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We introduce the C++ application and R package ranger. The software is a fastimplementation of random forests for high dimensional data. Ensembles ofclassification, regression and survival trees are supported. We describe theimplementation, provide examples, validate the package with a referenceimplementation, and compare runtime and memory usage with otherimplementations. The new software proves to scale best with the number offeatures, samples, trees, and features tried for splitting. Finally, we showthat ranger is the fastest and most memory efficient implementation of randomforests to analyze data on the scale of a genome-wide association study.

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R