{"added":null,"apiVersion":1,"behindSince":null,"buildDepends":[],"categories":["geography","math","r"],"comment":"Linear Networks Functionality of the 'spatstat' Family","compare":null,"dependentCount":null,"description":"Defines types of spatial data on a linear network and provides\nfunctionality for geometrical operations, data analysis and modelling\nof data on a linear network, in the 'spatstat' family of packages.\nContains definitions and support for linear networks, including\ncreation of networks, geometrical measurements, topological\nconnectivity, geometrical operations such as inserting and deleting\nvertices, intersecting a network with another object, and interactive\nediting of networks. Data types defined on a network include point\npatterns, pixel images, functions, and tessellations. Exploratory\nmethods include kernel estimation of intensity on a network,\nK-functions and pair correlation functions on a network, simulation\nenvelopes, nearest neighbour distance and empty space distance,\nrelative risk estimation with cross-validated bandwidth selection.\nFormal hypothesis tests of random pattern (chi-squared,\nKolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford,\nDao-Genton, two-stage Monte Carlo) and tests for covariate effects\n(Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also\nsupported. Parametric models can be fitted to point pattern data using\nthe function lppm() similar to glm(). Only Poisson models are\nimplemented so far. Models may involve dependence on covariates and\ndependence on marks. Models are fitted by maximum likelihood. Fitted\npoint process models can be simulated, automatically. Formal\nhypothesis tests of a fitted model are supported (likelihood ratio\ntest, analysis of deviance, Monte Carlo tests) along with basic tools\nfor model selection (stepwise(), AIC()) and variable selection (sdr).\nTools for validating the fitted model include simulation envelopes,\nresiduals, residual plots and Q-Q plots, leverage and influence\ndiagnostics, partial residuals, and added variable plots. Random point\npatterns on a network can be generated using a variety of models.","flavors":[],"html":"https://daemonports.org/netbsd/geography/R-spatstat.linnet/","kind":"port","lastUpdate":null,"license":["gnu-gpl-v2 OR gnu-gpl-v3"],"maintainer":"pkgsrc-users@NetBSD.org","name":"R-spatstat.linnet","newest":false,"newestVersion":null,"noVulnData":false,"origin":"geography/R-spatstat.linnet","otherSystems":[],"packages":[{"repo":"NetBSD 10.1 x86_64","versions":["3.5.1"]},{"repo":"NetBSD 10.1 aarch64","versions":["3.5.1"]},{"repo":"SmartOS / illumos x86_64","versions":["3.5.1"]},{"repo":"macOS 15 arm64","versions":["3.5.1"]}],"pkgName":"R-spatstat.linnet-3.5.1","related":null,"repository":"pkgsrc","requiredBy":["geography/R-spatstat"],"requiredByCount":1,"runDepends":["geography/R-spatstat.data","geography/R-spatstat.explore","geography/R-spatstat.geom","geography/R-spatstat.model","geography/R-spatstat.random","geography/R-spatstat.utils","math/R"],"status":null,"system":"netbsd","systemLabel":"NetBSD","version":"3.5.1","versions":null,"vulnerabilities":null,"www":"https://CRAN.R-project.org/package=spatstat.linnet"}