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
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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 412 downloads 13 exports 31 dependencies

Last updated from:b5073ca70a. Checks:2 ERROR, 11 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR263
linux-devel-x86_64OK250
source / vignettesOK365
linux-release-arm64ERROR242
linux-release-x86_64OK250
macos-release-arm64OK204
macos-release-x86_64OK414
macos-oldrel-arm64OK181
macos-oldrel-x86_64OK379
windows-develOK316
windows-releaseOK378
windows-oldrelOK301
wasm-releaseOK154

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