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.
Downloads:
Reverse dependencies:
| Reverse depends: |
Boruta, metaforest, tuneRanger |
| Reverse imports: |
abcrf, AmyloGram, CaseBasedReasoning, healthcareai, missRanger, MlBayesOpt, mopa, OOBCurve, quantregRanger, rmweather, sambia, SCORPIUS, Seurat, simPop, spm, tsensembler |
| Reverse suggests: |
batchtools, breakDown, bWGR, cattonum, climbeR, edarf, forestControl, GSIF, IPMRF, knockoff, MFKnockoffs, mlr, mlrCPO, pdp, purge, stranger, SuperLearner, tidypredict, vip |
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