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. Direct applications of generative data are the created functions for outlier detection and missing data completion. Reference: Goodfellow et al. (2014) <arXiv:1406.2661v1>.

Version:	1.3.3
Imports:	Rcpp (≥ 1.0.3), tensorflow (≥ 2.0.0)
LinkingTo:	Rcpp
Published:	2022-02-16
Author:	Werner Mueller
Maintainer:	Werner Mueller <werner.mueller5 at chello.at>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
SystemRequirements:	TensorFlow (https://www.tensorflow.org)
CRAN checks:	ganGenerativeData results

Documentation:

Reference manual:

ganGenerativeData.pdf

Downloads:

Package source:	ganGenerativeData_1.3.3.tar.gz
Windows binaries:	r-devel: ganGenerativeData_1.3.3.zip, r-release: ganGenerativeData_1.3.3.zip, r-oldrel: ganGenerativeData_1.3.3.zip
macOS binaries:	r-release (arm64): ganGenerativeData_1.3.3.tgz, r-oldrel (arm64): ganGenerativeData_1.3.3.tgz, r-release (x86_64): ganGenerativeData_1.3.3.tgz, r-oldrel (x86_64): ganGenerativeData_1.3.3.tgz
Old sources:	ganGenerativeData archive

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