  Determinantal point processes (DPPs) are specific probability distributions
over clouds of points that are used as models and computational tools across
physics, probability, statistics, and more recently machine learning. Sampling
from DPPs is a challenge and therefore we present DPPy, a Python toolbox that
gathers known exact and approximate sampling algorithms for both finite and
continuous DPPs. The project is hosted on GitHub and equipped with an extensive
documentation.
