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 Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  2. Bakar, K. Shuvo: Interpolation of daily rainfall data using censored Bayesian spatially varying model (2020)
  3. Huang, Danyang; Wang, Feifei; Zhu, Xuening; Wang, Hansheng: Two-mode network autoregressive model for large-scale networks (2020)
  4. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  5. Sofro, A’yunin; Shi, Jian Qing; Cao, Chunzheng: Regression analysis for multivariate process data of counts using convolved Gaussian processes (2020)
  6. Torabi, Mahmoud; Jiang, Jiming: Estimation of mean squared prediction error of empirically spatial predictor of small area means under a linear mixed model (2020)
  7. Wang, Jiangyan; Cao, Guanqun; Wang, Li; Yang, Lijian: Simultaneous confidence band for stationary covariance function of dense functional data (2020)
  8. Wehrhahn, Claudia; Leonard, Samuel; Rodriguez, Abel; Xifara, Tatiana: A Bayesian approach to disease clustering using restricted Chinese restaurant processes (2020)
  9. Zhu, Xuening; Huang, Danyang; Pan, Rui; Wang, Hansheng: Multivariate spatial autoregressive model for large scale social networks (2020)
  10. Balocchi, Cecilia; Jensen, Shane T.: Spatial modeling of trends in crime over time in Philadelphia (2019)
  11. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Morales, Javier: Multivariate bioclimatic indices modelling: a coregionalised approach (2019)
  12. Berchuck, Samuel I.; Mwanza, Jean-Claude; Warren, Joshua L.: Diagnosing glaucoma progression with visual field data using a spatiotemporal boundary detection method (2019)
  13. Branson, Zach; Rischard, Maxime; Bornn, Luke; Miratrix, Luke W.: A nonparametric Bayesian methodology for regression discontinuity designs (2019)
  14. Cassese, Alberto; Zhu, Weixuan; Guindani, Michele; Vannucci, Marina: A Bayesian nonparametric spiked process prior for dynamic model selection (2019)
  15. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  16. Datta, Abhirup; Banerjee, Sudipto; Hodges, James S.; Gao, Leiwen: Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models (2019)
  17. Davies, Tilman M.; Schofield, Matthew R.; Cornwall, Jon; Sheard, Philip W.: Modelling multilevel spatial behaviour in binary-mark muscle fibre configurations (2019)
  18. Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard: A unified view on Bayesian varying coefficient models (2019)
  19. Gouriéroux, C.; Monfort, A.; Zakoïan, J.-M.: Consistent pseudo-maximum likelihood estimators and groups of transformations (2019)
  20. Guhaniyogi, Rajarshi; Banerjee, Sudipto: Multivariate spatial meta kriging (2019)

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