spBayes
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.
Keywords for this software
References in zbMATH (referenced in 325 articles , 1 standard article )
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Sorted by year (- Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
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- Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
- Sofro, A’yunin; Shi, Jian Qing; Cao, Chunzheng: Regression analysis for multivariate process data of counts using convolved Gaussian processes (2020)
- Sugasawa, Shonosuke: Small area estimation of general parameters: Bayesian transformed spatial prediction approach (2020)
- Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
- Torabi, Mahmoud; Jiang, Jiming: Estimation of mean squared prediction error of empirically spatial predictor of small area means under a linear mixed model (2020)
- Wang, Jiangyan; Cao, Guanqun; Wang, Li; Yang, Lijian: Simultaneous confidence band for stationary covariance function of dense functional data (2020)
- Wang, Wenjia; Tuo, Rui; Jeff Wu, C. F.: On prediction properties of kriging: uniform error bounds and robustness (2020)
- Warren, Joshua L.: A nonstationary spatial covariance model for processes driven by point sources (2020)
- Wehrhahn, Claudia; Leonard, Samuel; Rodriguez, Abel; Xifara, Tatiana: A Bayesian approach to disease clustering using restricted Chinese restaurant processes (2020)
- Yan, Yuan; Jeong, Jaehong; Genton, Marc G.: Multivariate transformed Gaussian processes (2020)
- Zhu, Xuening; Huang, Danyang; Pan, Rui; Wang, Hansheng: Multivariate spatial autoregressive model for large scale social networks (2020)
- Balocchi, Cecilia; Jensen, Shane T.: Spatial modeling of trends in crime over time in Philadelphia (2019)