spFFBS
1.0-1Spatiotemporal 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.
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Health
- OK2026-07-1713 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-1112 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-259 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-234 OK · 1 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Documentation
- Examples that run
- 0%
- Documented parameters
- 94%
- Return-value docs
- 100%
- References docs
- 0%
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Checks run against github.com/lucapresicce/spffbs on 2026-07-30.
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2 releases. Pick two to compare their code metrics. R releases are shown for context.
- 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
- not tracked yet
- Installed size
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- With dependencies
- not tracked yet