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>.

Contains functions for evaluating, analyzing, and fitting combined
    action dose response surfaces with the Bivariate Response to Additive
    Interacting Doses (BRAID) model of combined action, along with tools for
    implementing other combination analysis methods, including Bliss independence,
    combination index, and additional response surface methods.

Deals with the braid groups. Includes creation of some
    specific braids, group operations, free reduction, and Bronfman
    polynomials. Braid theory has applications in fluid mechanics and
    quantum physics. The code is adapted from the 'Haskell' library
    'combinat', and is based on Birman and Brendle (2005)
    <doi:10.48550/arXiv.math/0409205>.