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) <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
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

Documentation:

Reference manual:	gainML.pdf
Vignettes:	Implementation

Downloads:

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

Linking:

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