Bayesian Spatial Blind Source Separation
Overview
Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-05-02
- NOTE11 OK, 3 NOTE2026-04-22
- ERROR10 OK, 3 NOTE, 1 ERROR2026-04-18
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- NOTE7 OK, 7 NOTE2026-04-08
- ERROR6 OK, 7 NOTE, 1 ERROR2026-04-07
- NOTE11 OK, 3 NOTE2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 71%
- Documented parameters
- 94%
- Return-value docs
- 100%
- Help pages citing references
- 0%
- BSPBSS-vignette HTML
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Dependencies
Code & Tests
People & History
Package metadata
- First published
- 2022-09-02
- Total releases
- 5 / 4 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.4.0
- Download size
- 356 KB
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