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RGAN  

Generative Adversarial Nets (GAN) in R
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("RGAN", "0.1.1")



Attach the package and use:
library("RGAN")
Maintained by
Marcel Neunhoeffer
[Scholar Profile | Author Map]
First Published: 2022-03-29
Latest Update: 2022-03-29
Description:
An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 . A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.
How to cite:
Marcel Neunhoeffer (2022). RGAN: Generative Adversarial Nets (GAN) in R. R package version 0.1.1, https://cran.r-project.org/web/packages/RGAN. Accessed 10 Apr. 2025.
Previous versions and publish date:
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Complete documentation for RGAN
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