Source: r-cran-spatstat.core
Maintainer: Debian R Packages Maintainers
Uploaders: Andreas Tille
Section: gnu-r
Testsuite: autopkgtest-pkg-r
Priority: optional
Build-Depends: debhelper-compat (= 13),
dh-r,
r-base-dev,
r-cran-spatstat.data (>= 2.1-0),
r-cran-spatstat.geom (>= 2.3-0),
r-cran-nlme,
r-cran-rpart,
r-cran-spatstat.utils (>= 2.2-0),
r-cran-spatstat.sparse (>= 2.0-0),
r-cran-mgcv,
r-cran-matrix,
r-cran-abind,
r-cran-tensor,
r-cran-goftest
Standards-Version: 4.6.0
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-spatstat.core
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-spatstat.core.git
Homepage: https://cran.r-project.org/package=spatstat.core
Rules-Requires-Root: no
Package: r-cran-spatstat.core
Architecture: any
Depends: ${R:Depends},
${shlibs:Depends},
${misc:Depends}
Recommends: ${R:Recommends}
Suggests: ${R:Suggests}
Description: core functionality of the 'spatstat' family of GNU R packages
Functionality for data analysis and modelling of 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'.) 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.