ganGenerativeData: Generate Generative Data for a Data Source

Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data classifying and missing data completion. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.

Version: 2.0.2
Imports: Rcpp (≥ 1.0.3), tensorflow (≥ 2.0.0), httr (≥ 1.4.7)
LinkingTo: Rcpp
Published: 2024-06-23
DOI: 10.32614/CRAN.package.ganGenerativeData
Author: Werner Mueller
Maintainer: Werner Mueller <werner.mueller5 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: TensorFlow (
CRAN checks: ganGenerativeData results


Reference manual: ganGenerativeData.pdf


Package source: ganGenerativeData_2.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ganGenerativeData_2.0.2.tgz, r-oldrel (arm64): ganGenerativeData_2.0.2.tgz, r-release (x86_64): ganGenerativeData_2.0.2.tgz, r-oldrel (x86_64): ganGenerativeData_2.0.2.tgz
Old sources: ganGenerativeData archive


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