Person-specific and within-person network estimation from
    intensive longitudinal and panel data. Estimators include ordinary
    vector autoregression (VAR), graphical vector autoregression (graphical
    VAR), multilevel vector autoregression (mlVAR), rolling ordinary and
    graphical VAR, native Bayesian VAR and multilevel Bayesian VAR, unified
    Structural Equation Modeling (uSEM), and Group Iterative Multiple Model
    Estimation (GIMME). All estimators are native clean-room
    implementations. All functions are validated against
    authoritative literature. Also provides preprocessing audits,
    edge-stability diagnostics, model-comparison reports, and rolling
    forecast validation. Methods are described in
    <doi:10.1007/978-3-031-95365-1_20> and
    <doi:10.1080/00273171.2018.1454823>.

Carry out comparative authorship analysis of disputed and undisputed texts within the Likelihood Ratio Framework for expressing evidence in forensic science. This package contains implementations of well-known algorithms for comparative authorship analysis, such as Smith and Aldridge's (2011) Cosine Delta <doi:10.1080/09296174.2011.533591> or Koppel and Winter's (2014) Impostors Method <doi:10.1002/asi.22954>, as well as functions to measure their performance and to calibrate their outputs into Log-Likelihood Ratios.

A toolkit for idionomic science, a research philosophy that places
    the unit of the ensemble (individual/couple/group) at the center of analysis.
    Rather than assuming a common distribution, a similar enough process for each
    unit, and fitting a single model to the whole ensemble, idionomic methods model
    each unit separately, then aggregate upward if sensible. The group-level picture
    emerges from individual results, not the other way around, while explicitly
    evaluating whether aggregation is reasonable given the measured level of
    heterogeneity of effects. The package is built around intensive longitudinal
    data where each participant contributes a time series. It provides a pipeline
    from preprocessing through modeling to group-level summaries. Current functions:
    data quality screening (i_screener()), within-person standardization
    (pmstandardize()), linear detrending (i_detrender()), per-subject
    ARIMAX (AutoRegressive Integrated Moving Average with eXogenous inputs)
    modeling and meta-analysis (iarimax()), individual p-values (i_pval()),
    Sign Divergence and Equisyncratic Null tests (sden_test()), and directed
    loop detection (looping_machine()). Methods are described in
    Hernandez et al. (2024) <doi:10.1007/978-3-030-77644-2_136-1>,
    Ciarrochi et al. (2024) <doi:10.1007/s10608-024-10486-w>, and
    Sahdra et al. (2024) <doi:10.1016/j.jcbs.2024.100728>.