v.1.0.0

First release with basic funtionality.

General:

• Autoregressive conditional structure is taken care of behind the scenes and connects manifolds
• Coverage is straightforward. Everything (including spherical, interval and simplex flows) is based on a Gaussian base distribution (arXiv:2008.0582).
• Bisection & Newton iterations for differentiable inverse (used for certain non-analytic inverse flow functions)
• amortizable MLPs that can use low-rank approximations
• amortizable PDFs - the total PDF can be the output of another neural network
• unit tests that make sure backwards / and forward flow passes of all implemented flow-layers agree
• include log-lambda as an additional flow parameter to define parametrized Poisson-Processes
• easily extendible: define new Euclidean / spherical flow layers by subclassing Euclidean or spherical base classes

Euclidean flows:

• Generic affine flow (Multivariate normal distribution) ("t")
• Gaussianization flow arXiv:2003.01941 ("g")
• Hybrid of nonlinear scalings and rotations ("Polynomial Stretch flow") ("p")

Spherical flows:

S1:

• Moebius transformations (described in arXiv:2002.02428) ("m")
• Circular rational-quadratic splines (described in arXiv:2002.02428) ("o")

S2:

• Autorregressive flow for N-Spheres (arXiv:2002.02428) ("n")
• Exponential map flow (arXiv:0906.0874/arXiv:2002.02428) ("v")
• Neural Manifold Ordinary Differential Equations arXiv:2006.10254 ("c")

Interval Flows:

• "Neural Spline Flows" (Rational-quadratic splines) arXiv:1906.04032 ("r")

Simplex Flows:

• Autoregressive simplex flow arXiv:2008.05456 ("w")