Module for statistical learning, with a particular emphasis on time-dependent modelling

Umbrella package of the 'spatstat' family................

Spatiotemporal epidemic model introduced in the context of COVID-19, ACM TSAS, 2022

Pieces of code that have appeared on my blog with a focus on stochastic simulations.

A Spatio-temporal point process simulator.

A general framework for learning spatio-temporal point processes via reinforcement learning

Deep Point Process by PyTorch

Code for "Long Horizon Forecasting With Temporal Point Processes", WSDM 2021

A package for temporal point process modeling, simulation and inference (unmaintained)

Code and real data for "Counterfactual Temporal Point Processes", NeurIPS 2022

A method for event correlation detection based on Spatial-Temporal-Textual point process

PPG (Point Process Generator) is a Reinforcement Learning framework that is able to produce actions by imitating expert sequences.

Sub-package of spatstat containing all datasets

sub-package of spatstat containing core functionality for data analysis and modelling

3D object-based model of braided river deposits (marked point process), an open-source software package (R language)

Python Package for simulation and estimation of Hawkes processes

Efficient point process inference for large scale object detection

Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis

Hidden Markov Hawkes Process - Model for Analyzing Topical Transitions in text based cascades in Social Networks.

Tools for evaluating the goodness of fit of a point process model via the time rescaling theorem