A comprehensive family of R packages for analyzing spatial point pattern data and other spatial data types.
spatstat is a family of R packages for the statistical analysis of spatial point pattern data and other spatial data types. It provides a comprehensive suite of tools for exploratory data analysis, statistical modeling, simulation, and inference, addressing the need for rigorous spatial statistics in research and applied work. The project originated as a single package and has evolved into a modular family to manage its extensive functionality.
Researchers, data scientists, and analysts in fields like ecology, epidemiology, geography, and materials science who work with spatial data and require advanced statistical methods. It is also valuable for statisticians developing or applying spatial methodology.
Developers choose spatstat for its unparalleled breadth and depth in spatial statistics, its long-standing reputation in the research community, and its open-source nature. It offers a cohesive, well-documented ecosystem that integrates exploratory, modeling, and simulation tools, reducing the need to assemble disparate packages.
Umbrella package of the 'spatstat' family................
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Covers the entire workflow from exploratory analysis to formal inference, with 10 core sub-packages dedicated to geometry, modeling, simulation, and more, as outlined in the package structure.
Split into a family of sub-packages for better maintainability, while the umbrella package provides a cohesive installation and loading experience, addressing CRAN size constraints.
Includes spatstat.linnet for spatial analysis on linear networks, a niche feature valuable for fields like transportation or epidemiology, as highlighted in the key features.
Supported by a dedicated website, book, vignettes, and active community on stackoverflow, with clear bug reporting via GitHub issues for each sub-package.
Installation involves handling 10 core packages and separate extensions, with dependencies and frequent updates, making setup and maintenance cumbersome for users.
With over 200,000 lines of code and decades of development, mastering the extensive functionality requires significant time and statistical background.
Development versions are updated almost daily, and the modular split means users must ensure compatibility across packages, risking instability in workflows.