Abstract
This paper describes the package <b>PtProcess</b> which uses the <b>R</b> statistical language. The package provides a unified approach to fitting and simulating a wide variety of temporal point process or temporal marked point process models. The models are specified by an intensity function which is conditional on the history of the process. The user needs to provide routines for calculating the conditional intensity function. Then the package enables one to carry out maximum likelihood fitting, goodness of fit testing, simulation and comparison of models. The package includes the routines for the conditional intensity functions for a variety of standard point process models. The package is intended to simplify the fitting of point process models indexed by time in much the same way as generalized linear model programs have simplified the fitting of various linear models. The primary examples used in this paper are earthquake sequences but the package is intended to have a much wider applicability.
Highlights
This paper describes a unified approach to fitting and simulating a wide variety of temporal point process or temporal marked point process models using the R statistical language (R Development Core Team 2010)
These models are defined in terms of a conditional intensity function, i.e., conditional on the history of the process over time
An example of a ground intensity function is that of the simple ETAS model, i.e., epidemic type aftershock sequence model used in modelling earthquake counts, see Ogata (1988, 1998, 1999)
Summary
This paper describes a unified approach to fitting and simulating a wide variety of temporal point process or temporal marked point process models using the R statistical language (R Development Core Team 2010). These models are defined in terms of a conditional intensity function, i.e., conditional on the history of the process over time. PtProcess: Modelling Marked Point Processes Indexed by Time in R aftershocks These characteristics can be observed in the upper panel of Figure 1.
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