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 et al. (2022+) "Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process" <doi:10.1080/01621459.2022.2123336>.

Version: 1.0.5
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: 2022-11-25
DOI: 10.32614/CRAN.package.BSPBSS
Author: Ben Wu [aut, cre], Ying Guo [aut], Jian Kang [aut]
Maintainer: Ben Wu <wuben at>
License: GPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README
CRAN checks: BSPBSS results


Reference manual: BSPBSS.pdf
Vignettes: BSPBSS-vignette


Package source: BSPBSS_1.0.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BSPBSS_1.0.5.tgz, r-oldrel (arm64): BSPBSS_1.0.5.tgz, r-release (x86_64): BSPBSS_1.0.5.tgz, r-oldrel (x86_64): BSPBSS_1.0.5.tgz
Old sources: BSPBSS archive


Please use the canonical form to link to this page.