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Thegbm package, which standsforgeneralizedboostedmodels, provides extensions toFreund and Schapire’s AdaBoost algorithm andFriedman’s gradientboosting machine. Itincludes regression methods for least squares, absolute loss,t-distribution loss, quantile regression, logistic, multinomiallogistic, Poisson, Cox proportional hazards partial likelihood, AdaBoostexponential loss, Huberized hinge loss, and Learning to Rank measures(i.e.,LambdaMart).

# The easiest way to get gbm is to it install from CRAN:install.packages("gbm")# Alternatively, you can install the development version from GitHub:if (!requireNamespace("remotes")) {  install.packages("remotes")}remotes::install_github("gbm-developers/gbm")

Thegbm package is retired andno longer under active development. We will only make the necessarychanges to ensure thatgbmremains on CRAN. For the most part, no new features will be added, andonly the most critical of bugs will be fixed.

This is a maintained version ofgbm back compatible to CRANversions ofgbm 2.1.x. Itexists mainly for the purpose of reproducible research and data analysesperformed with the 2.1.x versions ofgbm. For newer development,and a more consistent API, try out thegbm3 package!