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.

References in zbMATH (referenced in 340 articles , 1 standard article )

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  1. Bansal, Prateek; Krueger, Rico; Graham, Daniel J.: Fast Bayesian estimation of spatial count data models (2021)
  2. 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)
  3. Chen, Wanfang; Castruccio, Stefano; Genton, Marc G.: Assessing the risk of disruption of wind turbine operations in Saudi Arabia using Bayesian spatial extremes (2021)
  4. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  5. Gerber, Florian; Nychka, Douglas W.: Parallel cross-validation: a scalable fitting method for Gaussian process models (2021)
  6. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  7. Katzfuss, Matthias; Guinness, Joseph: A general framework for Vecchia approximations of Gaussian processes (2021)
  8. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  9. Rischard, Maxime; Branson, Zach; Miratrix, Luke; Bornn, Luke: Do school districts affect NYC house prices? Identifying border differences using a Bayesian nonparametric approach to geographic regression discontinuity designs (2021)
  10. Song, Yunquan; Liang, Xijun; Zhu, Yanji; Lin, Lu: Robust variable selection with exponential squared loss for the spatial autoregressive model (2021)
  11. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  12. Bakar, K. Shuvo: Interpolation of daily rainfall data using censored Bayesian spatially varying model (2020)
  13. 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)
  14. Baptista, Helena; Congdon, Peter; Mendes, Jorge M.; Rodrigues, Ana M.; Canhão, Helena; Dias, Sara S.: Disease mapping models for data with weak spatial dependence or spatial discontinuities (2020)
  15. 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)
  16. Heaton, Matthew J.; Berrett, Candace; Pugh, Sierra; Evans, Amber; Sloan, Chantel: Modeling bronchiolitis incidence proportions in the presence of spatio-temporal uncertainty (2020)
  17. Horváth, Lajos; Kokoszka, Piotr; Wang, Shixuan: Testing normality of data on a multivariate grid (2020)
  18. Huang, Danyang; Wang, Feifei; Zhu, Xuening; Wang, Hansheng: Two-mode network autoregressive model for large-scale networks (2020)
  19. Lai, Chi-Wei; Huang, Hsin-Cheng: Intensity estimation of spatial point processes based on area-aggregated data (2020)
  20. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)

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