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Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <doi:10.48550/arXiv.2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <doi:10.48550/arXiv.1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
Category: Technology
profoc.berrisch.biz1
Structured Data
8
Content Structure
5
Entity Clarity
3
E-E-A-T Signals
8
Technical AEO
5
AI Discoverability
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Scored by Engagemii on May 29, 2026. Methodology: engagemii.com/aeo/methodology
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Cite this score: Engagemii (2026). "AEO Score for Probabilistic Forecast Combination Using CRPS Learning • profoc." Retrieved from https://engagemii.com/aeo/brands/profoc-berrisch-biz
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