spBayes

spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models. Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude–longitude, Easting–Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.


References in zbMATH (referenced in 277 articles , 1 standard article )

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  1. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Morales, Javier: Multivariate bioclimatic indices modelling: a coregionalised approach (2019)
  2. Branson, Zach; Rischard, Maxime; Bornn, Luke; Miratrix, Luke W.: A nonparametric Bayesian methodology for regression discontinuity designs (2019)
  3. Cassese, Alberto; Zhu, Weixuan; Guindani, Michele; Vannucci, Marina: A Bayesian nonparametric spiked process prior for dynamic model selection (2019)
  4. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  5. Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard: A unified view on Bayesian varying coefficient models (2019)
  6. Gouriéroux, C.; Monfort, A.; Zakoïan, J.-M.: Consistent pseudo-maximum likelihood estimators and groups of transformations (2019)
  7. Guhaniyogi, Rajarshi; Banerjee, Sudipto: Multivariate spatial meta kriging (2019)
  8. Heaton, Matthew J.; Datta, Abhirup; Finley, Andrew O.; Furrer, Reinhard; Guinness, Joseph; Guhaniyogi, Rajarshi; Gerber, Florian; Gramacy, Robert B.; Hammerling, Dorit; Katzfuss, Matthias; Lindgren, Finn; Nychka, Douglas W.; Sun, Furong; Zammit-Mangion, Andrew: A case study competition among methods for analyzing large spatial data (2019)
  9. Huang, Yen-Ning; Reich, Brian J.; Fuentes, Montserrat; Sankarasubramanian, A.: Complete spatial model calibration (2019)
  10. Hwang, Youngdeok; Kim, Hang J.; Chang, Won; Yeo, Kyongmin; Kim, Yongku: Bayesian pollution source identification via an inverse physics model (2019)
  11. Johnson, Margaret; Caragea, Petruţa C.; Meiring, Wendy; Jeganathan, C.; Atkinson, Peter M.: Bayesian dynamic linear models for estimation of phenological events from remote sensing data (2019)
  12. Keefe, Matthew J.; Ferreira, Marco A. R.; Franck, Christopher T.: Objective Bayesian analysis for Gaussian hierarchical models with intrinsic conditional autoregressive priors (2019)
  13. Lagos-Álvarez, Bernardo; Padilla, Leonardo; Mateu, Jorge; Ferreira, Guillermo: A Kalman filter method for estimation and prediction of space-time data with an autoregressive structure (2019)
  14. Liang, Waley W. J.; Lee, Herbert K. H.: Bayesian nonstationary Gaussian process models via treed process convolutions (2019)
  15. Li, Linyuan; Lu, Kewei; Xiao, Yimin: Wavelet thresholding in fixed design regression for Gaussian random fields (2019)
  16. Li, Miaoqi; Kang, Emily L.: Randomized algorithms of maximum likelihood estimation with spatial autoregressive models for large-scale networks (2019)
  17. Li, Weidong; Zhang, Chuanrong: Markov chain random fields in the perspective of spatial Bayesian networks and optimal neighborhoods for simulation of categorical fields (2019)
  18. Mastrantonio, Gianluca; Lasinio, Giovanna Jona; Pollice, Alessio; Capotorti, Giulia; Teodonio, Lorenzo; Genova, Giulio; Blasi, Carlo: A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles (2019)
  19. Motarjem, K.; Mohammadzadeh, M.; Abyar, A.: Bayesian analysis of spatial survival model with non-Gaussian random effect (2019)
  20. Neelon, Brian: Bayesian zero-inflated negative binomial regression based on Pólya-gamma mixtures (2019)

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