Renewal Hawkes Process
Dynamically approxomated minus loglikelihood of a RHawkes model
Partial EM algorithm for the RHawkes process, version 1
Partial EM algorithm for the RHawkes process, version 2
Minus loglikelihood of a RHawkes model
Minus loglikelihood of a RHawkes model with parent probabilities
Minus loglikelihood of a RHawkes model with Rosenblatt residuals
RHawkes predictive density function
RHawkes predictive hazard function
tools:::Rd_package_title("RHawkes")
Simulate a fitted RHawkes process model
Simulate a fitted RHawkes process model for prediction purposes
Simulate a renewal Hawkes (RHawkes) process
Simulate a renewal Hawkes (RHawkes) process
The renewal Hawkes (RHawkes) process (Wheatley, Filimonov, and Sornette, 2016 <doi:10.1016/j.csda.2015.08.007>) is an extension to the classical Hawkes self-exciting point process widely used in the modelling of clustered event sequence data. This package provides functions to simulate the RHawkes process with a given immigrant hazard rate function and offspring birth time density function, to compute the exact likelihood of a RHawkes process using the recursive algorithm proposed by Chen and Stindl (2018) <doi:10.1080/10618600.2017.1341324>, to compute the Rosenblatt residuals for goodness-of-fit assessment, and to predict future event times based on observed event times up to a given time. A function implementing the linear time RHawkes process likelihood approximation algorithm proposed in Stindl and Chen (2021) <doi:10.1007/s11222-021-10002-0> is also included.