Feedstock license: BSD-3-Clause

Home: http://www.spatstat.org

Package license: GPL-2.0-or-later

Summary: Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

Development: https://github.com/spatstat/spatstat

All platforms:

Name	Downloads	Version	Platforms

Installing r-spatstat from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge
conda config --set channel_priority strict

conda install r-spatstat

mamba install r-spatstat

# for adding to your local project
pixi add r-spatstat
# for installing globally
pixi global install r-spatstat

It is possible to list all of the versions of r-spatstat available on your platform:

conda search r-spatstat --channel conda-forge

mamba search r-spatstat --channel conda-forge

pixi search r-spatstat --channel conda-forge

# Search all versions available on your platform:
mamba repoquery search r-spatstat --channel conda-forge

# List packages depending on `r-spatstat`:
mamba repoquery whoneeds r-spatstat --channel conda-forge

# List dependencies of `r-spatstat`:
mamba repoquery depends r-spatstat --channel conda-forge

conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by Azure, GitHub, CircleCI, AppVeyor, Drone, and TravisCI it is possible to build and upload installable packages to the conda-forge anaconda.org channel for Linux, Windows and OSX respectively.

To manage the continuous integration and simplify feedstock maintenance, conda-smithy has been developed. Using the conda-forge.yml within this repository, it is possible to re-render all of this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.

For more information, please check the conda-forge documentation.

feedstock - the conda recipe (raw material), supporting scripts and CI configuration.

conda-smithy - the tool which helps orchestrate the feedstock. Its primary use is in the construction of the CI .yml files and simplify the management of many feedstocks.

conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)

If you would like to improve the r-spatstat recipe or build a new package version, please fork this repository and submit a PR. Upon submission, your changes will be run on the appropriate platforms to give the reviewer an opportunity to confirm that the changes result in a successful build. Once merged, the recipe will be re-built and uploaded automatically to the conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge channel. Note that all branches in the conda-forge/r-spatstat-feedstock are immediately built and any created packages are uploaded, so PRs should be based on branches in forks, and branches in the main repository should only be used to build distinct package versions.

In order to produce a uniquely identifiable distribution:

• If the version of a package is not being increased, please add or increase the build/number.
• If the version of a package is being increased, please remember to return the build/number back to 0.

• @conda-forge/r
• @ocefpaf