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.
| Reverse depends: |
Boruta, metaforest, tuneRanger |
| Reverse imports: |
abcrf, alookr, AmyloGram, banter, BioMM, CornerstoneR, fairadapt, funest, genphen, healthcareai, hpiR, memoria, miceRanger, missRanger, OOBCurve, orf, outForest, poolVIM, PrInCE, quantregRanger, radiant.model, randomForestExplainer, REMP, rfinterval, riskRegression, rmweather, RNAmodR.ML, sambia, SCORPIUS, simPop, solitude, spm, SPOT, StratifiedMedicine, VIM, VSURF |
| Reverse suggests: |
batchtools, breakDown, butcher, cattonum, DALEX, drifter, dynwrap, fastshap, flashlight, forestControl, GSIF, iBreakDown, iml, Infusion, IPMRF, knockoff, lime, MachineShop, MFKnockoffs, mlr, mlr3learners, mlr3proba, mlrCPO, modelDown, modelplotr, nlpred, parsnip, pdp, purge, r2pmml, splitTools, SSLR, SuperLearner, superml, tidypredict, topdownr, tree.interpreter, varImp, vip |