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RGAN
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]
[Scholar Profile | Author Map]
All associated links for this package
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 28 Aug. 2026.
Previous versions and publish date:
No previous versions
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imports, suggests or enhances
Complete documentation for RGAN
Functions, R codes and Examples using
the RGAN R package
Some associated functions: DCGAN_Discriminator . DCGAN_Generator . Discriminator . GAN_update_plot . GAN_update_plot_image . GAN_value_fct . Generator . KLWGAN_value_fct . WGAN_value_fct . WGAN_weight_clipper . data_transformer . expert_sample_synthetic_data . gan_trainer . gan_update_step . kl_fake . kl_gen . kl_real . sample_synthetic_data . sample_toydata . torch_rand_ab .
Some associated R codes: data-transformer.R . discriminator-architectures.R . gan-trainer.R . generator-architectures.R . post-gan.R . sample-synthetic-data.R . utility-functions.R . value-functions.R . Full RGAN package functions and examples
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