{"added":null,"apiVersion":1,"behindSince":null,"buildDepends":[],"categories":["geography","math","r"],"comment":"Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests","compare":null,"dependentCount":null,"description":"Comprehensive open-source toolbox for analysing Spatial Point\nPatterns. Focused mainly on two-dimensional point patterns, including\nmultitype/marked points, in any spatial region. Also supports\nthree-dimensional point patterns, space-time point patterns in any\nnumber of dimensions, point patterns on a linear network, and patterns\nof other geometrical objects. Supports spatial covariate data such as\npixel images. Contains over 2000 functions for plotting spatial data,\nexploratory data analysis, model-fitting, simulation, spatial\nsampling, model diagnostics, and formal inference. Many data types and\nexploratory methods are supported.  Formal hypothesis tests of random\npattern and tests for covariate effects are also supported. Parametric\nmodels can be fitted to point pattern data using the functions ppm(),\nkppm(), slrm(), dppm() similar to glm(). Types of models include\nPoisson, Gibbs and Cox point processes, Neyman-Scott cluster\nprocesses, and determinantal point processes. Models may involve\ndependence on covariates, inter-point interaction, cluster formation\nand dependence on marks. Models are fitted by maximum likelihood,\nlogistic regression, minimum contrast, and composite likelihood\nmethods. A model can be fitted to a list of point patterns (replicated\npoint pattern data) using the function mppm(). The model can include\nrandom effects and fixed effects depending on the experimental design,\nin addition to all the features listed above.  Fitted point process\nmodels can be simulated, automatically. Formal hypothesis tests of a\nfitted model are supported along with basic tools for model selection.","flavors":[],"html":"https://daemonports.org/netbsd/geography/R-spatstat/","kind":"port","lastUpdate":null,"license":["gnu-gpl-v2"],"maintainer":"pkgsrc-users@NetBSD.org","name":"R-spatstat","newest":false,"newestVersion":null,"noVulnData":false,"origin":"geography/R-spatstat","otherSystems":[],"packages":[{"repo":"NetBSD 10.1 x86_64","versions":["3.6.0"]},{"repo":"NetBSD 10.1 aarch64","versions":["3.6.0"]},{"repo":"SmartOS / illumos x86_64","versions":["3.6.0"]},{"repo":"macOS 15 arm64","versions":["3.6.0"]}],"pkgName":"R-spatstat-3.6.0","related":null,"repository":"pkgsrc","requiredBy":[],"requiredByCount":0,"runDepends":["geography/R-deldir","geography/R-spatstat.data","geography/R-spatstat.geom","geography/R-spatstat.linnet","geography/R-spatstat.model","geography/R-spatstat.utils","graphics/R-polyclip","math/R","math/R-abind","math/R-goftest","math/R-tensor"],"status":null,"system":"netbsd","systemLabel":"NetBSD","version":"3.6.0","versions":null,"vulnerabilities":null,"www":"https://CRAN.R-project.org/package=spatstat"}