gbm: Generalized Boosted Regression Models

An implementation of 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: 2.1.3
Depends: R (≥ 2.9.0), survival, lattice, splines, parallel
Suggests: RUnit
Published: 2017-03-21
Author: Greg Ridgeway with contributions from others
Maintainer: ORPHANED
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
NeedsCompilation: yes
In views: MachineLearning, Survival
CRAN checks: gbm results


Reference manual: gbm.pdf
Package source: gbm_2.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: gbm_2.1.3.tgz
OS X Mavericks binaries: r-oldrel: gbm_2.1.3.tgz
Old sources: gbm archive

Reverse dependencies:

Reverse depends: BigTSP, bst, ecospat, gbm2sas, mma, personalized, twang
Reverse imports: aurelius, biomod2, bujar, EnsembleBase, gbts, horserule, imputeR, inTrees, IPMRF, mvtboost, SDMPlay, spm, SSDM, tsensembler
Reverse suggests: AzureML, BiodiversityR, caretEnsemble, crimelinkage, dismo, fscaret, mboost, mlr, ModelMap, opera, pdp, plotmo, pmml, preprosim, subsemble, SuperLearner


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