DIYABC-RF  is an inference software implementing Approximate Bayesian Computation (ABC) combined with supervised machine learning based on Random Forests (RF), for model choice and parameter inference in the context of population genetics analysis.
If you are looking for the previous DIYABC V2.1: please check here, however we are not supporting this version anymore.
DIYABC-RF can be used through a graphical user interface (GUI) called
DIYABC-RF GUI, please visit the dedicated page for more information (and especially installation instructions).
DIYABC-RF GUI is a simple and user friendly interface for the
The complete interface documentation is available on the dedicated page.
If you encounter any issue, please visit and report bug at DIYABC-RF GUI issue tracker.
Please visit the dedicated page for more details.
 Collin F-D, Raynal L, Durif G, Gautier M, Vitalis R, Lombaert E., Marin J-M, Estoup A (2020) DIYABC Random Forest v1.0: extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms. Submitted to Molecular Ecology Resources.
diyabc github project: https://github.com/diyabc