Package: gainML
Type: Package
Date: 2019-06-25
Title: Machine Learning-Based Analysis of Potential Power Gain from
        Passive Device Installation on Wind Turbine Generators
Version: 0.1.0
Authors@R: c(
  person("Hoon", "Hwangbo", email = "hhwangb1@utk.edu", role = c("aut","cre")),
  person("Yu", "Ding", email = "yuding@tamu.edu", role = c("aut")),
  person("Daniel", "Cabezon", email = "Daniel.Cabezon@edpr.com", role = c("aut")),
  person("Texas A&M University", role = c("cph")),
  person("EDP Renewables", role = c("cph")))
Author: Hoon Hwangbo [aut, cre],
  Yu Ding [aut],
  Daniel Cabezon [aut],
  Texas A&M University [cph],
  EDP Renewables [cph]
Maintainer: Hoon Hwangbo <hhwangb1@utk.edu>
Copyright: Copyright (c) 2019 Y. Ding, H. Hwangbo, Texas A&M
        University, D. Cabezon, and EDP Renewables
Description: 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>.
Depends: R (>= 3.6.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: fields (>= 9.0), FNN (>= 1.1), utils, stats
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-06-25 16:50:02 UTC; User
Repository: CRAN
Date/Publication: 2019-06-28 13:40:07 UTC
Built: R 4.6.0; ; 2026-04-26 00:44:24 UTC; unix
