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DIYABC-RF [1] 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.

:warning: If you are looking for the previous DIYABC V2.1: please check here, however we are not supporting this version anymore.

Graphical User Interface

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 DIYABC-RF framework.

The complete interface documentation is available on the dedicated page.

:warning: If you encounter any issue, please visit and report bug at DIYABC-RF GUI issue tracker.

Command line tools

For advanced users, it is possible to use DIYABC-RF as a command-line pipeline based on the command-line softwares diyabc and abcranger.

Please visit the dedicated page for more details.


[1] 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: