  Masked autoregressive flow (MAF) is a state-of-the-art non-parametric density
estimation technique. It is based on the idea (known as a normalizing flow)
that a simple base probability distribution can be mapped into a complicated
target distribution that one wishes to approximate, using a sequence of
bijective transformations. The denmarf package provides a scikit-learn-like
interface in Python for researchers to effortlessly use MAF for density
estimation in their applications to evaluate probability densities of the
underlying distribution of a set of data and generate new samples from the
data, on either a CPU or a GPU, as simple as "from denmarf import
DensityEstimate; de = DensityEstimate().fit(X)". The package also implements
logistic transformations to facilitate the fitting of bounded distributions.
