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Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields

Citations

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Cited by:

  1. Anja Zgodic & Ray Bai & Jiajia Zhang & Yuan Wang & Christopher Rorden & Alexander C. McLain, 2026. "Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression," Computational Statistics, Springer, vol. 41(1), pages 1-23, January.
  2. Aaron Osgood‐Zimmerman & Jon Wakefield, 2023. "A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling," International Statistical Review, International Statistical Institute, vol. 91(2), pages 318-342, August.
  3. Azaïs, Jean-Marc & Chassan, Malika, 2020. "Discretization error for the maximum of a Gaussian field," Stochastic Processes and their Applications, Elsevier, vol. 130(2), pages 545-559.
  4. Christoph Muehlmann & Claudia Cappello & Sandra De Iaco & Klaus Nordhausen, 2025. "Anisotropic local covariance matrices for spatial blind source separation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(4), pages 753-770, December.
  5. Moreva, Olga & Schlather, Martin, 2023. "Bivariate covariance functions of Pólya type," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
  6. Nathan A. Ryder & Joshua P. Keller, 2023. "Spatiotemporal Exposure Prediction with Penalized Regression," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 260-278, June.
  7. Brown, Patrick E., 2015. "Model-Based Geostatistics the Easy Way," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i12).
  8. Isabelle Grenier & Bruno Sansó & Jessica L. Matthews, 2024. "Multivariate nearest‐neighbors Gaussian processes with random covariance matrices," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), May.
  9. Matthias Katzfuss, 2017. "A Multi-Resolution Approximation for Massive Spatial Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 201-214, January.
  10. Noel Cressie & Andrew Zammit-Mangion, 2016. "Multivariate spatial covariance models: a conditional approach," Biometrika, Biometrika Trust, vol. 103(4), pages 915-935.
  11. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2015. "Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i07).
  12. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
  13. Padoan, Simone A. & Bevilacqua, Moreno, 2015. "Analysis of Random Fields Using CompRandFld," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i09).
  14. Sigrist, Fabio & Künsch, Hans R. & Stahel, Werner A., 2015. "spate: An R Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i14).
  15. D'Angelo, Nicoletta & Adelfio, Giada & Mateu, Jorge, 2023. "Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
  16. Ghorbani, Mohammad & Vafaei, Nafiseh & Dvořák, Jiří & Myllymäki, Mari, 2021. "Testing the first-order separability hypothesis for spatio-temporal point patterns," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
  17. Joaquim Soler-Sagarra & Vivien Hakoun & Marco Dentz & Jesus Carrera, 2021. "The Multi-Advective Water Mixing Approach for Transport through Heterogeneous Media," Energies, MDPI, vol. 14(20), pages 1-18, October.
  18. Dolder, Paul J. & Minto, Cóilín & Guarini, Jean-Marc & Poos, Jan Jaap, 2020. "Highly resolved spatiotemporal simulations for exploring mixed fishery dynamics," Ecological Modelling, Elsevier, vol. 424(C).
  19. Ninna Vihrs & Jesper Møller & Alan E. Gelfand, 2022. "Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 185-210, March.
  20. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
  21. Thurner, Stephanie D & Converse, Sarah J & Branch, Trevor A, 2021. "Modeling opportunistic exploitation: increased extinction risk when targeting more than one species," Ecological Modelling, Elsevier, vol. 454(C).
  22. Cécile Hardouin & Noel Cressie, 2018. "Two-scale spatial models for binary data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 1-24, March.
  23. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).
  24. Matteo Tomasetto & Eleonora Arnone & Laura M. Sangalli, 2024. "Modeling Anisotropy and Non‐Stationarity Through Physics‐Informed Spatial Regression," Environmetrics, John Wiley & Sons, Ltd., vol. 35(8), December.
  25. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  26. de Iaco, Sandra, 2017. "The cgeostat Software for Analyzing Complex-Valued Random Fields," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i05).
  27. Xavier Barber & David Conesa & Antonio López-Quílez & Joaquín Martínez-Minaya & Iosu Paradinas & Maria Grazia Pennino, 2021. "Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach," Mathematics, MDPI, vol. 9(4), pages 1-12, February.
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