Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder
Extract the compensator differences
Self-exciting Hawkes process(es)
Spatial or spatiotemporal log-Gaussian Cox process (LGCP)
Marked spatial log-Gaussian Cox process (mLGCP)
Modelling spatiotemporal self-excitement
Extract reported parameter estimates
A package to fit Hawkes and Log-Gaussian Cox Point Process models usin...
Estimated random field(s)
Mesh weights
Transform a fmesher::fm_mesh_2d into a sf object
Calculate a number of different geometric attributes of a Delaunay tri...
Plot the estimated random field(s) of a fitted LGCP
Plot Hawkes intensity
Plot the estimated intensity from a fitted LGCP model
Multivariate Hawkes fitted model plot
Simulate a self-exciting Hawkes process
Simulate a log-Gaussian Cox process (LGCP)
Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using 'TMB' (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the field(s).