semiArtificial: Generator of semi-artificial data

Package semiArtificial contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generator: -a RBF network based generator using rbfDDA from RSNNS package. More generators are planned in near future. Data evaluation support tools include: -single attribute based statistical evaluation: mean, median,standard deviation, skewness, kurtosis, KS test, Hellinger distance -evaluation based on clustering using Adjusted Rand Index (ARI) -evaluation based on classification performance with various learning models, eg, random forests.

Version: 1.2.0
Imports: RSNNS, CORElearn, MASS, nnet, cluster, mclust, fpc, stats, timeSeries, timeDate
Published: 2014-03-17
Author: Marko Robnik-Sikonja
Maintainer: Marko Robnik-Sikonja <marko.robnik at>
License: GPL-3
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: semiArtificial results


Reference manual: semiArtificial.pdf
Package source: semiArtificial_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: semiArtificial_1.2.0.tgz, r-oldrel: semiArtificial_1.2.0.tgz
OS X Mavericks binaries: r-release: semiArtificial_1.2.0.tgz
Old sources: semiArtificial archive