  We introduce the information geometry module of the Python package Geomstats.
The module first implements Fisher-Rao Riemannian manifolds of widely used
parametric families of probability distributions, such as normal, gamma, beta,
Dirichlet distributions, and more. The module further gives the Fisher-Rao
Riemannian geometry of any parametric family of distributions of interest,
given a parameterized probability density function as input. The implemented
Riemannian geometry tools allow users to compare, average, interpolate between
distributions inside a given family. Importantly, such capabilities open the
door to statistics and machine learning on probability distributions. We
present the object-oriented implementation of the module along with
illustrative examples and show how it can be used to perform learning on
manifolds of parametric probability distributions.
