Several algorithms support parallel multithreaded execution via OpenMP.

Title: Classification, regression, feature evaluation and ordinal
It is especially strong in feature evaluation where it contains several variants of Relief
algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM...
Its additional strength is ordEval algorithm and its visualization used for evaluation of data sets with ordinal features and class.

Title: Classification, Regression and Feature Evaluation
Author: Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao
Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with ordinal features and class.

Description: The package is a port of stand-alone C++ software to R.
It contains several machine learning model learning techniques in classification and regression,
algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM.
The top-level documentation is reachable through ?CORElearn.

Package: CORElearn
Title: Classification, Regression and Feature Evaluation
Version: 0.9.46
Date: 2015-05-29
Author: Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao
Maintainer: "Marko Robnik-Sikonja" <marko.robnik@fri.uni-lj.si>
Description: This is a suite of machine learning algorithms written in C++ with R interface.
It contains several machine learning model learning techniques in classification and regression,
for example classification and regression trees with optional constructive induction and models in the leaves,
random forests, kNN, naive Bayes, and locally weighted regression.
It is especially strong in feature evaluation where it contains several variants of Relief
algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM.
These methods can be used for example to discretize numeric attributes.
Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with
ordinal features and class enabling analysis according to the Kano model.
Several algorithms support parallel multithreaded execution via OpenMP.
The top-level documentation is reachable through ?CORElearn.
License: GPL-3
URL: http://lkm.fri.uni-lj.si/rmarko/software/
Imports: cluster, rpart, stats
Suggests: lattice,MASS,rpart.plot
NeedsCompilation: yes
Packaged: 2015-06-02 12:04:19 UTC; rmarko
Repository: CRAN
Date/Publication: 2015-06-03 19:06:30