solutions to reach typical significance levels: brQCA(). After applying recommendations from brQCA(), QCAdiff() shows which cases are excluded from the final result.
Depends: R (>= 3.2.3), dplyr, QCA, bootstrap
Packaged: 2018-02-19 00:43:21 UTC; burrelvannjr

solutions to reach typical significance levels: brQCA(). Data included
Packaged: 2022-01-18 17:39:16 UTC; burrelvannjr
Burrel Vann Jr [aut, cre] (<https://orcid.org/0000-0003-3066-5815>)
Maintainer: Burrel Vann Jr <bvannjr@sdsu.edu>

Packaged: 2022-01-29 16:10:38 UTC; burrelvannjr

Description: 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.

Description: 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.

comment = "Author of the 'braces' Javascript library"))
Jon Schlinkert [aut] (Author of the 'braces' Javascript library)

Title: Calculate the Revealed Aggregator of Probability Predictions
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"
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.
Packaged: 2021-04-16 12:33:30 UTC; ville.satopaa