arfpy: A python package for density estimation and generative modeling
  with adversarial random forests
Kristin Blesch Marvin N. Wright
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.