Package: r-cran-spatstat
Version: 3.6-0-1
Architecture: all
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 5483
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-spatstat.model (>= 3.7-0), r-cran-spatstat.linnet (>= 3.5-0), r-cran-spatstat.utils (>= 3.2-2)
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=spatstat
Description: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
 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 3000
 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.
