ldmppr: Location Dependent Marked Point Processes in R

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.

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Publication Details

Journal
The R Journal
Published
2026-09-30
DOI
https://doi.org/10.32614/rj-2026-047
Primary Topic
Point processes and geometric inequalities
Type
article
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article

ldmppr: Location Dependent Marked Point Processes in R

Lane Drew, Andee Kaplan
The R Journal
Point processes and geometric inequalities
article

ldmppr: Location Dependent Marked Point Processes in R

Lane Drew, Andee Kaplan
article en

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.

The R JournalVol. 18(3)
Colorado State University (US)
Life in Land
Openalex Percentile: Top 56%
Point processes and geometric inequalities
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ldmppr: Location Dependent Marked Point Processes in R — Lane Drew, Andee Kaplan · The R Journal (2026) | TGRS Research Map | TGRS