Provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.

Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.

Forecasters predicting the chances of a future event may disagree due to
    differing evidence or noise. To harness the collective evidence of the crowd, 
    Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions"
    <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian
    aggregator that is regularized by analyzing the forecasters' disagreement and ascribing 
    over-dispersion to noise. This aggregator requires no user intervention and can be computed
    efficiently even for a large numbers of predictions. The author evaluates
    the aggregator on subjective probability predictions collected during
    a four-year forecasting tournament sponsored by the US intelligence community.
    The aggregator improves the accuracy of simple averaging by around 20% and
    other state-of-the-art aggregators by 10-25%. The advantage stems almost
    exclusively from improved calibration. This aggregator -- know as "the revealed
    aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event
    and b) the forecasters' common prior (p0) of the future event. In this R-package,
    the function sample_aggregator(p,p0,...) allows the user to calculate the revealed
    aggregator. Its use is illustrated with a simple example.

Provides functions to visualize combined action data in 'ggplot2'.
    Also provides functions for producing full BRAID analysis reports with 
    custom layouts and aesthetics, using the BRAID method originally described 
    in Twarog et al. (2016) <doi:10.1038/srep25523>.