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  "Author": "Esteban Alfaro [aut, cre], Matias Gamez [aut], Noelia Garcia\n[aut], L. Guo [ctb], A. Albano [ctb], M. Sciandra [ctb], A.\nPlai [ctb]",
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  "Description": "It implements Freund and Schapire's Adaboost.M1 algorithm\nand Breiman's Bagging algorithm using classification trees as\nindividual classifiers. Once these classifiers have been\ntrained, they can be used to predict on new data. Also, cross\nvalidation estimation of the error can be done. Since version\n2.0 the function margins() is available to calculate the\nmargins for these classifiers. Also a higher flexibility is\nachieved giving access to the rpart.control() argument of\n'rpart'. Four important new features were introduced on version\n3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new\nfunction errorevol() shows the error of the ensembles as a\nfunction of the number of iterations. In addition, the\nensembles can be pruned using the option 'newmfinal' in the\npredict.bagging() and predict.boosting() functions and the\nposterior probability of each class for observations can be\nobtained. Version 3.1 modifies the relative importance measure\nto take into account the gain of the Gini index given by a\nvariable in each tree and the weights of these trees. Version\n4.0 includes the margin-based ordered aggregation for Bagging\npruning (Guo and Boukir, 2013) and a function to auto prune the\n'rpart' tree. Moreover, three new plots are also available\nimportanceplot(), plot.errorevol() and plot.margins(). Version\n4.1 allows to predict on unlabeled data. Version 4.2 includes\nthe parallel computation option for some of the functions.\nVersion 5.0 includes the Boosting and Bagging algorithms for\nlabel ranking (Albano, Sciandra and Plaia, 2023).",
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