside built-in 
             datasets.