spatstat is a family of R packages for analysing spatial point pattern data (and other kinds of spatial data). It has extensive capabilities for exploratory analysis, statistical modelling, simulation and statistical inference.
See the website www.spatstat.org or read the book.
spatstat has been split into a family of packagesOriginally there was a single package called spatstat. It grew so large that CRAN required us to split it into pieces.
The original spatstat has now been divided into a family of 10 sub-packages:
Click the green badge to visit the CRAN page which contains the current release of each sub-package. Click the blue badge to visit the GitHub repository for the current development version of the sub-package
There still exists a package called spatstat, which is now an umbrella package that requires all the sub-packages listed above, and provides introductory guides and vignettes.
You can install and load the new spatstat family in virtually the same way as you would previously have installed and loaded the old spatstat package. When you install the new umbrella package spatstat, all the sub-packages listed above will be installed. When you load the new umbrella spatstat package in an R session, all the sub-packages listed above will be loaded or imported.
Each official release has a version number like 1.2-3, while a development version has a number like 1.2-3.004, which R recognises as a later version than 1.2-3.
Additionally there are extension packages which contain more functionality. These packages are not automatically installed or loaded; the user must do that if these extra features are desired.
The pink box marked spatstat contains all the code that will be installed when you install the spatstat umbrella package, and loaded or imported when you load the spatstat umbrella package.
The blue boxes are extension packages which must be installed and loaded separately.
Installing the official releaseTo install the official release of spatstat from CRAN, start R and type
install.packages('spatstat', dependencies=TRUE)
This will install the 10 packages depicted in the pink box above.
To install the extension packages (blue boxes) you need to do the same thing for each extension package, e.g.
install.packages('spatstat.local')
To check that the installation has been successful, check that the version numbers of the packages (which are printed when you load the packages) match the version numbers of the official releases listed above (green badges). If this is not true, you may need to un-install the previous installation of spatstat, or check the file permissions which apply to the filespace where R is installed.
You can install the development version of spatstat from the GitHub repositories (which you are visiting now) or from r-universe.
The easiest way is to install the development version from r-universe:
repo <- c('https://spatstat.r-universe.dev', 'https://cloud.r-project.org')
install.packages("spatstat", dependencies=TRUE, repos=repo)
and again to install the development version of the extension package spatstat.local,
install.packages("spatstat.local", repos=repo)
Check that the installation was successful by comparing version numbers as explained above.
Users are encouraged to report bugs. If you find a bug in a spatstat function, please identify the sub-package containing that function. Visit the GitHub repository for the sub-package, click the Issues tab at the top of the page, and press new issue to start a new bug report, documentation correction or feature request.
Please do not post questions on the Issues pages, because they are too clunky for correspondence.
For questions about spatstat, first check the question-and-answer website stackoverflow to see whether your question has already been asked and answered. If not, you can either post your question at stackoverflow, or email the authors.
Feel free to fork spatstat or one of its sub-packages, make changes to the code, and ask us to include them in the package by making a github pull request.
spatstat is the result of 30 years of software development and contains over 190,000 lines of code. It is still under development, motivated by the needs of researchers in many fields, and driven by innovations in statistical science. We welcome contributions of code, and suggestions for improvements.
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