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FoRecoML: Forecast Reconciliation with Machine Learning

Nonlinear forecast reconciliation with machine learning in cross-sectional (Spiliotis et al. 2021 <doi:10.1016/j.asoc.2021.107756>), temporal, and cross-temporal (Rombouts et al. 2024 <doi:10.1016/j.ijforecast.2024.05.008>) frameworks.

Version: 1.0.0
Depends: R (≥ 3.4), Matrix, FoReco
Imports: stats, cli, methods, randomForest, lightgbm, xgboost, mlr3, mlr3tuning, mlr3learners, paradox
Suggests: testthat (≥ 3.0.0), ranger
Published: 2026-04-21
DOI: 10.32614/CRAN.package.FoRecoML
Author: Daniele Girolimetto ORCID iD [aut, cre], Yangzhuoran Fin Yang ORCID iD [aut], Jeroen Rombouts ORCID iD [aut], Ines Wilms ORCID iD [aut]
Maintainer: Daniele Girolimetto <daniele.girolimetto at unipd.it>
BugReports: https://github.com/danigiro/FoRecoML/issues
License: GPL (≥ 3)
URL: https://github.com/danigiro/FoRecoML, https://danigiro.github.io/FoRecoML/
NeedsCompilation: no
Materials: README, NEWS
In views: TimeSeries
CRAN checks: FoRecoML results

Documentation:

Reference manual: FoRecoML.html , FoRecoML.pdf

Downloads:

Package source: FoRecoML_1.0.0.tar.gz
Windows binaries: r-release: FoRecoML_1.0.0.zip, r-oldrel: FoRecoML_1.0.0.zip
macOS binaries: r-release (arm64): FoRecoML_1.0.0.tgz, r-oldrel (arm64): FoRecoML_1.0.0.tgz, r-release (x86_64): FoRecoML_1.0.0.tgz, r-oldrel (x86_64): FoRecoML_1.0.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.