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, -a Random Forest based generator for both classification and regression problems -a density forest based generator for unsupervised data Data evaluation support tools include: -single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance -evaluation based on clustering using Adjusted Rand Index (ARI) and FM -evaluation based on classification performance with various learning models, eg, random forests.

Version: 2.0.1
Imports: CORElearn, RSNNS, MASS, nnet, cluster, mclust, fpc, stats, timeDate, robustbase, dendextend, ks, logspline, methods
Published: 2015-09-04
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_2.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: semiArtificial_2.0.1.tgz, r-oldrel: semiArtificial_1.2.0.tgz
OS X Mavericks binaries: r-release: semiArtificial_2.0.1.tgz
Old sources: semiArtificial archive

Reverse dependencies:

Reverse depends: ExplainPrediction