spBPS
2.0-1Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning
Overview
Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2025) doi:10.48550/arXiv.2410.09504. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.
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Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- WARNING2026-06-0812 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Show 2 earlier snapshots
- NOTE2026-03-2612 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-03-104 OK · 2 NOTE · 0 WARNING · 8 ERROR · 0 FAILURE
Documentation
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- Documented parameters
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- Return-value docs
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- References docs
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Checks run against github.com/lucapresicce/spbps on 2026-07-19.
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Code & Tests
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- 2.0-1Latest
- RR 4.6.0 released · 2026-04-24
- 1.0-12026-03-19 · diff ↗
- 0.0-42026-03-10
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2024-10-25
- Total releases
- 3 / 2 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 1.8.0
- Download size
- 122 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet