Engineered features and "helper" functions ancillary to the
    'public.ctn0094data' package, extending this package for ease of use (see 
    <https://CRAN.R-project.org/package=public.ctn0094data>). This
    'public.ctn0094data' package contains harmonized datasets from some of the
    National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN)
    projects. Specifically, the CTN-0094 project is to harmonize and de-identify
    clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for
    opioid use disorder. This current version is built from 'public.ctn0094data'
    v. 1.0.6.

Performs sensitivity analysis for publication bias in meta-analyses
    (per Mathur & VanderWeele, 2020 [<doi:10.31219/osf.io/s9dp6>]). These analyses
    enable statements such as: "For publication bias to shift the observed point
    estimate to the null, 'significant' results would need to be at least
    30-fold more likely to be published than negative or 'nonsignificant'
    results." Comparable statements can be made regarding shifting to a chosen
    non-null value or shifting the confidence interval. Provides a worst-case
    meta-analytic point estimate under maximal publication bias obtained simply
    by conducting a standard meta-analysis of only the negative and
    "nonsignificant" studies.

Implements a unified interface for benchmarking meta-analytic
    publication bias correction methods through simulation studies (see 
    Bartoš et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides
    1) predefined data-generating mechanisms from the literature, 2) functions
    for running meta-analytic methods on simulated data, 3) pre-simulated
    datasets and pre-computed results for reproducible benchmarks, 4) tools for
    visualizing and comparing method performance.

Tools for Bayesian estimation of meta-analysis models that account
    for publications bias or p-hacking. For publication bias, this package
    implements a variant of the p-value based selection model of Hedges (1992)
    <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also
    implements the mixture of truncated normals model for p-hacking described
    in Moss and De Bin (2019) <arXiv:1911.12445>.