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). Originally developed by Greg Ridgeway.

Version: 2.1.4
Depends: R (≥ 2.9.0)
Imports: gridExtra, lattice, parallel, survival
Suggests: knitr, pdp, RUnit, splines, viridis
Published: 2018-09-16
Author: Brandon Greenwell ORCID iD [aut, cre], Bradley Boehmke ORCID iD [aut], Jay Cunningham [aut], GBM Developers [aut] (
Maintainer: Brandon Greenwell <greenwell.brandon at>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
NeedsCompilation: yes
Materials: README NEWS
In views: MachineLearning, Survival
CRAN checks: gbm results


Reference manual: gbm.pdf
Vignettes: Generalized Boosted Models: A guide to the gbm package
Package source: gbm_2.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: gbm_2.1.3.tgz, 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, inTrees, IPMRF, lilikoi, mvtboost, Plasmode, scorecardModelUtils, SDMPlay, spm, SSDM, tsensembler
Reverse suggests: AzureML, BiodiversityR, caretEnsemble, crimelinkage, DALEX, dismo, featurefinder, fscaret, mboost, mlr, opera, pdp, plotmo, pmml, preprosim, subsemble, SuperLearner, vip, WeightIt


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