Other packages > Find by keyword >

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]
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 06 Mar. 2026.
Previous versions and publish date:
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)
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
DatabionicSwarm  
Swarm Intelligence for Self-Organized Clustering
Algorithms implementing populations of agents that interact with one another and sense their environ ...
Download / Learn more Package Citations See dependency  
solitude  
An Implementation of Isolation Forest
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detecti ...
Download / Learn more Package Citations See dependency  
EMVS  
The Expectation-Maximization Approach to Bayesian Variable Selection
An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization p ...
Download / Learn more Package Citations See dependency  
lbfgs  
Limited-memory BFGS Optimization
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implem ...
Download / Learn more Package Citations See dependency  
mlr3viz  
Visualizations for 'mlr3'
Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, le ...
Download / Learn more Package Citations See dependency  

26,264

R Packages

223,360

Dependencies

70,376

Author Associations

26,265

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA