Ensemble_ranking_IW     Ensemble methods for ranking data:
                        Item-Weighted Boosting and Bagging Algorithms
MarginOrderedPruning.Bagging
                        MarginOrderedPruning.Bagging
adabag-package          Applies Multiclass AdaBoost.M1, SAMME and
                        Bagging
autoprune               Builds automatically a pruned tree of class
                        'rpart'
bagging                 Applies the Bagging algorithm to a data set
bagging.cv              Runs v-fold cross validation with Bagging
boosting                Applies the AdaBoost.M1 and SAMME algorithms to
                        a data set
boosting.cv             Runs v-fold cross validation with AdaBoost.M1
                        or SAMME
errorevol               Shows the error evolution of the ensemble
errorevol_ranking_vector_IW
                        Calculate the error evolution and final
                        predictions of an item-weighted ensemble for
                        rankings
importanceplot          Plots the variables relative importance
margins                 Calculates the margins
plot.errorevol          Plots the error evolution of the ensemble
plot.margins            Plots the margins of the ensemble
predict.bagging         Predicts from a fitted bagging object
predict.boosting        Predicts from a fitted boosting object
prep_data               Prepare Ranking Data for Item-Weighted Ensemble
                        Algorithm
simulatedRankingData    Simulated ranking data
