Package: RGAN
Title: Generative Adversarial Nets (GAN) in R
Version: 0.1.1
Authors@R: 
    person(given = "Marcel",
           family = "Neunhoeffer",
           role = c("aut", "cre"),
           email = "marcel.neunhoeffer@gmail.com",
           comment = c(ORCID = "0000-0002-9137-5785"))
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 <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.
License: MIT + file LICENSE
URL: https://github.com/mneunhoe/RGAN
BugReports: https://github.com/mneunhoe/RGAN/issues
Encoding: UTF-8
RoxygenNote: 7.1.2
Imports: cli, torch, viridis
NeedsCompilation: no
Packaged: 2022-03-28 21:25:13 UTC; marcelneunhoeffer
Author: Marcel Neunhoeffer [aut, cre] (<https://orcid.org/0000-0002-9137-5785>)
Maintainer: Marcel Neunhoeffer <marcel.neunhoeffer@gmail.com>
Repository: CRAN
Date/Publication: 2022-03-29 18:30:06 UTC
Built: R 4.6.0; ; 2026-04-26 09:57:15 UTC; unix
