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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.4.1 |
rolling linux/jammy R-4.5 | semiArtificial_2.4.1.tar.gz |
201.2 KiB |
2.4.1 |
rolling linux/noble R-4.5 | semiArtificial_2.4.1.tar.gz |
201.2 KiB |
2.4.1 |
rolling source/ R- | semiArtificial_2.4.1.tar.gz |
36.2 KiB |
2.4.1 |
latest linux/jammy R-4.5 | semiArtificial_2.4.1.tar.gz |
201.2 KiB |
2.4.1 |
latest linux/noble R-4.5 | semiArtificial_2.4.1.tar.gz |
201.2 KiB |
2.4.1 |
latest source/ R- | semiArtificial_2.4.1.tar.gz |
36.2 KiB |
2.4.1 |
2026-04-26 source/ R- | semiArtificial_2.4.1.tar.gz |
36.2 KiB |
2.4.1 |
2026-04-23 source/ R- | semiArtificial_2.4.1.tar.gz |
36.2 KiB |
2.4.1 |
2026-04-09 windows/windows R-4.5 | semiArtificial_2.4.1.zip |
204.1 KiB |
2.4.1 |
2025-04-20 source/ R- | semiArtificial_2.4.1.tar.gz |
36.2 KiB |