Generative Adversarial Nets (GAN) in R R package RGAN version 0.1.1 An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. 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. Software Imports: cli, torch, viridis Marcel Neunhoeffer <marcel.neunhoeffer@gmail.com> Comprehensive R Archive Network (CRAN) Marcel Neunhoeffer [aut, cre] (ORCID: <https://orcid.org/0000-0002-9137-5785>) MIT + file LICENSE (https://CRAN.R-project.org/package=RGAN/LICENSE) 2022-03-29 application/tgz https://CRAN.R-project.org/package=RGAN doi:10.32614/CRAN.package.RGAN