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.

Implements scalable joint models for large-scale competing risks time-to-event data with one or multiple longitudinal biomarkers using the efficient algorithms developed by Li et al. (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>.
             The time-to-event process is modeled using a cause-specific Cox proportional hazards model
             with time-fixed covariates, while longitudinal biomarkers are modeled
             using linear mixed-effects models. The association between the longitudinal
             and survival processes is captured through shared random effects. The
             package enables analysis of large-scale biomedical data to model biomarker
             trajectories, estimate their effects on event risks, and perform dynamic
             prediction of future events based on patients' longitudinal histories.
             Functions for simulating survival and longitudinal data for multiple
             biomarkers are included, along with built-in example datasets. The package
             also supports modeling a single biomarker with heterogeneous within-subject
             variability via functionality adapted from the 'JMH' package.