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 |
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