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spStack
View on CRAN: Click
here
Download and install spStack package within the R console
Install from CRAN:
install.packages("spStack")
Install from Github:
library("remotes")
install_github("cran/spStack") Install by package version:
library("remotes")
install_version("spStack", "1.1.2") Attach the package and use:
library("spStack")
Maintained by
Soumyakanti Pan
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[Scholar Profile | Author Map]
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First Published: 2024-10-03
Latest Update: 2025-07-14
Description:
Fits Bayesian hierarchical spatial process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon some candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2024) <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2024) <doi:10.48550/arXiv.2406.04655> for details.
How to cite:
Soumyakanti Pan (2024). spStack: Bayesian Geostatistics Using Predictive Stacking. R package version 1.1.2, https://cran.r-project.org/web/packages/spStack. Accessed 28 Jul. 2026.
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