You are viewing the GitHub repository which holds the latest development version of spatstat.explore. For the latest public release on CRAN, click the green badge above.

• Overview of spatstat.explore
• Detailed contents of package
• Installing the package
• Bug reports
• Questions
• Proposing changes to code
• Future development

The original spatstat package has been split into several sub-packages (See spatstat/spatstat)

This package spatstat.explore is one of the sub-packages. It contains the main user-level functions that perform exploratory and nonparametric statistical analysis of spatial data, with the exception of data on linear networks.

Most of the functionality is for spatial point patterns in two dimensions. There is a very modest amount of functionality for 3D and higher dimensional patterns and space-time patterns.

spatstat.explore supports

• data manipulation and exploratory graphics
• exploratory analysis
• smoothing
• cluster detection
• nonparametric estimation
• hypothesis tests (simulation-based and nonparametric)

For a full list of functions, see the help file for spatstat.explore-package.

• Clark-Evans index, Hopkins-Skellam index
• quadrat counting estimates of intensity, quadrat counting test
• Fry plot
• Morisita plot
• scan statistic
• cluster detection (Allard-Fraley cluster set, Byers-Raftery cleaning)

• kernel estimation of intensity of a point pattern
• kernel smoothing of mark values attached to point locations
• kernel estimation of relative risk
• kernel smoothing of a line segment pattern
• bandwidth selection
• spatial CDF
• nonparametric estimation of intensity as a function of a covariate
• ROC curve, AUC
• Sufficient Data Reduction
• optimal thresholding of a covariate

• summary functions (K-function, pair correlation function, empty space function, nearest neighbour distance function, J-function, etc) and multi-type versions of these functions
• mark correlation function, mark independence diagnostic
• local summary functions (LISA)
• simulation envelopes of summary functions
• manipulation of summary functions (plot, evaluate, differentiate, smooth etc)

• spatial bootstrap
• asymptotic variance estimates
• hypothesis tests (quadrat test, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, envelope tests, Dao-Genton test, balanced independent two-stage test)

• image blurring
• Choi-Hall data sharpening of point locations
• transects of an image along a line or curve
• programming tools

This repository contains the development version of spatstat.explore. The easiest way to install the development version is to start R and type

repo <- c('https://spatstat.r-universe.dev', 'https://cloud.r-project.org')
install.packages("spatstat.explore", dependencies=TRUE, repos=repo)

To install the latest public release of spatstat.explore, type

install.packages("spatstat.explore")

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 the spatstat package family, 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.explore, make changes to the code, and ask us to include them in the package by making a github pull request.

The spatstat package family is the result of 30 years of software development and contains over 200,000 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.