ranger: A Fast Implementation of Random Forests

A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.

Version:	0.18.0
Depends:	R (≥ 3.1)
Imports:	Rcpp (≥ 0.11.2), Matrix
LinkingTo:	Rcpp, RcppEigen
Suggests:	survival, testthat
Published:	2026-01-16
DOI:	10.32614/CRAN.package.ranger
Author:	Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb]
Maintainer:	Marvin N. Wright <cran at wrig.de>
BugReports:	https://github.com/imbs-hl/ranger/issues
License:	GPL-3
URL:	https://imbs-hl.github.io/ranger/, https://github.com/imbs-hl/ranger
NeedsCompilation:	yes
Citation:	ranger citation info
Materials:	NEWS
In views:	MachineLearning, Survival
CRAN checks:	ranger results

Documentation:

Reference manual:

ranger.html , ranger.pdf

Downloads:

Package source:	ranger_0.18.0.tar.gz
Windows binaries:	r-devel: ranger_0.18.0.zip, r-release: ranger_0.18.0.zip, r-oldrel: ranger_0.18.0.zip
macOS binaries:	r-release (arm64): ranger_0.18.0.tgz, r-oldrel (arm64): ranger_0.18.0.tgz, r-release (x86_64): ranger_0.18.0.tgz, r-oldrel (x86_64): ranger_0.18.0.tgz
Old sources:	ranger archive

Reverse dependencies:

Reverse depends:	causalweight, ClassificationEnsembles, Iscores, LogisticEnsembles, multimedia, OptHoldoutSize, PKLMtest, RfEmpImp, SPARRAfairness, tuneRanger
Reverse imports:	abcrf, ADAPTS, alookr, AmpGram, AmyloGram, AnimalSequences, arf, AWAggregator, Boruta, C443, CancerGram, CaseBasedReasoning, CCI, CGMissingDataR, ClassifyR, collinear, comets, CompositionalML, CoxAIPW, crossurr, ddecompose, ddml, discSurv, drpop, EFAfactors, enmSdmX, fairadapt, flevr, fluxfixer, fuzzylink, gapclosing, geomod, GRSxE, handwriterRF, hedgedrf, hpiR, hypoRF, influential, Infusion, ipd, IVDML, memoria, metaforest, meteo, miceRanger, missForest, missForestPredict, missRanger, mistyR, MLDataR, MLFS, MRTAnalysis, MSiP, multiclassPairs, MUVR2, nadir, ocf, OOBCurve, optRF, orf, outForest, phenomis, PND.heter.cluster, poolVIM, PrInCE, quantregRanger, quickSentiment, radiant.model, randomForestExplainer, RaSEn, RCAS, REMP, RFAE, rfinterval, RFlocalfdr, RFmstate, rfVarImpOOB, rfvimptest, riskRegression, rjaf, rmweather, RNAmodR.ML, RobustPrediction, roseRF, RPIV, sae.projection, sambia, scDiagnostics, scHiCcompare, SCORPIUS, SEMdeep, seqimpute, shadowVIMP, simPop, SISIR, SLOS, solitude, spatialRF, spCF, spFSR, spm, stablelearner, Statial, StratifiedMedicine, subscreen, synthpop, tall, TangledFeatures, text2emotion, text2sdg, tramicp, TSCI, tsensembler, ubair, utsf, vaccine, VIM, VIMPS, worcs
Reverse suggests:	arenar, autostats, batchtools, bioLeak, biotmle, bnns, breakDown, butcher, CALIBERrfimpute, CausalGPS, cdgd, CimpleG, confcons, cpi, DALEX, DALEXtra, decoupleR, DeepLearningCausal, DirectEffects, dlookr, DoubleML, drifter, dynwrap, e2tree, ENMTools, ensModelVis, explainer, FactorHet, fairmodels, familiar, fastml, fastshap, filtro, finetune, fmeffects, forestControl, fuseMLR, GenericML, heaping, HPiP, HPLB, iBreakDown, iml, important, ingredients, innsight, knockoff, ldmppr, lime, lmtp, MachineShop, MantaID, mcboost, micd, mice, miesmuschel, mllrnrs, mlr, mlr3fairness, mlr3learners, mlr3mbo, mlr3pipelines, mlr3shiny, mlr3spatial, mlr3summary, mlr3superlearner, mlr3tuningspaces, mlr3viz, mlrCPO, mlrintermbo, mlsurvlrnrs, MLwrap, modelDown, modelStudio, MSclassifR, multiDEGGs, nestedcv, OpEnCAST, orbital, parsnip, pathMED, pdp, PieGlyph, pminternal, polle, PortfolioTesteR, postcard, purge, qeML, r2pmml, RobinCar, SAiVE, SDModels, sense, shapr, sperrorest, spmodel, stacks, SuperLearner, survex, targeted, tdarec, text, tidyAML, tidypredict, tidysdm, tidysynthesis, topdownr, tram, transportr, tree.interpreter, txshift, varImp, vetiver, vimp, vivid, VSURF, xplainfi
Reverse enhances:	vip

Linking:

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