Umbrella package of the 'spatstat' family................
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Updated
Jun 7, 2024 - R
Umbrella package of the 'spatstat' family................
Module for statistical learning, with a particular emphasis on time-dependent modelling
Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
Sub-package of spatstat containing all datasets
Pieces of code that have appeared on my blog with a focus on stochastic simulations.
Offers models and utilities for event time data using point processes.
Spatiotemporal epidemic model introduced in the context of COVID-19, ACM TSAS, 2022
Sample a repelled point process, compute a Monte Carlo estimation for the integral of a function using various variants of the Monte Carlo method including the Monte Carlo with a repelled point process, and visualize gravitational allocations 2D.
A package for temporal point process modeling, simulation and inference (unmaintained)
Exploration of signal with non-overlapping rectangular pulses in the context of flicker noise.
A Spatio-temporal point process simulator.
Python Package for simulation and estimation of Hawkes processes
Maximum likelihood estimation of Hawkes processes
Code for "Fast Bayesian Estimation of Point Process Intensity as Function of Covariates" at NeurIPS2022
Analysis of point process driven by fractional Brownian motion.
Code and real data for "Counterfactual Temporal Point Processes", NeurIPS 2022
Hidden Markov Hawkes Process - Model for Analyzing Topical Transitions in text based cascades in Social Networks.
🧠 A decoder of Spike Trains using 🔬 Bayesian, State-Space, 📐 Point-Processes, EM-algorithm, Maximum-likelihood
sub-package of spatstat containing core functionality for data analysis and modelling
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