profoc: Probabilistic Forecast Combination Using CRPS Learning

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>.

Version:	1.3.4
Depends:	R (≥ 4.3.0)
Imports:	Rcpp (≥ 1.0.5), Matrix, abind, methods, lifecycle, generics, tibble, ggplot2
LinkingTo:	Rcpp, RcppArmadillo (≥ 0.10.7.5.0), RcppProgress, splines2 (≥ 0.4.4), rcpptimer (≥ 1.2.0)
Suggests:	testthat (≥ 3.0.0), gamlss.dist, knitr, rmarkdown, dplyr, rcpptimer (≥ 1.2.0)
Published:	2026-01-23
DOI:	10.32614/CRAN.package.profoc
Author:	Jonathan Berrisch [aut, cre], Florian Ziel [aut]
Maintainer:	Jonathan Berrisch <Jonathan at Berrisch.biz>
BugReports:	https://github.com/BerriJ/profoc/issues
License:	GPL (≥ 3)
URL:	https://profoc.berrisch.biz, https://github.com/BerriJ/profoc
NeedsCompilation:	yes
Language:	en-US
Citation:	profoc citation info
Materials:	README, NEWS
In views:	TimeSeries
CRAN checks:	profoc results

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