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xgboost
Extreme Gradient Boosting
View on CRAN: Click
here
Download and install xgboost package within the R console
Install from CRAN:
install.packages("xgboost")
Install from Github:
library("remotes")
install_github("cran/xgboost") Install by package version:
library("remotes")
install_version("xgboost", "3.1.3.1") Attach the package and use:
library("xgboost")
Maintained by
Jiaming Yuan
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-09-01
Latest Update: 2025-05-15
Description:
Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.
How to cite:
Jiaming Yuan (2014). xgboost: Extreme Gradient Boosting. R package version 3.1.3.1, https://cran.r-project.org/web/packages/xgboost. Accessed 15 Sep. 2026.
Previous versions and publish date:
(2026-07-09 06:56), 0.3-0 (2014-09-01 19:47), 0.3-1 (2014-09-07 09:26), 0.3-2 (2014-09-07 21:54), 0.3-3 (2015-03-03 11:05), 0.4-1 (2015-08-01 01:24), 0.4-2 (2015-08-02 08:23), 0.4-3 (2016-02-15 14:41), 0.4-4 (2016-07-12 10:55), 0.6-0 (2016-12-16 09:25), 0.6-2 (2016-12-18 11:23), 0.6-3 (2016-12-31 22:01), 0.6-4 (2017-01-05 10:40), 0.6.4.1 (2018-01-23 22:57), 0.71.1 (2018-05-16 07:18), 0.71.2 (2018-06-09 06:24), 0.81.0.1 (2019-01-31 10:10), 0.82.1 (2019-03-12 00:40), 0.90.0.1 (2019-07-25 22:40), 0.90.0.2 (2019-08-01 21:20), 1.0.0.1 (2020-03-23 09:00), 1.0.0.2 (2020-03-25 15:10), 1.1.1.1 (2020-06-14 16:40), 1.2.0.1 (2020-09-02 07:40), 1.3.1.1 (2021-01-05 21:00), 1.3.2.1 (2021-01-18 11:10), 1.4.1.1 (2021-04-22 11:20), 1.5.0.1 (2021-11-08 09:00), 1.5.0.2 (2021-11-21 17:30), 1.5.2.1 (2022-02-21 11:00), 1.6.0.1 (2022-04-16 17:50), 1.7.3.1 (2023-01-14 22:20), 1.7.5.1 (2023-03-30 21:40), 1.7.6.1 (2023-12-06 09:50), 1.7.7.1 (2024-01-25 14:10), 1.7.8.1 (2024-07-24 20:40), 1.7.9.1 (2025-03-26 15:30), 1.7.10.1 (2025-04-22 13:10), 1.7.11.1 (2025-05-15 09:10), 3.1.2.1 (2025-12-03 11:00), 3.1.3.1 (2026-01-12 09:10), 3.2.0.1 (2026-02-10 15:00)
Other packages that cited xgboost R package
View xgboost citation profile
Other R packages that xgboost depends,
imports, suggests or enhances
Complete documentation for xgboost
Functions, R codes and Examples using
the xgboost R package
Some associated functions: a-compatibility-note-for-saveRDS-save . agaricus.test . agaricus.train . callbacks . cb.cv.predict . cb.early.stop . cb.evaluation.log . cb.gblinear.history . cb.print.evaluation . cb.reset.parameters . cb.save.model . dim.xgb.DMatrix . dimnames.xgb.DMatrix . getinfo . normalize . predict.xgb.Booster . prepare.ggplot.shap.data . print.xgb.Booster . print.xgb.DMatrix . print.xgb.cv . setinfo . slice.xgb.DMatrix . xgb.Booster.complete . xgb.DMatrix . xgb.DMatrix.save . xgb.attr . xgb.config . xgb.create.features . xgb.cv . xgb.dump . xgb.gblinear.history . xgb.importance . xgb.load . xgb.load.raw . xgb.model.dt.tree . xgb.parameters . xgb.plot.deepness . xgb.plot.importance . xgb.plot.multi.trees . xgb.plot.shap . xgb.plot.shap.summary . xgb.plot.tree . xgb.save . xgb.save.raw . xgb.serialize . xgb.shap.data . xgb.train . xgb.unserialize . xgbConfig . xgboost-deprecated .
Some associated R codes: callbacks.R . utils.R . xgb.Booster.R . xgb.DMatrix.R . xgb.DMatrix.save.R . xgb.config.R . xgb.create.features.R . xgb.cv.R . xgb.dump.R . xgb.ggplot.R . xgb.importance.R . xgb.load.R . xgb.load.raw.R . xgb.model.dt.tree.R . xgb.plot.deepness.R . xgb.plot.importance.R . xgb.plot.multi.trees.R . xgb.plot.shap.R . xgb.plot.tree.R . xgb.save.R . xgb.save.raw.R . xgb.serialize.R . xgb.train.R . xgb.unserialize.R . xgboost.R . Full xgboost package functions and examples
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