Package: r-cran-spatstat.model
Version: 3.7-0-1
Architecture: amd64
Architecture-Variant: amd64v3
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 3871
Depends: r-api-4.0, r-cran-spatstat.data (>= 3.1-9), r-cran-spatstat.univar (>= 3.1-7), r-cran-spatstat.geom (>= 3.7-3), r-cran-spatstat.random (>= 3.4-5), r-cran-spatstat.explore (>= 3.8-0), r-cran-nlme, r-cran-rpart, r-cran-spatstat.utils (>= 3.2-2), r-cran-spatstat.sparse (>= 3.1), r-cran-mgcv, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-goftest (>= 1.2-2)
Recommends: r-cran-sm, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-fftwtools (>= 0.9-8), r-cran-nleqslv, r-cran-glmnet, r-cran-spatstat.linnet (>= 3.4-1), r-cran-spatstat (>= 3.5-1), r-cran-testthat
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=spatstat.model
Description: Parametric Statistical Modelling and Inference for the 'spatstat' Family
 Functionality for parametric statistical modelling and inference for spatial
 data, mainly spatial point patterns, in the 'spatstat' family of packages.
 (Excludes analysis of spatial data on a linear network, which is covered
 by the separate package 'spatstat.linnet'.) Supports parametric modelling,
 formal statistical inference, and model validation. Parametric models include
 Poisson point processes, Cox point processes, Neyman-Scott cluster processes,
 Gibbs point processes and determinantal point processes. Models can
 be fitted to data using maximum likelihood, maximum pseudolikelihood,
 maximum composite likelihood and the method of minimum contrast. Fitted models
 can be simulated and predicted. Formal inference includes hypothesis tests
 (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test,
 Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test,
 segregation test, ANOVA tests of fitted models, adjusted composite likelihood
 ratio test, envelope tests, Dao-Genton test, balanced independent two-stage
 test), confidence intervals for parameters, and prediction intervals for
 point counts. Model validation techniques include leverage, influence, partial
 residuals, added variable plots, diagnostic plots, pseudoscore residual plots,
 model compensators and Q-Q plots.
