Package: spFFBS
Title: Spatiotemporal Propagation for Multivariate Bayesian Dynamic
        Learning
Version: 1.0-1
Authors@R: 
    person("Luca", "Presicce", , "l.presicce@campus.unimib.it", role = c("aut", "cre"),
           comment = c(ORCID = "0009-0005-7062-3523"))
Maintainer: Luca Presicce <l.presicce@campus.unimib.it>
Description: Implementation of the Forward Filtering Backward Sampling
        (FFBS) algorithm with Dynamic Bayesian Predictive Stacking
        (DYNBPS) integration for multivariate spatiotemporal models, as
        introduced in "Adaptive Markovian Spatiotemporal Transfer
        Learning in Multivariate Bayesian Modeling" (Presicce and
        Banerjee, 2026+) <doi:10.48550/arXiv.2602.08544>. This
        methodology enables efficient Bayesian multivariate
        spatiotemporal modeling, utilizing dynamic predictive stacking
        to improve inference across multivariate time series of spatial
        datasets. The core functions leverage 'C++' for
        high-performance computation, making the framework well-suited
        for large-scale spatiotemporal data analysis in parallel
        computing environments.
LinkingTo: Rcpp, RcppArmadillo
Imports: spBPS, Rcpp (>= 1.1.1), abind
Suggests: doParallel, mniw, MBA, ggplot2, patchwork, reshape2, knitr,
        rmarkdown
License: GPL (>= 3)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
VignetteBuilder: knitr
URL: https://lucapresicce.github.io/spFFBS/
Config/pak/sysreqs: cmake libgmp3-dev make pkg-config libclang-dev
Repository: https://lucapresicce.r-universe.dev
Date/Publication: 2026-07-15 17:26:55 UTC
RemoteUrl: https://github.com/lucapresicce/spffbs
RemoteRef: HEAD
RemoteSha: d658b217a3a9308e8491a862783d8b1c329c9ab3
NeedsCompilation: yes
Packaged: 2026-10-19 11:21:05 UTC; root
Author: Luca Presicce [aut, cre] (ORCID:
    <https://orcid.org/0009-0005-7062-3523>)