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This paper introduces $\textit{arfpy}$, a python implementation ofAdversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweightprocedure for synthesizing new data that resembles some given data. Thesoftware $\textit{arfpy}$ equips practitioners with straightforwardfunctionalities for both density estimation and generative modeling. The methodis particularly useful for tabular data and its competitive performance isdemonstrated in previous literature. As a major advantage over the mostly deeplearning based alternatives, $\textit{arfpy}$ combines the method's reducedrequirements in tuning efforts and computational resources with a user-friendlypython interface. This supplies audiences across scientific fields withsoftware to generate data effortlessly.

arfpy: A python package for density estimation and generative modeling with adversarial random forests