Package: spStack
Type: Package
Version: 1.1.2
Title: Bayesian Geostatistics Using Predictive Stacking
Authors@R: c(
    person("Soumyakanti", "Pan", role = c("aut", "cre"),
    email = "span18@ucla.edu", comment = c(ORCID = "0009-0005-9889-7112")),
    person("Sudipto", "Banerjee", role = "aut", email = "sudipto@ucla.edu"))
Description: Fits Bayesian hierarchical spatial and spatial-temporal 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 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 (2025)
    <doi:10.1080/01621459.2025.2566449>, and, Pan, Zhang, Bradley, and Banerjee
    (2025) <doi:10.48550/arXiv.2406.04655> for details.
Imports: CVXR, future, future.apply, ggplot2, MBA, rstudioapi
NeedsCompilation: yes
License: GPL-3
Encoding: UTF-8
LazyData: true
Suggests: dplyr, ggpubr, knitr, patchwork, rmarkdown, spelling,
        testthat (>= 3.0.0), tidyr
Config/testthat/edition: 3
RoxygenNote: 7.3.2
Depends: R (>= 3.5)
VignetteBuilder: knitr
URL: https://span-18.github.io/spStack-dev/
BugReports: https://github.com/SPan-18/spStack-dev/issues
Language: en-US
Packaged: 2025-10-04 07:06:18 UTC; soumyakantipan
Author: Soumyakanti Pan [aut, cre] (ORCID:
    <https://orcid.org/0009-0005-9889-7112>),
  Sudipto Banerjee [aut]
Maintainer: Soumyakanti Pan <span18@ucla.edu>
Repository: CRAN
Date/Publication: 2025-10-04 07:30:02 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2025-12-24 03:51:33 UTC; windows
Archs: x64
