Detect and test for changes in covariance structures of functional data, as well as changepoint detection for multivariate data more generally. Method for detecting non-stationarity in resting state functional Magnetic Resonance Imaging (fMRI) scans as seen in Ramsay, K., & Chenouri, S. (2025) <doi:10.1080/10485252.2025.2503891> is implemented in fmri_changepoints(). Also includes depth- and rank-based implementation of the wild binary segmentation algorithm for detecting multiple changepoints in multivariate data.

Extends 'RT-QuIC' (Real-Time Quaking-Induced Conversion) statistical
    analysis to complex environmental matrices through hierarchical adaptive
    classification. 'KWELA' is named after a deity of the Fore people of Papua
    New Guinea, among whom Kuru, a notable human prion disease, was identified.
    Implements a 6-layer architecture: hard gate biological constraints,
    per-well adaptive scoring, separation-aware combination, Youden-optimized
    cutoffs, replicate consensus, and matrix instability detection. Features
    dual-mode operation (diagnostic/research), auto-profile selection
    (Standard/Sensitive/Matrix-Robust), RAF integration for artifact detection,
    matrix-aware baseline correction, and multiple consensus rules. Methods
    include energy distance (Szekely and Rizzo (2013) <doi:10.1016/j.jspi.2013.03.018>),
    CRPS (Gneiting and Raftery (2007) <doi:10.1198/016214506000001437>),
    SSMD (Zhang (2007) <doi:10.1016/j.ygeno.2007.01.005>),
    and Jensen-Shannon divergence (Lin (1991) <doi:10.1109/18.61115>). This
    package implements methodology currently under peer review; please contact
    the author before publication using this approach. Development followed an
    iterative human-machine collaboration where all algorithmic design,
    statistical methodologies, and biological validation logic were
    conceptualized, tested, and iteratively refined by Richard A. Feiss through
    repeated cycles of running experimental data, evaluating analytical outputs,
    and selecting among candidate algorithms and approaches. AI systems
    ('Anthropic Claude' and 'OpenAI GPT') served as coding assistants and
    analytical sounding boards under continuous human direction. The selection
    of statistical methods, evaluation of biological plausibility, and all final
    methodology decisions were made by the human author. AI systems did not
    independently originate algorithms, statistical approaches, or scientific
    methodologies.