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. Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree, Daniel J. Isaak, Kerrie Mengersen, Erin E. Peterson: SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks (2022) arXiv
  2. Guinness, Joseph: Nonparametric spectral methods for multivariate spatial and spatial-temporal data (2022)
  3. Li, Yehua; Qiu, Yumou; Xu, Yuhang: From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas (2022)
  4. Bansal, Prateek; Krueger, Rico; Graham, Daniel J.: Fast Bayesian estimation of spatial count data models (2021)
  5. Bivand, Roger S.; Gómez-Rubio, Virgilio: Spatial survival modelling of business re-opening after Katrina: survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months (2021)
  6. Chen, Wanfang; Castruccio, Stefano; Genton, Marc G.: Assessing the risk of disruption of wind turbine operations in Saudi Arabia using Bayesian spatial extremes (2021)
  7. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  8. Ferreira, Marco A. R.; Porter, Erica M.; Franck, Christopher T.: Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects (2021)
  9. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  10. Gerber, Florian; Nychka, Douglas W.: Parallel cross-validation: a scalable fitting method for Gaussian process models (2021)
  11. Ghorbani, Mohammad; Cronie, Ottmar; Mateu, Jorge; Yu, Jun: Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data (2021)
  12. Hepler, Staci A.; Waller, Lance A.; Kline, David M.: A multivariate spatiotemporal change-point model of opioid overdose deaths in Ohio (2021)
  13. Hrafnkelsson, Birgir; Siegert, Stefan; Huser, Raphaël; Bakka, Haakon; Jóhannesson, Árni V.: Max-and-smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models (2021)
  14. Huang, Danyang; Zhu, Xuening; Li, Runze; Wang, Hansheng: Feature screening for network autoregression model (2021)
  15. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  16. Jeffrey W. Doser, Andrew O. Finley, Marc Kery, Elise F. Zipkin: spOccupancy: An R package for single species, multispecies, and integrated spatial occupancy models (2021) arXiv
  17. Katzfuss, Matthias; Guinness, Joseph: A general framework for Vecchia approximations of Gaussian processes (2021)
  18. Mukherjee, Somabha; Son, Jaesung; Bhattacharya, Bhaswar B.: Fluctuations of the magnetization in the (p)-spin Curie-Weiss model (2021)
  19. Muré, Joseph: Propriety of the reference posterior distribution in Gaussian process modeling (2021)
  20. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)

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