  PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible
designing and efficient learning of point process models. It can be used for
interpretable sequential data modeling and analysis, e.g., Granger causality
analysis of multi-variate point processes, point process-based simulation and
prediction of event sequences. In practice, the key points of point
process-based sequential data modeling include: 1) How to design intensity
functions to describe the mechanism behind observed data? 2) How to learn the
proposed intensity functions from observed data? The goal of PoPPy is providing
a user-friendly solution to the key points above and achieving large-scale
point process-based sequential data analysis, simulation and prediction.
