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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  2. Dey, Soumen; Delampady, Mohan; Parameshwaran, Ravishankar; Kumar, N.Samba; Srivathsa, Arjun; Karanth, K.Ullas: Bayesian methods for estimating animal abundance at large spatial scales using data from multiple sources (2017)
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  4. Soubeyrand, Samuel: Book review of: S. Banerjee et al., Hierarchical modeling and analysis for spatial data. 2nd ed. (2017)
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  6. Alexeeff, Stacey E.; Pfister, Gabriele G.; Nychka, Doug: A Bayesian model for quantifying the change in mortality associated with future ozone exposures under climate change (2016)
  7. Aregay, Mehreteab; Lawson, Andrew B.; Faes, Christel; Kirby, Russell S.; Carroll, Rachel; Watjou, Kevin: Spatial mixture multiscale modeling for aggregated health data (2016)
  8. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: A comparison of spatial predictors when datasets could be very large (2016)
  9. Fox, Colin; Norton, Richard A.: Fast sampling in a linear-Gaussian inverse problem (2016)
  10. Giraud, Christophe; Calenge, Clément; Coron, Camille; Julliard, Romain: Capitalizing on opportunistic data for monitoring relative abundances of species (2016)
  11. Li, Linyuan: Nonparametric regression on random fields with random design using wavelet method (2016)
  12. Liu, Xiao; Yeo, Kyongmin; Hwang, Youngdeok; Singh, Jitendra; Kalagnanam, Jayant: A statistical modeling approach for air quality data based on physical dispersion processes and its application to ozone modeling (2016)
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  14. Mastrantonio, Gianluca; Jona Lasinio, Giovanna; Gelfand, Alan E.: Spatio-temporal circular models with non-separable covariance structure (2016)
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  17. Wang, Xiaojing; Berger, James O.: Estimating shape constrained functions using Gaussian processes (2016)
  18. Zhang, Linlin; Guindani, Michele; Versace, Francesco; Engelmann, Jeffrey M.; Vannucci, Marina: A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data (2016)
  19. Banerjee, Sudipto; Carlin, Bradley P.; Gelfand, Alan E.: Hierarchical modeling and analysis for spatial data (2015)
  20. Bianchini, Ilaria; Argiento, Raffaele; Auricchio, Ferdinando; Lanzarone, Ettore: Efficient uncertainty quantification in stochastic finite element analysis based on functional principal components (2015)

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