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.

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  1. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv
  2. Dreassi, Emanuela; Rigo, Pietro: A note on compatibility of conditional autoregressive models (2017)
  3. Soubeyrand, Samuel: Book review of: S. Banerjee et al., Hierarchical modeling and analysis for spatial data. 2nd ed. (2017)
  4. Acosta, Jonathan; Osorio, Felipe; Vallejos, Ronny: Effective sample size for line transect sampling models with an application to marine macroalgae (2016)
  5. Fox, Colin; Norton, Richard A.: Fast sampling in a linear-Gaussian inverse problem (2016)
  6. Li, Linyuan: Nonparametric regression on random fields with random design using wavelet method (2016)
  7. Madrid, A.E.; Angulo, J.M.; Mateu, J.: Point pattern analysis of spatial deformation and blurring effects on exceedances (2016)
  8. Mastrantonio, Gianluca; Jona Lasinio, Giovanna; Gelfand, Alan E.: Spatio-temporal circular models with non-separable covariance structure (2016)
  9. Terres, Maria A.; Gelfand, Alan E.: Spatial process gradients and their use in sensitivity analysis for environmental processes (2016)
  10. Wang, Xiaojing; Berger, James O.: Estimating shape constrained functions using Gaussian processes (2016)
  11. Banerjee, Sudipto; Carlin, Bradley P.; Gelfand, Alan E.: Hierarchical modeling and analysis for spatial data (2015)
  12. Bianchini, Ilaria; Argiento, Raffaele; Auricchio, Ferdinando; Lanzarone, Ettore: Efficient uncertainty quantification in stochastic finite element analysis based on functional principal components (2015)
  13. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: Comparing and selecting spatial predictors using local criteria (2015)
  14. Kaganovsky, Yan; Han, Shaobo; Degirmenci, Soysal; Politte, David G.; Brady, David J.; O’Sullivan, Joseph A.; Carin, Lawrence: Alternating minimization algorithm with automatic relevance determination for transmission tomography under Poisson noise (2015)
  15. Konomi, Bledar; Karagiannis, Georgios; Lin, Guang: On the Bayesian treed multivariate Gaussian process with linear model of coregionalization (2015)
  16. Li, Linyuan: Nonparametric adaptive density estimation on random fields using wavelet method (2015)
  17. Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria: Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan (2015)
  18. Ugarte, Maria Dolores: Book review of: S. Banerjee et al., Hierarchical modeling and analysis for spatial data. 2nd ed. (2015)
  19. Ver Hoef, Jay M.; Jansen, John K.: Estimating abundance from counts in large data sets of irregularly spaced plots using spatial basis functions (2015)
  20. Bliznyuk, Nikolay; Paciorek, Christopher J.; Schwartz, Joel; Coull, Brent: Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources (2014)

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