profoc: Probabilistic Forecast Combination Using CRPS Learning

Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <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: 0.8.5
Depends: R (≥ 3.0.2)
Imports: Rcpp (≥ 1.0.5), Matrix
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, splines2 (≥ 0.4.4)
Suggests: testthat (≥ 3.0.0), gamlss.dist, ggplot2
Published: 2021-10-22
Author: Jonathan Berrisch ORCID iD [cre], Florian Ziel ORCID iD [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
Materials: README NEWS
In views: TimeSeries
CRAN checks: profoc results

Documentation:

Reference manual: profoc.pdf

Downloads:

Package source: profoc_0.8.5.tar.gz
Windows binaries: r-devel: profoc_0.8.5.zip, r-devel-UCRT: profoc_0.8.5.zip, r-release: profoc_0.8.4.zip, r-oldrel: profoc_0.8.4.zip
macOS binaries: r-release (arm64): profoc_0.8.4.tgz, r-release (x86_64): profoc_0.8.4.tgz, r-oldrel: profoc_0.8.4.tgz
Old sources: profoc archive

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