Modeling point processes in R

There has been a major effort to develop libraries for R to carry out point process analysis. There are a number of libraries available for the analysis of multidimensional point processes. Baddeley and Turner have implemented "<code>spatstat</code>". Rowlingson and Diggle have provided another library for two-dimensional point processes called "<code>splancs</code>". "<code>ptproc</code>" is another major library that is developed at UCLA .
In order to do stochastic modeling for the point processes Baddeley and Turner have implemented the techniques as a package named "<code>spatstat</code>" in the . Both <code>spatstat</code> and R are freely available for download from the R website R-project. One can carry out basic Poisson regressions in R using GLM function.
The <code>spatstat</code> Package
The <code>spatstat</code> package is used to do analysis on spacial point processes. it includes:
#Tools for exploratory data analysis
#Convenient graphical facilities
#Tools to simulate a wide range of point pattern models
#Versatile model-fitting capabilities
#Model diagnostics.
The <code>spatstat</code> package is considered to be one of the largest contributions to the R project, the package contains about 300 user-level functions and a 500-page manual.
Stochastic Modeling Using <code>spatstat</code> and R
The <code>spatstat</code> package can be used to fit Poisson point process models, Gibbs point process models and random cluster process models to a point pattern dataset. It can be used for both homogeneous and inhomogeneous models.

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