The paper introduces the spatstat package, an R software for analyzing spatial point patterns. spatstat supports various functionalities including exploratory data analysis, model fitting, and simulation. It handles realistic datasets with inhomogeneous point patterns, arbitrary-shaped spatial sampling regions, extra covariate data, and marks attached to points. A key feature is its generic algorithm for fitting point process models to point pattern data, accessible via the ppm function, which is analogous to glm and lm in R. The package can handle complex datasets, such as those with multiple types of points and irregular sampling regions. It provides tools for creating, manipulating, and plotting point patterns, as well as performing exploratory data analysis using standard empirical summaries and other statistical techniques. spatstat also supports parametric model fitting, including models with spatial trend, covariate effects, and interpoint interactions. The package includes functions for fitting various point process models, such as Poisson, Strauss, and Poisson with log-linear or log-quadratic intensity functions. It also offers methods for simulating point processes and computing summary statistics for unmarked and multitype point patterns. The paper includes examples and demonstrations of spatstat's capabilities, emphasizing its flexibility and robustness in handling complex spatial data.The paper introduces the spatstat package, an R software for analyzing spatial point patterns. spatstat supports various functionalities including exploratory data analysis, model fitting, and simulation. It handles realistic datasets with inhomogeneous point patterns, arbitrary-shaped spatial sampling regions, extra covariate data, and marks attached to points. A key feature is its generic algorithm for fitting point process models to point pattern data, accessible via the ppm function, which is analogous to glm and lm in R. The package can handle complex datasets, such as those with multiple types of points and irregular sampling regions. It provides tools for creating, manipulating, and plotting point patterns, as well as performing exploratory data analysis using standard empirical summaries and other statistical techniques. spatstat also supports parametric model fitting, including models with spatial trend, covariate effects, and interpoint interactions. The package includes functions for fitting various point process models, such as Poisson, Strauss, and Poisson with log-linear or log-quadratic intensity functions. It also offers methods for simulating point processes and computing summary statistics for unmarked and multitype point patterns. The paper includes examples and demonstrations of spatstat's capabilities, emphasizing its flexibility and robustness in handling complex spatial data.