🤖 AI Summary
This study addresses a key limitation of traditional marked point process models, which commonly assume independence between marks and locations—an assumption often violated in real-world applications such as forestry. To overcome this constraint, the authors propose a unified framework that, for the first time, enables comprehensive modeling, parameter estimation, simulation, and visualization of location-dependent marked point processes within the R programming environment. Grounded in spatial point process theory, the approach integrates statistical modeling with computational tools to support fitting to empirical data, model diagnostics, and generation of realistic spatial patterns. By relaxing the restrictive independence assumption, this work provides a practical and extensible analytical toolkit for researchers in ecology and related fields.
📝 Abstract
In this article, we present $\textbf{ldmppr}$, an R package for estimating, evaluating, simulating from, and visualizing location-dependent marked spatial point processes. To date, it has commonly been assumed that the marks associated with a point process are independent of the locations. However, when dealing with many point processes, such as those arising in forestry applications, the independence assumption proves unreasonable. We introduce a practical framework for generating marked point processes with dependence between the marks and locations. We provide a brief discussion of the theory underpinning our modeling approach and outline the use of the package in a typical scenario involving real data. We highlight the functionality of the package for both generating from and assessing the goodness-of-fit of a given model, enabling users to generate realistic point patterns given a reference pattern or parameter values of interest.