Defines spatial data types and supports geometrical operations
	     on them. Data types include point patterns, windows (domains),
	     pixel images, line segment patterns, tessellations and hyperframes.
	     Capabilities include creation and manipulation of data
	     (using command line or graphical interaction),
	     plotting, geometrical operations (rotation, shift, rescale,
	     affine transformation), convex hull, discretisation and
	     pixellation, Dirichlet tessellation, Delaunay triangulation,
	     pairwise distances, nearest-neighbour distances,
	     distance transform, morphological operations
	     (erosion, dilation, closing, opening), quadrat counting,
	     geometrical measurement, geometrical covariance,
	     colour maps, calculus on spatial domains,
	     Gaussian blur, level sets of images, transects of images,
	     intersections between objects, minimum distance matching.
	     (Excludes spatial data on a network, which are supported by
	     the package 'spatstat.linnet'.)

Extension to the 'spatstat' package, containing
	     interactive graphics capabilities.

Extension to the 'spatstat' family of packages, for analysing
	     large datasets of spatial points on a network. The geometrically-
	     corrected K function is computed using a memory-efficient
	     tree-based algorithm described by Rakshit, Baddeley and Nair (2019).

Defines types of spatial data on a linear network
	     and provides functionality for geometrical operations,
	     data analysis and modelling of data on a linear network,
	     in the 'spatstat' family of packages.
	     Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks.
	     Data types defined on a network include point patterns, pixel images, functions, and tessellations.
	     Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. 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 function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood.
	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.
	Random point patterns on a network can be generated using a variety of models.