Alfaro, Esteban; Gamez, Matias and Garcia, Noelia
This package 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 predictions 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.

Applies Multiclass AdaBoost.M1, SAMME and Bagging
Alfaro, Esteban; Gamez, Matias and Garcia, Noelia; with contributions from Li Guo
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.