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.
Newer version available at github.com/gbm-developers/gbm3.
| Version: |
2.2.3 |
| Depends: |
R (≥ 2.9.0) |
| Imports: |
lattice, parallel, survival |
| Suggests: |
covr, gridExtra, knitr, pdp, RUnit, splines, tinytest, vip, viridis, MASS |
| Published: |
2026-01-22 |
| DOI: |
10.32614/CRAN.package.gbm |
| Author: |
Greg Ridgeway 
[aut, cre],
Daniel Edwards [ctb],
Brian Kriegler [ctb],
Stefan Schroedl [ctb],
Harry Southworth [ctb],
Brandon Greenwell
[ctb],
Bradley Boehmke
[ctb],
Jay Cunningham [ctb],
GBM Developers [aut] (https://github.com/gbm-developers) |
| Maintainer: |
Greg Ridgeway <gridge at upenn.edu> |
| 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 |
| Reverse depends: |
gbm2sas, LogisticEnsembles, mma, NumericEnsembles |
| Reverse imports: |
aihuman, ARTtransfer, biomod2, bonsaiforest, branchpointer, bst, bujar, CoxAIPW, CRE, crispRdesignR, crossurr, cytofQC, E2E, ecospat, enmSdmX, EpiSemble, evalITR, EZtune, funcml, GB5mcPred, gbm.auto, gbts, i3pack, IPMRF, lilikoi, MEGB, metaEnsembleR, mimar, MLInterfaces, mob, mvGPS, nadir, OpEnHiMR, paths, pomodoro, precmed, PSweight, qeML, ranktreeEnsemble, regressoR, rgnoisefilt, ROOT, RSDA, scorecardModelUtils, SDMtune, spm, spm2, SSDM, tidylearn, traineR, tsensembler, twang, twangContinuous, twangMediation, varPro, visualpred, WaveletGBM, xgrove |
| Reverse suggests: |
AutoMLR, BiodiversityR, caretEnsemble, caretSDM, cdgd, ciu, CMA, cobalt, condvis2, countSTAR, cramR, DALEXtra, DeepLearningCausal, dismo, DrData, dynamicSDM, e2tree, fairmodels, imputeR, insight, MachineShop, MatchIt, mboost, mlr, mpae, npcs, OpEnCAST, opencesp, optweight, pdp, plotmo, pminternal, pmml, posterior, psborrow2, riskRegression, rSAFE, shapr, subsemble, SuperLearner, SuperSurv, survex, treeshap, vivid, WeightIt, writeAlizer |
| Reverse enhances: |
vip |