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. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  2. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  3. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  4. Bakar, K. Shuvo: Interpolation of daily rainfall data using censored Bayesian spatially varying model (2020)
  5. Banerjee, Trambak; Mukherjee, Gourab; Dutta, Shantanu; Ghosh, Pulak: A large-scale constrained joint modeling approach for predicting user activity, engagement, and churn with application to freemium mobile games (2020)
  6. Bradley, Jonathan R.; Holan, Scott H.; Wikle, Christopher K.: Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family (2020)
  7. Heaton, Matthew J.; Berrett, Candace; Pugh, Sierra; Evans, Amber; Sloan, Chantel: Modeling bronchiolitis incidence proportions in the presence of spatio-temporal uncertainty (2020)
  8. Horváth, Lajos; Kokoszka, Piotr; Wang, Shixuan: Testing normality of data on a multivariate grid (2020)
  9. Huang, Danyang; Wang, Feifei; Zhu, Xuening; Wang, Hansheng: Two-mode network autoregressive model for large-scale networks (2020)
  10. Lai, Chi-Wei; Huang, Hsin-Cheng: Intensity estimation of spatial point processes based on area-aggregated data (2020)
  11. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  12. Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
  13. Sofro, A’yunin; Shi, Jian Qing; Cao, Chunzheng: Regression analysis for multivariate process data of counts using convolved Gaussian processes (2020)
  14. Sugasawa, Shonosuke: Small area estimation of general parameters: Bayesian transformed spatial prediction approach (2020)
  15. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  16. Torabi, Mahmoud; Jiang, Jiming: Estimation of mean squared prediction error of empirically spatial predictor of small area means under a linear mixed model (2020)
  17. Vanhatalo, Jarno; Hartmann, Marcelo; Veneranta, Lari: Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data (2020)
  18. Wang, Jiangyan; Cao, Guanqun; Wang, Li; Yang, Lijian: Simultaneous confidence band for stationary covariance function of dense functional data (2020)
  19. Wang, Wenjia; Tuo, Rui; Jeff Wu, C. F.: On prediction properties of kriging: uniform error bounds and robustness (2020)
  20. Warren, Joshua L.: A nonstationary spatial covariance model for processes driven by point sources (2020)

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