Résumé
The AbcRanger library provides methodologies for model choice and parameter estimation based on fast and scalable Random Forests, tuned to handle large and/or high dimensional datasets. The library, initially intended for the population genetics ABC framework DIYABC, has been generalized to any ABC reference table generator. At first, computational issues were encountered with the reference ABC-Random Forest. Those issues have been diagnosed by us as friction between "strict" Machine Learning setup and ABC context, and this incited us to modify the C++ implementation of state-of-the-art random forests, ranger, to tailor it for ABC needs: potentially "deep" decision trees are not stored in memory anymore, but are processed by batches in parallel. We focused on memory and thread scalability, ease of use (minimal hyperparameter set). R and python interfaces are provided.