ganDataModel: Create a Hierarchical, Categorical Data Model for a Data Source

Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analysed for different levels. For each level subspaces with density values above a level are determined. The obtained set of subspaces categorizes the data source hierarchically. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' <>.

Version: 1.0.2
Imports: Rcpp (≥ 1.0.3), tensorflow (≥ 2.0.0)
LinkingTo: Rcpp
Published: 2022-07-16
Author: Werner Mueller
Maintainer: Werner Mueller <werner.mueller5 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: TensorFlow (
CRAN checks: ganDataModel results


Reference manual: ganDataModel.pdf


Package source: ganDataModel_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ganDataModel_1.0.2.tgz, r-oldrel (arm64): ganDataModel_1.0.2.tgz, r-release (x86_64): ganDataModel_1.0.2.tgz, r-oldrel (x86_64): ganDataModel_1.0.2.tgz


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