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.

Homepage:
https://CRAN.R-project.org/package=spatstat.model
