A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA). This paper develops a methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. This enables us to use integrated nested Laplace approximation and to considerably speed up the inferential task. In addition, methods for model comparison and model assessment facilitate the modelling process. The performance of the approach is assessed in a simulation study. To demonstrate the versatility of the approach, models are fitted to two rather different examples, a large rainforest data set with covariates and a point pattern with multiple marks.

References in zbMATH (referenced in 49 articles , 1 standard article )

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  1. Kim, Hyotae; Kottas, Athanasios: Erlang mixture modeling for Poisson process intensities (2022)
  2. Sahu, Sujit K.: Bayesian modeling of spatio-temporal data with R (2022)
  3. Bivand, Roger S.; Gómez-Rubio, Virgilio: Spatial survival modelling of business re-opening after Katrina: survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months (2021)
  4. Castro-Camilo, Daniela; Mhalla, Linda; Opitz, Thomas: Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures (2021)
  5. Dawkins, Laura C.; Williamson, Daniel B.; Mengersen, Kerrie L.; Morawska, Lidia; Jayaratne, Rohan; Shaddick, Gavin: Where is the clean air? A Bayesian decision framework for personalised cyclist route selection using R-INLA (2021)
  6. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  7. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  8. Lawson, Andrew B.: Using R for Bayesian spatial and spatio-temporal health modeling (2021)
  9. Umlauf, N., Klein, N., Simon, T., Zeileis, A: bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond) (2021) not zbMATH
  10. van Niekerk, Janet; Bakka, Haakon; Rue, Håvard: Competing risks joint models using R-INLA (2021)
  11. van Niekerk, Janet; Rue, Håvard: Skewed probit regression -- identifiability, contraction and reformulation (2021)
  12. Van Niekerk, J., Bakka, H., Rue, H., Schenk, O. : New Frontiers in Bayesian Modeling Using the INLA Package in R (2021) not zbMATH
  13. Yaqiong Wang, Francesco Finazzi, Alessandro Fasso: D-STEM v2: A Software for Modeling Functional Spatio-Temporal Data (2021) not zbMATH
  14. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  15. Anita K. Nandi, Tim C. D. Lucas, Rohan Arambepola, Peter Gething, Daniel J. Weiss: disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling (2020) arXiv
  16. Borrajo, M. I.; González-Manteiga, W.; Martínez-Miranda, M. D.: Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates (2020)
  17. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  18. Lenzi, Amanda; Genton, Marc G.: Spatiotemporal probabilistic wind vector forecasting over Saudi Arabia (2020)
  19. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  20. Finley, Andrew O.; Datta, Abhirup; Cook, Bruce D.; Morton, Douglas C.; Andersen, Hans E.; Banerjee, Sudipto: Efficient algorithms for Bayesian nearest neighbor Gaussian processes (2019)

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