arfpy: A python package for density estimation and generative modeling with adversarial random forests
It provides a user-friendly tool for practitioners across scientific fields to generate data with minimal effort, though it is incremental as it builds on existing ARF methodology.
The paper introduces arfpy, a Python package implementing Adversarial Random Forests for density estimation and generative modeling on tabular data, offering reduced tuning and computational requirements compared to deep learning alternatives.
This paper introduces $\textit{arfpy}$, a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new data that resembles some given data. The software $\textit{arfpy}$ equips practitioners with straightforward functionalities for both density estimation and generative modeling. The method is particularly useful for tabular data and its competitive performance is demonstrated in previous literature. As a major advantage over the mostly deep learning based alternatives, $\textit{arfpy}$ combines the method's reduced requirements in tuning efforts and computational resources with a user-friendly python interface. This supplies audiences across scientific fields with software to generate data effortlessly.