Deterministic, name-blind detection of variable roles (group,
    outcome, survival time and event, paired and agreement measurements,
    repeated measures, scale items, subject identifier, covariate) in tabular
    data. Roles are assigned from each column's information-theoretic signature
    -- Shannon entropy, normalized mutual information, and distributional shape
    -- rather than from column names, so renaming columns to 'col_1', 'col_2',
    ... does not change the result ("Data inspice, non nomen"). An optional,
    capped name-based hint and automatic header-row detection are also provided.
    No large language models and no external data transmission. Extracted from
    the 'MDStatR' biostatistics engine; see Boynukara (2026)
    <doi:10.5281/zenodo.20707791>.

Fast and efficient computation of rolling and expanding statistics for time-series data.

Wraps the 'Ollama' <https://ollama.com> API, which can be used to 
    communicate with generative large language models locally.

Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.

R Markdown output formats based on JavaScript libraries such as
    'Scrollama' (<https://github.com/russellsamora/scrollama>) for storytelling.

Fast and efficient computation of rolling and expanding
    eigenanalysis for time-series data. The 'rolleigen' package decomposes
    the covariance matrix of the explanatory variables into eigenvalues
    and eigenvectors to perform principal component analysis (Pearson,
    1901, <doi:10.1080/14786440109462720>; Hotelling, 1933,
    <doi:10.1037/h0071325>) and principal component regression (Massy,
    1965, <doi:10.1080/01621459.1965.10480787>) over rolling and expanding
    windows. For each window, the eigenvalues and eigenvectors are
    computed from the covariance matrix and, optionally, ordered from
    largest to smallest to summarize the directions of greatest variation
    in the data. A subset of leading components is then used to fit a
    regression that mitigates collinearity in the explanatory variables.
    Use cases include dimensionality reduction, factor extraction, and
    regression on collinear explanatory variables. The package supports
    rolling and expanding windows, weights, and handling of missing values
    via the min_obs, complete_obs, and na_restore arguments. The
    implementation uses the online and offline algorithms from the 'roll'
    package to compute rolling and expanding cross-products efficiently,
    with parallelism across columns and windows provided by
    'RcppParallel'.