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gainML
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]
[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
Previous versions and publish date:
No previous versions
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Other R packages that gainML depends,
imports, suggests or enhances
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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