fastAdaboost: a Fast Implementation of Adaboost

Implements Adaboost based on C++ backend code. This is blazingly fast and especially useful for large, in memory data sets. The package uses decision trees as weak classifiers. Once the classifiers have been trained, they can be used to predict new data. Currently, we support only binary classification tasks. The package implements the Adaboost.M1 algorithm and the real Adaboost(SAMME.R) algorithm.

Version: 1.0.0
Depends: R (≥ 3.1.2)
Imports: Rcpp, rpart
LinkingTo: Rcpp (≥ 0.12.0)
Suggests: testthat, knitr, MASS
Published: 2016-02-28
Author: Sourav Chatterjee [aut, cre]
Maintainer: Sourav Chatterjee <souravc83 at gmail.com>
BugReports: https://github.com/souravc83/fastAdaboost/issues
License: MIT + file LICENSE
URL: https://github.com/souravc83/fastAdaboost
NeedsCompilation: yes
Materials: README
CRAN checks: fastAdaboost results

Downloads:

Reference manual: fastAdaboost.pdf
Package source: fastAdaboost_1.0.0.tar.gz
Windows binaries: r-devel: fastAdaboost_1.0.0.zip, r-release: fastAdaboost_1.0.0.zip, r-oldrel: fastAdaboost_1.0.0.zip
OS X Mavericks binaries: r-release: fastAdaboost_1.0.0.tgz, r-oldrel: fastAdaboost_1.0.0.tgz

Linking:

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