Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 52

The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.

Hybrids of Gibbs Point Process Models and Their Implementation

Baddeley, Adrian, Turner, Rolf, Mateu, Jorge, Bevan, Andrew·2013·Journal of Statistical Software
DOI 10.18637/jss.v055.i11
Open this paper in CoChat — free account

Abstract

We describe a simple way to construct new statistical models for spatial point pattern data. Taking two or more existing models (finite Gibbs spatial point processes) we multiply the probability densities together and renormalise to obtain a new probability density. We call the resulting model a hybrid. We discuss stochastic properties of hybrids, their statistical implications, statistical inference, computational strategies and software implementation in the R package spatstat. Hybrids are particularly useful for constructing models which exhibit interaction at different spatial scales. The methods are demonstrated on a real data set on human social interaction. Software and data are provided.

Overview

Integrity signals

Registry

Crossref · 10.18637/jss.v055.i11

Cites

72 of 72 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 72 references

Cited by

See all 52

Metadata

Authors
Baddeley, A., Turner, R., Mateu, J., & Bevan, A.
Venue
Journal of Statistical Software
Publisher
Foundation for Open Access Statistic
Published
2013
Volume
55
Issue
11
ISSN
1548-7660
DOI
10.18637/jss.v055.i11
Cited by
52
References
72
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Hybrids of Gibbs Point Process Models and Their Implementation. https://app.cochat.ai/papers/10.18637/jss.v055.i11
The paper
Baddeley, A., Turner, R., Mateu, J., & Bevan, A. (2013). Hybrids of Gibbs Point Process Models and Their Implementation. Journal of Statistical Software. https://doi.org/10.18637/jss.v055.i11

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.