Package: semiArtificial 2.4.1
semiArtificial: Generator of Semi-Artificial Data
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 generators: i) a RBF network based generator using rbfDDA() from package 'RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests.
Authors:
semiArtificial_2.4.1.tar.gz
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semiArtificial_2.4.1.tgz(r-4.4-any)semiArtificial_2.4.1.tgz(r-4.3-any)
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semiArtificial.pdf |semiArtificial.html✨
semiArtificial/json (API)
| # Install 'semiArtificial' in R: |
| install.packages('semiArtificial', repos = c('https://rmarko.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:c92aafb2b1. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 21 2024 |
| R-4.5-win | OK | Aug 21 2024 |
| R-4.5-linux | OK | Aug 21 2024 |
| R-4.4-win | OK | Aug 21 2024 |
| R-4.4-mac | OK | Aug 21 2024 |
| R-4.3-win | OK | Aug 21 2024 |
| R-4.3-mac | OK | Aug 21 2024 |
Exports:cleanDatadataSimilaritydsClustCompareindAttrGennewdatanewdata.RBFgeneratornewdata.TreeEnsembleperformanceComparerbfDataGentreeEnsemble
Dependencies:classcliclustercolorspaceCORElearnDBIDEoptimRdiptestdplyrfansifarverflexclustflexmixFNNfpcgenericsggplot2gluegtableisobandkernlabKernSmoothkslabelinglatticelifecyclelogsplinelpSolvemagrittrMASSMatrixmcclustmclustmgcvminqamitoolsmodeltoolsmulticoolmunsellmvtnormnlmennetnumDerivpillarpkgconfigplotrixprabcluspracmaproxyR6RColorBrewerRcppRcppArmadillorlangrobustbaserpartrpart.plotRSNNSscalesStatMatchsurveysurvivaltibbletidyselecttimeDateutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Generation and evaluation of semi-artificial data | semiArtificial-package semiArtificial |
| Rejection of new instances based on their distance to existing instances | cleanData |
| Evaluate statistical similarity of two data sets | dataSimilarity |
| Evaluate clustering similarity of two data sets | dsClustCompare |
| Generate semi-artificial data using a generator | newdata newdata.RBFgenerator newdata.TreeEnsemble |
| Evaluate similarity of two data sets based on predictive performance | performanceCompare |
| A data generator based on RBF network | rbfDataGen |
| A data generator based on forest | indAttrGen treeEnsemble |
