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
Current version

DIYABC-RF allows population biologists to make inference based on Approximate Bayesian Computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIYABC can be used to compare competing scenarios, estimate parameters for one or more scenarios, and compute bias and precision measures for a given scenario and known values of parameters.

Developed by​

If you have any question, please feel free to contact me. However, I strongly recommend you read the manual first.

Send a message


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, 21(8), 2598–2613. (



Last updated

By […] on 2021-09-29