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. Newer version available at github.com/gbm-developers/gbm3.

Version: 2.1.9
Depends: R (≥ 2.9.0)
Imports: lattice, parallel, survival
Suggests: covr, gridExtra, knitr, pdp, RUnit, splines, tinytest, vip, viridis
Published: 2024-01-10
DOI: 10.32614/CRAN.package.gbm
Author: Ridgeway Greg ORCID iD [aut, cre], Daniel Edwards ORCID iD [ctb], Brian Kriegler ORCID iD [ctb], Stefan Schroedl ORCID iD [ctb], Harry Southworth ORCID iD [ctb], Brandon Greenwell ORCID iD [ctb], Bradley Boehmke ORCID iD [ctb], Jay Cunningham [ctb], GBM Developers [aut] (https://github.com/gbm-developers)
Maintainer: Ridgeway Greg <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

Documentation:

Reference manual: gbm.pdf
Vignettes: Generalized Boosted Models: A guide to the gbm package

Downloads:

Package source: gbm_2.1.9.tar.gz
Windows binaries: r-devel: gbm_2.1.9.zip, r-release: gbm_2.1.9.zip, r-oldrel: gbm_2.1.9.zip
macOS binaries: r-release (arm64): gbm_2.1.9.tgz, r-oldrel (arm64): gbm_2.1.9.tgz, r-release (x86_64): gbm_2.1.9.tgz, r-oldrel (x86_64): gbm_2.1.9.tgz
Old sources: gbm archive

Reverse dependencies:

Reverse depends: gbm2sas, mma
Reverse imports: autoMrP, biomod2, Bodi, branchpointer, bst, bujar, countSTAR, CoxAIPW, CRE, crispRdesignR, crossurr, cytofQC, ecospat, enmSdmX, EnsembleBase, EpiSemble, evalITR, EZtune, GB5mcPred, gbm.auto, gbts, inTrees, lilikoi, LOGANTree, metaEnsembleR, MiDA, MLInterfaces, mob, mvGPS, OpEnHiMR, paths, pomodoro, precmed, PSweight, qeML, ranktreeEnsemble, rgnoisefilt, RSDA, scorecardModelUtils, SDMtune, spm, spm2, SSDM, statVisual, traineR, tsensembler, twang, twangContinuous, twangMediation, visualpred, WaveletGBM, xgrove
Reverse suggests: BiodiversityR, caretEnsemble, cdgd, ciu, CMA, cobalt, condvis2, corrgrapher, creditmodel, cvwrapr, DALEXtra, dismo, dynamicSDM, fairmodels, featurefinder, flowml, fscaret, imputeR, insight, LDLcalc, MachineShop, MatchIt, mboost, mlr, mpae, nestedcv, npcs, opera, pdp, plotmo, pmml, posterior, psborrow2, riskRegression, rSAFE, shapr, subsemble, SuperLearner, superMICE, survex, treeshap, triplot, WeightIt
Reverse enhances: vip

Linking:

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