Package: profoc 1.3.2

profoc: Probabilistic Forecast Combination Using CRPS Learning

Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <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) <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]

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

On CRAN:

13 exports 13 stars 1.92 score 35 dependencies 13 scripts 366 downloads

Last updated 6 months agofrom:c84315dca8. Checks:OK: 9. Indexed: yes.

Exports:autoplotbatchconlineinit_experts_listmake_basis_matsmake_hat_matsmake_knotsonlineoraclepenaltypost_process_modelsplines2_basistidy

Dependencies:abindclicolorspacefansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppProgressrcpptimerrlangscalessplines2tibbleutf8vctrsviridisLitewithr