person(given = "Trevor",
family = "Davis",
role = c("aut", "cre"),
email = "trevor.l.davis@gmail.com")
Performs brace expansions on strings.  Made popular by Unix shells, brace expansion allows users to quickly generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string.  The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.

Performs brace expansions on strings.  Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string.  The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.

c(person(given = "Trevor",
family = "Davis",
role = c("aut", "cre"),
email = "trevor.l.davis@gmail.com"),
person(given = "Jon",
family = "Schlinkert",
role = "aut",
comment = "Author of the 'braces' Javascript library"))
Trevor Davis [aut, cre],
Jon Schlinkert [aut] (Author of the 'braces' Javascript library)

Calculate the Revealed Aggregator of Probability Predictions
c(person(given = "Ville",
family = "Satopää",
email = "ville.satopaa@gmail.com",
role = c("aut", "cre", "cph")))
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' base rate (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 stylized example.