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 253 articles , 1 standard article )

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  1. Cassese, Alberto; Zhu, Weixuan; Guindani, Michele; Vannucci, Marina: A Bayesian nonparametric spiked process prior for dynamic model selection (2019)
  2. Guhaniyogi, Rajarshi; Banerjee, Sudipto: Multivariate spatial meta kriging (2019)
  3. Hwang, Youngdeok; Kim, Hang J.; Chang, Won; Yeo, Kyongmin; Kim, Yongku: Bayesian pollution source identification via an inverse physics model (2019)
  4. 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)
  5. Keefe, Matthew J.; Ferreira, Marco A. R.; Franck, Christopher T.: Objective Bayesian analysis for Gaussian hierarchical models with intrinsic conditional autoregressive priors (2019)
  6. Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun: ExaGeoStatR: A Package for Large-Scale Geostatistics in R (2019) arXiv
  7. Song, Joon Jin; Mallick, Bani: Hierarchical Bayesian models for predicting spatially correlated curves (2019)
  8. Acosta, Jonathan; Vallejos, Ronny: Effective sample size for spatial regression models (2018)
  9. Barlow, Anna Maria; Rohrbeck, Christian; Sharkey, Paul; Shooter, Rob; Simpson, Emma S.: A Bayesian spatio-temporal model for precipitation extremes -- STOR team contribution to the EVA2017 challenge (2018)
  10. Bezener, Martin; Hughes, John; Jones, Galin: Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion) (2018)
  11. Chandra, Hukum; Salvati, Nicola: Small area estimation of proportions under a spatial dependent aggregated level random effects model (2018)
  12. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  13. Gonçalves, Flávio B.; Gamerman, Dani: Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2018)
  14. Hensman, James; Durrande, Nicolas; Solin, Arno: Variational Fourier features for Gaussian processes (2018)
  15. Kottas, Athanasios: Discussion of paper “Nonparametric Bayesian inference in applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page (2018)
  16. Liu, Xiao; Gopal, Vikneswaran; Kalagnanam, Jayant: A spatio-temporal modeling framework for weather radar image data in tropical southeast Asia (2018)
  17. Liu, Ying; Flegal, James M.: Weighted batch means estimators in Markov chain Monte Carlo (2018)
  18. Mak, Simon; Sung, Chih-Li; Wang, Xingjian; Yeh, Shiang-Ting; Chang, Yu-Hung; Joseph, V. Roshan; Yang, Vigor; Wu, C. F. Jeff: An efficient surrogate model for emulation and physics extraction of large eddy simulations (2018)
  19. Müeller, Peter; Quintana, Fernando A.; Page, Garritt: Nonparametric Bayesian inference in applications (2018)
  20. Opitz, Thomas; Huser, Raphaël; Bakka, Haakon; Rue, Håvard: INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (2018)

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