gbm: Generalized Boosted Regression Models

This package implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

Version: 2.1
Depends: R (≥ 2.9.0), survival, lattice, splines, parallel
Suggests: RUnit
Published: 2013-05-10
Author: Greg Ridgeway with contributions from others
Maintainer: Harry Southworth <harry.southworth at gmail.com>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: http://code.google.com/p/gradientboostedmodels/
NeedsCompilation: yes
In views: MachineLearning, Survival
CRAN checks: gbm results

Downloads:

Package source: gbm_2.1.tar.gz
MacOS X binary: gbm_2.1.tgz
Windows binary: gbm_2.1.zip
Reference manual: gbm.pdf
Old sources: gbm archive

Reverse dependencies:

Reverse depends: BigTSP, biomod2, bst, imputation, ModelMap, mseq, twang
Reverse suggests: BiodiversityR, caret, dismo, mboost, SuperLearner