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:	0.17.0
Depends:	R (≥ 3.1)
Imports:	Rcpp (≥ 0.11.2), Matrix
LinkingTo:	Rcpp, RcppEigen
Suggests:	survival, testthat
Published:	2024-11-08
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.pdf

Downloads:

Package source:	ranger_0.17.0.tar.gz
Windows binaries:	r-devel: ranger_0.17.0.zip, r-release: ranger_0.17.0.zip, r-oldrel: ranger_0.17.0.zip
macOS binaries:	r-release (arm64): ranger_0.17.0.tgz, r-oldrel (arm64): ranger_0.17.0.tgz, r-release (x86_64): ranger_0.17.0.tgz, r-oldrel (x86_64): ranger_0.17.0.tgz
Old sources:	ranger archive

Reverse dependencies:

Reverse depends:	causalweight, Iscores, metaforest, multimedia, OptHoldoutSize, optRF, PKLMtest, RfEmpImp, SPARRAfairness, SpatialML, tuneRanger
Reverse imports:	abcrf, ADAPTS, alookr, AmpGram, AmyloGram, AnimalSequences, arf, Bodi, Boruta, C443, CancerGram, CaseBasedReasoning, ClassifyR, collinear, comets, CompositionalML, CornerstoneR, CoxAIPW, crossurr, ddecompose, ddml, discSurv, drpop, dsld, EFAfactors, enmSdmX, fairadapt, flevr, gapclosing, geomod, GRSxE, handwriterRF, hedgedrf, hpiR, htmldf, hypoRF, imanr, influential, Infusion, ipd, ldmppr, MDEI, memoria, meteo, miceRanger, missForestPredict, missRanger, mistyR, MLDataR, MLFS, mlmts, MSiP, multiclassPairs, MUVR2, ocf, OOBCurve, orf, OSTE, outForest, outqrf, phenomis, poolVIM, PrInCE, quantregRanger, radiant.model, randomForestExplainer, RaSEn, RCAS, REMP, rfinterval, RFlocalfdr, RFpredInterval, rfVarImpOOB, rfvimptest, riskRegression, rjaf, rmweather, RNAmodR.ML, RobustPrediction, roseRF, sae.projection, sambia, scDiagnostics, SCORPIUS, SEMdeep, seqimpute, simPop, SISIR, solitude, spatialRF, spFSR, spm, stablelearner, Statial, StratifiedMedicine, subscreen, synthpop, TangledFeatures, text2sdg, tramicp, TSCI, tsensembler, utsf, vaccine, VIM, VIMPS, worcs
Reverse suggests:	arenar, autostats, batchtools, biotmle, breakDown, butcher, CALIBERrfimpute, CausalGPS, cdgd, confcons, corrgrapher, cpi, DALEX, DALEXtra, decoupleR, DirectEffects, dlookr, DoubleML, drifter, dynwrap, ENMTools, explainer, fairmodels, familiar, fastml, fastshap, finetune, flowml, fmeffects, forestControl, fuseMLR, GenericML, HPiP, HPLB, ibawds, iBreakDown, iml, ingredients, innsight, knockoff, lime, lmtp, MachineShop, MantaID, mcboost, micd, mice, miesmuschel, mllrnrs, mlr, mlr3fairness, mlr3learners, mlr3mbo, mlr3pipelines, mlr3shiny, mlr3spatial, mlr3summary, mlr3superlearner, mlr3tuningspaces, mlr3viz, mlrCPO, mlrintermbo, mlsurvlrnrs, modelDown, modelStudio, nestedcv, nlpred, parsnip, pdp, PieGlyph, polle, purge, qeML, r2pmml, RobinCar, SAiVE, sense, shapr, sirus, sperrorest, spmodel, SSLR, stacks, SuperLearner, superMICE, superml, survex, text, tidyAML, tidypredict, tidysdm, topdownr, tree.interpreter, treeshap, triplot, txshift, varImp, vetiver, vimp, viraldomain, viralmodels, vivid, VSURF
Reverse enhances:	vip

Linking:

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