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 Oneoff 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 overdispersion 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 fouryear forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other stateoftheart aggregators by 1025%. 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 Rpackage, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example.
Package details 


Author  Ville Satopää [aut, cre, cph] 
Maintainer  Ville Satopää <ville.satopaa@gmail.com> 
License  GPL2 
Version  0.1.1 
Package repository  View on CRAN 
Installation 
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