Package: adabag
Type: Package
Version: 5.1
Date: 2025-07-28
Title: Applies Multiclass AdaBoost.M1, SAMME and Bagging
Authors@R: c(person(given = "Esteban", family = "Alfaro", role = c("aut","cre"), email = "Esteban.Alfaro@uclm.es"),
                 person(given = "Matias", family = "Gamez", role = "aut"),
		person(given = "Noelia", family = "Garcia", role = "aut"),
		person(given = "L.", family = "Guo", role = "ctb"),
		person(given = "A.", family = "Albano", role = "ctb"),
		person(given = "M.", family = "Sciandra", role = "ctb"),
		person(given = "A.", family = "Plai", role = "ctb"))
Author: Esteban Alfaro [aut, cre],
  Matias Gamez [aut],
  Noelia Garcia [aut],
  L. Guo [ctb],
  A. Albano [ctb],
  M. Sciandra [ctb],
  A. Plai [ctb]
Maintainer: Esteban Alfaro <Esteban.Alfaro@uclm.es>
Depends: rpart, caret, foreach, doParallel, R (>= 4.0.0)
Imports: methods, tidyr, dplyr, ConsRank (>= 2.1.3)
Suggests: mlbench
Description: 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). 
License: GPL (>= 2)
Encoding: UTF-8
LazyLoad: yes
LazyData: true
NeedsCompilation: no
Packaged: 2025-07-28 09:48:08 UTC; Esteban.Alfaro
Repository: CRAN
Date/Publication: 2025-07-28 10:40:02 UTC
Built: R 4.6.0; ; 2026-04-30 18:30:05 UTC; unix
