MLearn                  revised MLearn interface for machine learning
RAB                     real adaboost (Friedman et al)
balKfold.xvspec         generate a partition function for
                        cross-validation, where the partitions are
                        approximately balanced with respect to the
                        distribution of a response variable
classifierOutput-class
                        Class "classifierOutput"
clusteringOutput-class
                        container for clustering outputs in uniform
                        structure
confuMat-methods        Compute the confusion matrix for a classifier.
fs.absT                 support for feature selection in
                        cross-validation
fsHistory               extract history of feature selection for a
                        cross-validated machine learner
getGrid                 MLInterfaces infrastructure
learnerSchema-class     Class "learnerSchema" -- convey information on
                        a machine learning function to the MLearn
                        wrapper
planarPlot-methods      Methods for Function planarPlot in Package
                        'MLInterfaces'
raboostCont-class       Class "raboostCont" ~~~
varImpStruct-class      Class "varImpStruct" -- collect data on
                        variable importance from various machine
                        learning methods
xvalLoop                Cross-validation in clustered computing
                        environments
xvalSpec                container for information specifying a
                        cross-validated machine learning exercise
