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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 utk.edu> |
| 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: gainML_0.1.0.zip, r-release: gainML_0.1.0.zip, r-oldrel: gainML_0.1.0.zip |
| 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 |
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These binaries (installable software) and packages are in development.
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