It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging
algorithm using classification trees as individual classifiers. Once these classifiers have been
trained, they can be used to predict on new data. Also, cross validation estimation of the error can
be done. Since version 2.0 the function margins() is available to calculate the margins for these
classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument
of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu
et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as
a function of the number of iterations. In addition, the ensembles can be pruned using the option
'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of
each class for observations can be obtained. Version 3.1 modifies the relative importance measure
to take into account the gain of the Gini index given by a variable in each tree and the weights of
these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo
and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also
available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on
unlabeled data. Version 4.2 includes the parallel computation option for some of the functions.
Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandra
and Plaia, 2023).