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R-CMD-check CRAN Downloads monthCRAN Downloads overall ## ranger: A Fast Implementation of Random Forests Marvin N. Wright


ranger is a fast implementation of random forests (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). Includes implementations of extremely randomized trees (Geurts et al. 2006) and quantile regression forests (Meinshausen 2006).

ranger is written in C++, but a version for R is available, too. We recommend to use the R version. It is easy to install and use and the results are readily available for further analysis. The R version is as fast as the standalone C++ version.


R version

To install the ranger R package from CRAN, just run

R version >= 3.1 is required. With recent R versions, multithreading on Windows platforms should just work. If you compile yourself, the new RTools toolchain is required.

To install the development version from GitHub using devtools, run


Standalone C++ version

To install the C++ version of ranger in Linux or Mac OS X you will need a compiler supporting C++14 (i.e. gcc >= 5 or Clang >= 3.4) and Cmake. To build start a terminal from the ranger main directory and run the following commands

cd cpp_version
mkdir build
cd build
cmake ..

After compilation there should be an executable called “ranger” in the build directory.

To run the C++ version in Microsoft Windows please cross compile or ask for a binary.


R version

For usage of the R version see ?ranger in R. Most importantly, see the Examples section. As a first example you could try

ranger(Species ~ ., data = iris)

Standalone C++ version

In the C++ version type

./ranger --help 

for a list of commands. First you need a training dataset in a file. This file should contain one header line with variable names and one line with variable values per sample (numeric only). Variable names must not contain any whitespace, comma or semicolon. Values can be separated by whitespace, comma or semicolon but can not be mixed in one file. A typical call of ranger would be for example

./ranger --verbose --file data.dat --depvarname Species --treetype 1 --ntree 1000 --nthreads 4

If you find any bugs, or if you experience any crashes, please report to us. If you have any questions just ask, we won’t bite.

Please cite our paper if you use ranger.