xgboost: Extreme Gradient Boosting

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.

Version: 1.6.0.1
Depends: R (≥ 3.3.0)
Imports: Matrix (≥ 1.1-0), methods, data.table (≥ 1.9.6), jsonlite (≥ 1.0)
Suggests: knitr, rmarkdown, ggplot2 (≥ 1.0.1), DiagrammeR (≥ 0.9.0), Ckmeans.1d.dp (≥ 3.3.1), vcd (≥ 1.3), testthat, lintr, igraph (≥ 1.0.1), float, crayon, titanic
Published: 2022-04-16
Author: Tianqi Chen [aut], Tong He [aut], Michael Benesty [aut], Vadim Khotilovich [aut], Yuan Tang ORCID iD [aut], Hyunsu Cho [aut], Kailong Chen [aut], Rory Mitchell [aut], Ignacio Cano [aut], Tianyi Zhou [aut], Mu Li [aut], Junyuan Xie [aut], Min Lin [aut], Yifeng Geng [aut], Yutian Li [aut], Jiaming Yuan [aut, cre], XGBoost contributors [cph] (base XGBoost implementation)
Maintainer: Jiaming Yuan <jm.yuan at outlook.com>
BugReports: https://github.com/dmlc/xgboost/issues
License: Apache License (== 2.0) | file LICENSE
URL: https://github.com/dmlc/xgboost
NeedsCompilation: yes
SystemRequirements: GNU make, C++14
In views: HighPerformanceComputing, MachineLearning, ModelDeployment, Survival
CRAN checks: xgboost results

Documentation:

Reference manual: xgboost.pdf
Vignettes: Discover your data
XGBoost presentation
XGBoost from JSON
xgboost: eXtreme Gradient Boosting

Downloads:

Package source: xgboost_1.6.0.1.tar.gz
Windows binaries: r-devel: xgboost_1.6.0.1.zip, r-release: xgboost_1.6.0.1.zip, r-oldrel: xgboost_1.6.0.1.zip
macOS binaries: r-release (arm64): xgboost_1.6.0.1.tgz, r-oldrel (arm64): xgboost_1.6.0.1.tgz, r-release (x86_64): xgboost_1.6.0.1.tgz, r-oldrel (x86_64): xgboost_1.6.0.1.tgz
Old sources: xgboost archive

Reverse dependencies:

Reverse depends: SCATE, twangRDC
Reverse imports: adapt4pv, alookr, audrex, autoBagging, autostats, bambu, BayesSpace, CausalGPS, causalweight, ccmap, creditmodel, dblr, EIX, fastrmodels, fdm2id, GeneralisedCovarianceMeasure, GNET2, GPCERF, healthcareai, infinityFlow, inTrees, irboost, mikropml, mixgb, modeltime, MSclassifR, nfl4th, nflfastR, nsga3, oncrawlR, personalized, predhy, predictoR, PriceIndices, promor, radiant.model, rminer, scDblFinder, scds, SELF, sentiment.ai, SHAPforxgboost, shapviz, surveyvoi, tidybins, traineR, trena, tsensembler, twang, visaOTR, wactor, weightedGCM, xgb2sql, xrf
Reverse suggests: BAGofT, bigsnpr, Boruta, breakDown, butcher, CBDA, cfbfastR, cheem, coefplot, cuda.ml, DALEXtra, DriveML, easyalluvial, embed, familiar, fastshap, FeatureHashing, FLAME, flashlight, forecastML, GenericML, lime, MachineShop, mcboost, metabolomicsR, mistat, mlflow, mlr, mlr3benchmark, mlr3hyperband, mlr3learners, mlr3tuningspaces, mlr3viz, modelplotr, modelStudio, modeltime.ensemble, nlpred, ParBayesianOptimization, parsnip, pdp, PheCAP, Platypus, pmml, r2pmml, rattle, rBayesianOptimization, sense, shapr, sits, SPOTMisc, SuperLearner, superMICE, superml, survex, tidypredict, twangMediation, utiml, vetiver, vimp, vip, workboots

Linking:

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