ganGenerativeData: Generate Generative Data for a Data Source

Generative Adversarial Networks are applied to generate generative data for a data source. In iterative training steps the distribution of generated data converges to that of the data source. An application of generative data is the created density value function which is used to classify data records. Reference: Goodfellow et al. (2014) <arXiv:1406.2661v1>.

Version: 1.2.0
Imports: Rcpp (≥ 1.0.3), tensorflow (≥ 2.0.0)
LinkingTo: Rcpp
Published: 2021-10-09
Author: Werner Mueller
Maintainer: Werner Mueller <werner.mueller5 at chello.at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: ganGenerativeData results

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Reference manual: ganGenerativeData.pdf
Package source: ganGenerativeData_1.2.0.tar.gz
Windows binaries: r-devel: ganGenerativeData_1.1.1.zip, r-devel-UCRT: ganGenerativeData_1.2.0.zip, r-release: ganGenerativeData_1.2.0.zip, r-oldrel: ganGenerativeData_1.1.1.zip
macOS binaries: r-release (arm64): ganGenerativeData_1.1.1.tgz, r-release (x86_64): ganGenerativeData_1.2.0.tgz, r-oldrel: ganGenerativeData_1.2.0.tgz
Old sources: ganGenerativeData archive

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