semiArtificial

Package: semiArtificial
Title: Generator of Semi-Artificial Data
Version: 2.0.1
Date: 2015-09-03
Author: Marko Robnik-Sikonja
Maintainer: Marko Robnik-Sikonja <marko.robnik@fri.uni-lj.si>
Description: 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.
License: GPL-3
URL: http://lkm.fri.uni-lj.si/rmarko/software/
Imports:
CORElearn,RSNNS,MASS,nnet,cluster,mclust,fpc,stats,timeDate,robustbase,dendextend,ks,logspline,methods
NeedsCompilation: no
Packaged: 2015-09-03 21:28:10 UTC; rmarko
Repository: CRAN
Date/Publication: 2015-09-04 01:11:01

Install log

* installing to library '/home/ddbj/local/lib64/R/library'
* installing *source* package 'semiArtificial' ...
** package 'semiArtificial' successfully unpacked and MD5 sums checked
** R
** preparing package for lazy loading
** help
*** installing help indices
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    dataSimilarity                          html  
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    performanceCompare                      html  
    rbfDataGen                              html  
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** building package indices
** testing if installed package can be loaded
* DONE (semiArtificial)
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