Package: profoc 1.3.3

Jonathan Berrisch

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_1.3.3.tar.gz
profoc_1.3.3.tar.gz(r-4.5-noble)profoc_1.3.3.tar.gz(r-4.4-noble)
profoc_1.3.3.tgz(r-4.4-emscripten)profoc_1.3.3.tgz(r-4.3-emscripten)
profoc.pdf |profoc.html
profoc/json (API)
NEWS

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

Peer review:

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

Pkgdown:https://profoc.berrisch.biz

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

3.77 score 13 scripts 414 downloads 13 exports 35 dependencies

Last updated 3 months agofrom:7c8fdf57f2. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linux-x86_64OKNov 21 2024

Exports:autoplotbatchconlineinit_experts_listmake_basis_matsmake_hat_matsmake_knotsonlineoraclepenaltypost_process_modelsplines2_basistidy

Dependencies:abindclicolorspacefansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppProgressrcpptimerrlangscalessplines2tibbleutf8vctrsviridisLitewithr

Introduction

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Production

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Using the C++ Interface

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