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: 1.3.3 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: 2024-09-21 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=profoc to link to this page.
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