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license: GPL-3.0-only
title: 'spStack: Bayesian Geostatistics Using Predictive Stacking'
version: 1.0.1
doi: 10.32614/CRAN.package.spStack
abstract: 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) <https://doi.org/10.48550/arXiv.2304.12414>,
  and, Pan, Zhang, Bradley, and Banerjee (2024) <https://doi.org/10.48550/arXiv.2406.04655>
  for details.
authors:
- family-names: Pan
  given-names: Soumyakanti
  email: span18@ucla.edu
  orcid: https://orcid.org/0009-0005-9889-7112
- family-names: Banerjee
  given-names: Sudipto
  email: sudipto@ucla.edu
repository: https://CRAN.R-project.org/package=spStack
repository-code: https://github.com/SPan-18/spStack-dev
url: https://span-18.github.io/spStack-dev/
date-released: '2024-10-08'
contact:
- family-names: Pan
  given-names: Soumyakanti
  email: span18@ucla.edu
  orcid: https://orcid.org/0009-0005-9889-7112