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IHSEP - Inhomogeneous Self-Exciting Process

Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.

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cpp

2.01 score 2 dependents 17 scripts 193 downloads

lpint - Local Polynomial Estimators of the Intensity Function and Its Derivatives

Functions to estimate the intensity function and its derivative of a given order of a multiplicative counting process using the local polynomial method.

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1.95 score 3 dependents 2 scripts 247 downloads

ibs - Integral of B-Spline Functions

Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function.

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cpp

1.30 score 3 scripts 194 downloads

coxsei - Fitting a CoxSEI Model

Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.

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1.08 score 12 scripts 581 downloads

RHawkes - Renewal Hawkes 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.

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1.04 score 1 stars 11 scripts 204 downloads