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') can be directly analyzed.


  • Set write.forest=TRUE by default
  • Add num.trees option to predict()
  • Faster version of getTerminalNodeIDs(), included in predict()
  • Handle new factor levels in 'order' mode
  • Use unadjusted p-value for 2 categories in maxstat splitting
  • Bug fixes
  • Add Windows multithreading support for new toolchain
  • Add splitting by maximally selected rank statistics for survival and regression forests
  • Faster method for unordered factor splitting
  • Add p-values for variable importance
  • Runtime improvement for regression forests on classification data
  • Bug fixes
  • Reduce memory usage of savest forest objects (changed child.nodeIDs interface)
  • Add keep.inbag option to track in-bag counts
  • Add option sample.fraction for fraction of sampled observations
  • Add tree-wise split.select.weights
  • Add predict.all option in predict() to get individual predictions for each tree for classification and regression
  • Add case-specific random forests
  • Add case weights (weighted bootstrapping or subsampling)
  • Remove tuning functions, please use mlr or caret
  • Catch error of outdated gcc not supporting C++11 completely
  • Bug fixes
  • Allow the user to interrupt computation from R
  • Transpose classification.table and rename to confusion.matrix
  • Respect R seed for prediction
  • Memory improvements for variable importance computation
  • Fix bug: Probability prediction for single observations
  • Fix bug: Results not identical when using alternative interface
  • Small fixes for Solaris compiler
  • Add C-index splitting
  • Fix NA SNP handling
  • Fix matrix and gwaa alternative survival interface
  • Version submitted to JSS
  • Small changes in documentation
  • Preallocate memory for splitting
  • Remove recursive splitting
  • Allow matrix as input data in R version
  • Fix prediction of classification forests in R
  • Speedup growing for continuous covariates
  • Add memory save option to save memory for very large datasets (but slower)
  • Remove memory mode option from R version since no performance gain
  • Fix problems when using Rcpp <0.11.4
  • Add option to split on unordered categorical covariates
  • Optimize memory management for very large survival forests
  • Set required Rcpp version to 0.11.2
  • Fix large $call objects when using BatchJobs
  • Add details and example on GenABEL usage to documentation
  • Minor changes to documentation
  • Speedup for survival forests with continuous covariates
  • R version: Generate seed from R. It is no longer necessary to set the seed argument in ranger calls.
  • Windows support for R version (without multithreading)
  • Speedup growing of regression and probability prediction forests
  • Prediction forests are now handled like regression forests: MSE used for prediction error and permutation importance
  • Fixed name conflict with randomForest package for "importance"
  • Fixed a bug: prediction function is now working for probability prediction forests
  • Slot "predictions" for probability forests now contains class probabilities
  • importance function is now working even if randomForest package is loaded after ranger
  • Fixed a bug: Split selection weights are now working as expected
  • Small changes in documentation

Reference manual

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0.8.0 by Marvin N. Wright, 2 months ago


Report a bug at https://github.com/imbs-hl/ranger/issues

Browse source code at https://github.com/cran/ranger

Authors: Marvin N. Wright

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning, Survival Analysis

GPL-3 license

Imports Rcpp, Matrix

Suggests survival, testthat, GenABEL

Linking to Rcpp, RcppEigen

Imported by AmyloGram, OOBCurve, Seurat, abcrf, healthcareai, iqspr, mopa, simPop.

Depended on by Boruta.

Suggested by GSIF, IPMRF, MFKnockoffs, NAM, SuperLearner, bWGR, batchtools, climbeR, edarf, mlr, pdp, purge.

See at CRAN