Provides visual representations of risk-of-bias assessments 
    using the ROBUST-RCT framework, as described in Wang et al. (2025) 
    <doi:10.1136/bmj-2024-081199>. The graphical visualization displays 
    both factual evaluation (Step 1) and judgment (Step 2).

Implements several methods for False Discovery Rate control based on the e-Closure Principle,
  in particular the Closed e-Benjamini-Hochberg and Closed Benjamini-Yekutieli procedures.

Functions for easy building of error correction models (ECM) for time series regression.

Implements the Elliptical Covering Marginal Likelihood Estimator
    (ECMLE), a geometric method for approximating marginal likelihood from
    posterior draws and log-posterior evaluations. The method constructs a
    collection of non-overlapping ellipsoids in a high-posterior-density
    region, computes the covered volume, and combines this with posterior
    sample coverage to estimate model evidence. It is designed to stabilize
    harmonic-mean-based evidence approximation and can be applied in
    multimodal settings. The methodology is described in Naderi et al. (2025)
    <doi:10.48550/arXiv.2510.20617>.

Programmatic interface to the European Centre for Medium-Range
    Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>)
    and Copernicus's Data Stores. Allows for easy downloads of weather 
    forecasts and climate reanalysis data in R. Data stores covered include the Climate Data Store (CDS; 
    <https://cds.climate.copernicus.eu>), Atmosphere Data Store (ADS; 
    <https://ads.atmosphere.copernicus.eu>) and Early Warning Data Store (CEMS; 
    <https://ewds.climate.copernicus.eu>).

Implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <doi:10.1093/pan/mpm017>) and (2011 <doi:10.18637/jss.v042.i05>) for ecological inference in 2 by 2 tables as well as the method of bounds introduced by Duncan and Davis (1953).  The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models).  For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models.