Package: gbm3 3.0.1

Greg Ridgeway

gbm3: Generalized Boosted Regression Models

Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. Schapire (1997) <doi:10.1006/jcss.1997.1504> and Friedman's gradient boosting machine, J.H. Friedman (2001) <doi:10.1214/aos/1013203451>. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMART).

Authors:James Hickey [aut], Paul Metcalfe [aut], Greg Ridgeway [aut, cre], Stefan Schroedl [aut], Harry Southworth [aut], Terry Therneau [aut]

gbm3_3.0.1.tar.gz
gbm3_3.0.1.zip(r-4.7)gbm3_3.0.1.zip(r-4.6)gbm3_3.0.1.zip(r-4.5)
gbm3_3.0.1.tgz(r-4.6-x86_64)gbm3_3.0.1.tgz(r-4.6-arm64)gbm3_3.0.1.tgz(r-4.5-x86_64)gbm3_3.0.1.tgz(r-4.5-arm64)
gbm3_3.0.1.tar.gz(r-4.7-arm64)gbm3_3.0.1.tar.gz(r-4.7-x86_64)gbm3_3.0.1.tar.gz(r-4.6-arm64)gbm3_3.0.1.tar.gz(r-4.6-x86_64)
gbm3_3.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gbm3/json (API)

# Install 'gbm3' in R:
install.packages('gbm3', repos = c('https://gbm-developers.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gbm-developers/gbm3/issues

Pkgdown/docs site:https://gbm-developers.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

cppopenmp

8.90 score 150 stars 66 scripts 577 downloads 24 exports 4 dependencies

Last updated from:806c2a067b. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK267
linux-devel-x86_64OK307
source / vignettesOK358
linux-release-arm64OK274
linux-release-x86_64OK299
macos-release-arm64OK260
macos-release-x86_64OK848
macos-oldrel-arm64OK172
macos-oldrel-x86_64OK545
windows-develOK309
windows-releaseOK336
windows-oldrelOK316
wasm-releaseOK186

Exports:available_distributionsbaseline_hazardcalibrate_plotdistribution_namegbmgbm_distgbm_moregbm_roc_areagbm.fitgbm.perfgbmParallelgbmtgbmt_fitgbmt_performanceinteractiteration_errorperf_pairwisepermutation_relative_influencepretty_gbm_treequantile_rugrelative_influenceto_old_gbmtraining_paramstrees

Dependencies:latticeMatrixRcppsurvival

Getting started with the gbm package
Example dataset | Creating a gbm distribution object | Setting training parameters | Additional variable and fitting parameters | Cross Validation | Parallelisation | Putting it all together | Default behaviour | Identifying the optimal iteration | Fitting additional trees | Predictions on other data | Relative influence of predictors | Plotting the marginal effects of selected variables | Additional useful functions
Rendered fromgetting-started-with-gbm.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2016-08-11

Guide to the Cox Proportional Hazards model
Set-up of data and distribution object | Creating a boosted model | Strata Updates | Role of additional parameters in GBMDist | ties and prior_node_coeff_var | Description of the underlying algorithm - specifically for CoxPH
Rendered fromcox-proportional-hazards-guide.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2016-08-11

Model Specific Parameters
Distributions with model specific parameters | Cox proportional hazards model | Pairwise distribution | Quantile | TDist | Tweedie
Rendered frommodel-specific-parameters.Rmdusingknitr::rmarkdown

Last update: 2026-05-08
Started: 2016-08-11

Generalized Boosted Models: A guide to the gbm package
High-level description of stochastic gradient boosting | Gradient boosting in more detail | Friedman's gradient boosting machine | Improving boosting methods using control of the learning rate, sub-sampling, and a decomposition for interpretation | Decreasing the learning rate | Variance reduction using subsampling | ANOVA decomposition | Relative influence | Common user options | Loss function | The relationship between shrinkage and number of iterations | Estimating the optimal number of iterations
Rendered fromgbm.Rmdusingknitr::rmarkdown

Last update: 2024-01-14
Started: 2024-01-13