gainML: Machine Learning-Based Analysis of Potential Power Gain from Passive Device Installation on Wind Turbine Generators

Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators. H. Hwangbo, Y. Ding, and D. Cabezon (2019) <doi:10.48550/arXiv.1906.05776>.

Version: 0.1.0
Depends: R (≥ 3.6.0)
Imports: fields (≥ 9.0), FNN (≥ 1.1), utils, stats
Suggests: knitr, rmarkdown
Published: 2019-06-28
DOI: 10.32614/CRAN.package.gainML
Author: Hoon Hwangbo [aut, cre], Yu Ding [aut], Daniel Cabezon [aut], Texas A&M University [cph], EDP Renewables [cph]
Maintainer: Hoon Hwangbo <hhwangb1 at>
License: GPL-3
Copyright: Copyright (c) 2019 Y. Ding, H. Hwangbo, Texas A&M University, D. Cabezon, and EDP Renewables
NeedsCompilation: no
CRAN checks: gainML results


Reference manual: gainML.pdf
Vignettes: Implementation


Package source: gainML_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): gainML_0.1.0.tgz, r-oldrel (arm64): gainML_0.1.0.tgz, r-release (x86_64): gainML_0.1.0.tgz, r-oldrel (x86_64): gainML_0.1.0.tgz


Please use the canonical form to link to this page.