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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "spFFBS" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'spFFBS: Spatiotemporal Propagation for Multivariate Bayesian Dynamic Learning'
version: 0.0-2
doi: 10.32614/CRAN.package.spFFBS
abstract: 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+) <https://doi.org/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.
authors:
- family-names: Presicce
  given-names: Luca
  email: l.presicce@campus.unimib.it
  orcid: https://orcid.org/0009-0005-7062-3523
repository: https://lucapresicce.r-universe.dev
commit: 3cd4e4d22f95c389794032004af2892e43fd4913
url: https://lucapresicce.github.io/spFFBS/
date-released: '2026-04-22'
contact:
- family-names: Presicce
  given-names: Luca
  email: l.presicce@campus.unimib.it
  orcid: https://orcid.org/0009-0005-7062-3523