given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.

Given a collection of intervals with integer start and end
positions, find recurrently targeted regions and estimate the significance
of finding. Randomization is implemented by parallel methods, either
using local host machines, or submitting grid engine jobs.

CORElearn - classification, regression, feature evaluation and
ordinal evaluation
Marko Robnik-Sikonja <marko.robnik@fri.uni-lj.si>, Petr Savicky
<savicky@cs.cas.cz>
CORElearn is machine learning suite ported to R from
standalone C++ package. It contains several model learning
techniques in classification and regression, for example
decision 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. The top
level documentation is reachable through ?CORElearn.

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. The top
level documentation is reachable through ?CORElearn.