Functionality for data analysis and modelling ofspatial data mainly spatial point patternsin the spatstat family of packages.Excludes analysis of spatial data on a linear networkwhich 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.