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, AmyloGram, banter, CaseBasedReasoning, CornerstoneR, healthcareai, missRanger, MlBayesOpt, mopa, OOBCurve, poolVIM, quantregRanger, riskRegression, rmweather, sambia, SCORPIUS, simPop, solitude, spm, tsensembler, VIM |
| Reverse suggests: |
batchtools, breakDown, bWGR, cattonum, climbeR, edarf, forestControl, GSIF, iBreakDown, iml, IPMRF, knockoff, lime, MachineShop, MFKnockoffs, mlr, mlrCPO, parsnip, pdp, purge, r2pmml, SuperLearner, superml, tidypredict, vip |