Package: profoc 1.3.4

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>.

Authors:Jonathan Berrisch [aut, cre], Florian Ziel [aut]

profoc_1.3.4.tar.gz
profoc_1.3.4.zip(r-4.7-x86_64)profoc_1.3.4.zip(r-4.7-arm64)profoc_1.3.4.zip(r-4.6-x86_64)profoc_1.3.4.zip(r-4.6-arm64)profoc_1.3.4.zip(r-4.5-x86_64)
profoc_1.3.4.tgz(r-4.6-x86_64)profoc_1.3.4.tgz(r-4.6-arm64)profoc_1.3.4.tgz(r-4.5-x86_64)profoc_1.3.4.tgz(r-4.5-arm64)
profoc_1.3.4.tar.gz(r-4.7-arm64)profoc_1.3.4.tar.gz(r-4.7-x86_64)profoc_1.3.4.tar.gz(r-4.6-arm64)profoc_1.3.4.tar.gz(r-4.6-x86_64)
profoc_1.3.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
profoc/json (API)

# Install 'profoc' in R:
install.packages('profoc', repos = c('https://berrij.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/berrij/profoc/issues

Pkgdown/docs site:https://profoc.berrisch.biz

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

5.50 score 14 stars 15 scripts 556 downloads 13 exports 31 dependencies

Last updated from:b5073ca70a. Checks:4 ERROR, 11 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR232
linux-devel-x86_64OK267
source / vignettesOK347
linux-release-arm64ERROR259
linux-release-x86_64OK199
macos-release-arm64OK215
macos-release-x86_64OK462
macos-oldrel-arm64OK183
macos-oldrel-x86_64OK564
windows-devel-arm64ERROR336
windows-devel-x86_64OK341
windows-release-arm64ERROR293
windows-release-x86_64OK278
windows-oldrel-x86_64OK295
wasm-releaseOK165

Exports:autoplotbatchconlineinit_experts_listmake_basis_matsmake_hat_matsmake_knotsonlineoraclepenaltypost_process_modelsplines2_basistidy

Dependencies:abindclicpp11farvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMatrixpillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppProgressrcpptimerrlangS7scalessplines2tibbleutf8vctrsviridisLitewithr

Introduction
A Short Introduction to profoc | Online Learning | References

Last update: 2024-01-10
Started: 2023-08-28

Production
Using online in Production | Combining new expert predictions | Updating the model weights | Summary on predict() and update()

Last update: 2024-01-10
Started: 2024-01-10

Using the C++ Interface
Introduction | Online learning with conline | Accessing the results | Summary

Last update: 2024-01-10
Started: 2024-01-10