Package: spStack
Type: Package
Version: 1.0.1
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 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.
Imports: CVXR, future, future.apply, ggplot2, MBA, rstudioapi
NeedsCompilation: yes
License: GPL-3
Encoding: UTF-8
LazyData: true
Suggests: ggpubr, knitr, rmarkdown, spelling, testthat (>= 3.0.0)
Config/testthat/edition: 3
RoxygenNote: 7.3.2
Depends: R (>= 2.10)
VignetteBuilder: knitr
URL: https://github.com/SPan-18/spStack-dev,
        https://span-18.github.io/spStack-dev/
BugReports: https://github.com/SPan-18/spStack-dev/issues
Language: en-US
Packaged: 2024-10-08 00:02:26 UTC; soumyakantipan
Author: Soumyakanti Pan [aut, cre] (<https://orcid.org/0009-0005-9889-7112>),
  Sudipto Banerjee [aut]
Maintainer: Soumyakanti Pan <span18@ucla.edu>
Repository: CRAN
Date/Publication: 2024-10-08 07:00:02 UTC
Built: R 4.3.3; aarch64-apple-darwin20; 2024-10-08 07:49:28 UTC; unix
Archs: spStack.so.dSYM
