Spatiotemporal Propagation for Multivariate Bayesian Dynamic Learning
Overview
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.
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-17
- ERROR12 OK, 1 ERROR2026-06-11
- OK13 OK2026-05-02
Show 2 earlier changesHide 2 earlier changes
- NOTE9 OK, 1 NOTE2026-04-25
- ERROR4 OK, 1 NOTE, 1 ERROR2026-04-23
Documentation
- Help pages
- 4 (2 internal)includes a package overview page
- Examples that run
- 0%1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 50%
- Documented parameters
- 94%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Repository
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Checks run against github.com/lucapresicce/spffbs on 2026-10-22.
GitHub Pages site last deployed 2026-07-15 · The repository has a site deploy workflow
pkgdown site last built 2026-07-15
Dependencies
Code & Tests
People & History
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- 1.0-1Latest
- RR 4.6.0 released · 2026-04-24
- 0.0-22026-04-22
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-04-22
- Total releases
- 2 / 1 yrs
- License
- GPL (>= 3) OSI
- Download size
- 352 KB
Cite
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citation("spFFBS")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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