Provides a unified framework for sparse-group regularization and
    precision matrix estimation in Gaussian graphical models. It implements
    multiple sparse-group penalties, including sparse-group lasso, sparse-group
    adaptive lasso, sparse-group SCAD, and sparse-group MCP, and solves them
    efficiently using ADMM-based optimization. The package is designed for
    high-dimensional network inference where both sparsity and group structure
    are present.

Generates a Report Card for rater reliability on binary
    outcomes from an N x k subject-by-rater rating matrix, on both the
    inter-rater and intra-rater axes. Each panel coefficient is
    positioned on a data-generating-process-calibrated reference surface
    conditioned on the study's rater count, sample size, and prevalence,
    yielding a pooled percentile (the coefficient's position within the
    design's achievable agreement range) together with a consistency band
    on panel quality: the quality levels whose sampling distributions are
    consistent with the observed value at that design. The panel
    coefficients are the prevalence-adjusted bias-adjusted kappa (PABAK)
    of Byrt, Bishop, and Carlin (1993) <doi:10.1016/0895-4356(93)90018-V>,
    the first-order agreement coefficient (AC1) of Gwet (2008)
    <doi:10.1348/000711006X126600>, the multi-rater kappa of Fleiss (1971)
    <doi:10.1037/h0031619>, and the observed intraclass correlation. A
    cross-coefficient discordance diagnostic (delta-hat) reports the
    spread of the coefficients' implied panel qualities and flags panels
    for which no single coefficient is a stable summary by the spread's
    percentile on a matched null distribution; for such divergent panels
    the report routes to a pairwise PABAK matrix and per-rater sensitivity
    and specificity recovered from the latent-class model of Dawid and
    Skene (1979) <doi:10.2307/2346806>, with the two-rater bounds of Hui
    and Walter (1980) <doi:10.2307/2530508>.

Facilitates the citation of R packages used in analysis
    projects. Scans project for packages used, gets their citations, and
    produces a document with citations in the preferred bibliography
    format, ready to be pasted into reports or manuscripts. Alternatively,
    'grateful' can be used directly within an 'R Markdown' or 'Quarto' document.