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Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios anddoi:10.1038/s41467-018-03100-7 pmid:29459739 pmcid:PMC5818606 fatcat:xadvvke23zbrrcei3bnf65hqum