Forecast Reconciliation with Machine Learning R package FoRecoML version 1.0.0 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. Software Depends: R (>= 3.4), Matrix, FoReco Imports: stats, cli, methods, randomForest, lightgbm, xgboost, mlr3, mlr3tuning, mlr3learners, paradox Suggests: testthat (>= 3.0.0), ranger Daniele Girolimetto <daniele.girolimetto@unipd.it> Comprehensive R Archive Network (CRAN) Daniele Girolimetto [aut, cre] (ORCID: <https://orcid.org/0000-0001-9387-1232>), Yangzhuoran Fin Yang [aut] (ORCID: <https://orcid.org/0000-0002-1232-8017>), Jeroen Rombouts [aut] (ORCID: <https://orcid.org/0000-0003-2255-4875>), Ines Wilms [aut] (ORCID: <https://orcid.org/0000-0003-3269-4601>) GPL (>= 3) 2026-04-21 application/tgz https://CRAN.R-project.org/package=FoRecoML doi:10.32614/CRAN.package.FoRecoML