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spStack  

Bayesian Geostatistics Using Predictive Stacking
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", "1.0.1")



Attach the package and use:
library("spStack")
Maintained by
Soumyakanti Pan
[Scholar Profile | Author Map]
First Published: 2024-10-03
Latest Update: 2024-10-08
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 1.0.1, https://cran.r-project.org/web/packages/spStack. Accessed 17 Feb. 2025.
Previous versions and publish date:
1.0.0 (2024-10-03 20:50)
Other packages that cited spStack R package
View spStack citation profile
Other R packages that spStack depends, imports, suggests or enhances
Complete documentation for spStack
Functions, R codes and Examples using the spStack R package
Full spStack package functions and examples
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