Package: CORElearn
Title: CORElearn - classification, regression, feature evaluation and
ordinal evaluation
Version: 0.9.35
Date: 2011-08-04
Author: Marko Robnik-Sikonja <marko.robnik@fri.uni-lj.si>, Petr Savicky
<savicky@cs.cas.cz>
Maintainer: Marko Robnik-Sikonja <marko.robnik@fri.uni-lj.si>
Description: CORElearn is machine learning suite ported to R from
standalone C++ package. It contains several model learning
techniques in classification and regression, for example
classification and regression trees with optional constructive
induction and models in the leafs, random forests, kNN, naive
Bayes, and locally weighted regression. It is especially strong
in feature evaluation algorithms 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 ordinal features and class. Several
algorithms support parallel multithreaded execution via OpenMP.
Windows binary versions supporting multithreading are available
on package website, as CRAN uses different toolchain. The top
level documentation is reachable through ?CORElearn.
License: GPL-3
URL: http://lkm.fri.uni-lj.si/rmarko/software/
Depends: cluster, rpart, stats
Suggests: lattice
Packaged: 2011-08-19 06:11:01 UTC; rmarko
Repository: CRAN
Date/Publication: 2011-08-19 07:02:03

Description: CORElearn is machine learning suite ported to R from standalone C++ package.
It contains several model learning techniques in classification and regression,
for example classification and regression trees with optional constructive induction and models in the leafs,
random forests, kNN, naive Bayes, and locally weighted regression.
It is especially strong in feature evaluation algorithms 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 ordinal features and class.
Several algorithms support parallel multithreaded execution via OpenMP. Windows binary versions
supporting multithreading are available on package website, as CRAN uses different toolchain.