CRAN/E | gbm

gbm

Generalized Boosted Regression Models

Installation

About

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.

github.com/gbm-developers/gbm
Bug report File report

Key Metrics

Version 2.1.9
R ≥ 2.9.0
Published 2024-01-10 96 days ago
Needs compilation? yes
License GPL-2
License GPL-3
License File
CRAN checks gbm results

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Maintainer

Maintainer

Ridgeway Greg

gridge@upenn.edu

Authors

Ridgeway Greg

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)

Material

README
NEWS
Reference manual
Package source

In Views

MachineLearning
Survival

Vignettes

Generalized Boosted Models: A guide to the gbm package

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

gbm archive

Depends

R ≥ 2.9.0

Imports

lattice
parallel
survival

Suggests

covr
gridExtra
knitr
pdp
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tinytest
vip
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Reverse Depends

gbm2sas
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Reverse Imports

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RSDA
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SDMtune
spm
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SSDM
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twang
twangContinuous
twangMediation
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(warning: might lead to performance issues and take some time)

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Reverse Enhances

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