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] (https://github.com/gbm-developers)
Maintainer: Brandon Greenwell <greenwell.brandon at gmail.com>
BugReports: https://github.com/gbm-developers/gbm/issues
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: https://github.com/gbm-developers/gbm
NeedsCompilation: yes
Materials: README NEWS
In views: MachineLearning, Survival
CRAN checks: gbm results

Downloads:

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: gbm_2.1.4.zip, r-release: gbm_2.1.4.zip, r-oldrel: gbm_2.1.4.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=gbm to link to this page.