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gainML  

Machine Learning-Based Analysis of Potential Power Gain from Passive Device Installation on Wind Turbine Generators
View on CRAN: Click here


Download and install gainML package within the R console
Install from CRAN:
install.packages("gainML")

Install from Github:
library("remotes")
install_github("cran/gainML")

Install by package version:
library("remotes")
install_version("gainML", "0.1.0")



Attach the package and use:
library("gainML")
Maintained by
Hoon Hwangbo
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-28
Latest Update: 2019-06-28
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) .
How to cite:
Hoon Hwangbo (2019). gainML: Machine Learning-Based Analysis of Potential Power Gain from Passive Device Installation on Wind Turbine Generators. R package version 0.1.0, https://cran.r-project.org/web/packages/gainML. Accessed 10 Mar. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited gainML R package
View gainML citation profile
Other R packages that gainML depends, imports, suggests or enhances
Complete documentation for gainML
Functions, R codes and Examples using the gainML R package
Some associated functions: analyze.gain . analyze.p1 . analyze.p2 . arrange.data . bootstrap.gain . pw.freq . quantify.gain . wtg . 
Some associated R codes: analysis_total.R . arrange_data.R . calc_gcv.R . copyright.R . est_bw.R . fit_models.R . freq_data.R . norm_data.R . period1.R . period2.R . prediction.R . quantify_gain.R . quantify_uncertainty.R . wtg_data.R .  Full gainML package functions and examples
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