Link Search Menu Expand Document


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 maintaining this version anymore.


The complete software documentation for the graphical user interface (GUI) and the command line interface (CLI) is available on the dedicated page.

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.

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

Command line interface

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

Please visit the dedicated page for more details.

DIYABC-RF project examples

Please visit the dedicated page to find examples of DIYABC-RF data analysis projects, including data files and related DIYABC-RF configuration files.


[1] Collin F-D, Durif G, Raynal L, Gautier M, Vitalis R, Lombaert E., Marin J-M, Estoup A., 2021, Extending Approximate Bayesian Computation with Supervised Machine Learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest. Molecular Ecology Resources, Wiley/Blackwell, 21(8), pp. 2598–2613. <doi/10.1111/1755-0998.13413> <hal-03229207>

diyabc github project: