BSPBSS: Bayesian Spatial Blind Source Separation
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.
| Version: |
1.0.6 |
| Depends: |
R (≥ 3.4.0), movMF |
| Imports: |
rstiefel, Rcpp, ica, glmnet, gplots, BayesGPfit, svd, neurobase, oro.nifti, gridExtra, ggplot2, gtools |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown |
| Published: |
2025-10-16 |
| DOI: |
10.32614/CRAN.package.BSPBSS |
| Author: |
Ben Wu [aut, cre],
Ying Guo [aut],
Jian Kang [aut] |
| Maintainer: |
Ben Wu <wuben at ruc.edu.cn> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU make |
| Materials: |
README |
| CRAN checks: |
BSPBSS results |
Documentation:
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