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: 0.15.1
Depends: R (≥ 3.1)
Imports: Rcpp (≥ 0.11.2), Matrix
LinkingTo: Rcpp, RcppEigen
Suggests: survival, testthat
Published: 2023-04-03
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://github.com/imbs-hl/ranger
NeedsCompilation: yes
Citation: ranger citation info
Materials: NEWS
In views: MachineLearning, Survival
CRAN checks: ranger results


Reference manual: ranger.pdf


Package source: ranger_0.15.1.tar.gz
Windows binaries: r-devel: ranger_0.15.1.zip, r-release: ranger_0.15.1.zip, r-oldrel: ranger_0.15.1.zip
macOS binaries: r-release (arm64): ranger_0.15.1.tgz, r-oldrel (arm64): ranger_0.15.1.tgz, r-release (x86_64): ranger_0.15.1.tgz, r-oldrel (x86_64): ranger_0.15.1.tgz
Old sources: ranger archive

Reverse dependencies:

Reverse depends: causalweight, Iscores, metaforest, OptHoldoutSize, PKLMtest, RfEmpImp, SPARRAfairness, SpatialML, tuneRanger
Reverse imports: abcrf, ADAPTS, alookr, AmpGram, AmyloGram, arf, BioMM, Bodi, Boruta, C443, CancerGram, CaseBasedReasoning, ClassifyR, CornerstoneR, CoxAIPW, crossurr, ddml, discSurv, drpop, enmSdmX, gapclosing, geomod, GRSxE, hpiR, htmldf, hypoRF, influential, Infusion, MDEI, memoria, miceRanger, missRanger, mistyR, MLDataR, MLFS, mlmts, mlr3shiny, MSiP, multiclassPairs, ocf, OOBCurve, orf, OSTE, outForest, phenomis, poolVIM, PrInCE, quantregRanger, radiant.model, randomForestExplainer, RaSEn, RCAS, REMP, rfinterval, RFpredInterval, rfVarImpOOB, rfvimptest, riskRegression, rmweather, RNAmodR.ML, sambia, SCORPIUS, seqimpute, simPop, SISIR, solitude, spatialRF, spFSR, spm, SPOT, stablelearner, Statial, StratifiedMedicine, subscreen, synthpop, TangledFeatures, tbma, text2sdg, tramicp, TSCI, tsensembler, VIM, worcs
Reverse suggests: arenar, batchtools, biotmle, breakDown, butcher, CALIBERrfimpute, CausalGPS, cdgd, concrete, corrgrapher, cpi, DALEX, DALEXtra, decoupleR, dlookr, DoubleML, drifter, dynwrap, fairmodels, familiar, fastshap, finetune, fmeffects, forestControl, GenericML, HPiP, HPLB, iBreakDown, iml, ingredients, knockoff, lime, lmtp, MachineShop, MACP, mcboost, micd, mice, microbiomeMarker, miesmuschel, mllrnrs, mlr, mlr3fairness, mlr3learners, mlr3mbo, mlr3spatial, mlr3tuningspaces, mlr3viz, mlrCPO, mlrintermbo, mlsurvlrnrs, modelDown, modelStudio, nestedcv, nlpred, parsnip, pdp, PieGlyph, piRF, polle, purge, r2pmml, sense, shapr, sirus, soilassessment, sperrorest, spmodel, SSLR, stacks, SuperLearner, superMICE, superml, survex, text, tidypredict, topdownr, tree.interpreter, treemisc, triplot, txshift, varImp, vetiver, vimp, vivid, VSURF
Reverse enhances: vip


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