The R Package Ada for Stochastic Boosting
Performs discrete, real, and gentle boost under both exponential and
logistic loss on a given data set.  The package ada provides a straightforward,
well-documented, and broad boosting routine for classification, ideally suited
for small to moderate-sized data sets.

Adaptive Huber Estimation and Regression
c(person("Xiaoou", "Pan", email = "xip024@ucsd.edu", role = c("aut", "cre")),
person("Wen-Xin", "Zhou", email = "wez243@ucsd.edu", role = "aut"))
Huber-type estimation for mean, covariance and (regularized) regression. For all the methods, the robustification parameter tau is chosen by a tuning-free principle.
Xiaoou Pan [aut, cre],
Wen-Xin Zhou [aut]

Esteban Alfaro Cortés, Matías Gámez Martínez y Noelia García Rubio
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.

Esteban Alfaro Cortes, Matias Gamez Martinez and Noelia Garcia Rubio
Thu Oct 25 11:12:42 2007; Esteban.Alfaro

Esteban Alfaro Cortes, Matias Gamez Martinez and Noelia Garcia
Rubio
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. This version 2.0 adds
a new function "margins" to calculate the margins for these
classifiers. Also a higher flexibility is achieved giving
access to the "rpart.control" argument of "rpart".

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.  This version 2.0
adds a new function "margins" to calculate the margins for
these classifiers.  Also a higher flexibility is achieved
giving access to the "rpart.control" argument of "rpart".