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.
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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