Bayesian Spatial Blind Source Separation R package BSPBSS version 1.0.6 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. Software 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 Ben Wu <wuben@ruc.edu.cn> Comprehensive R Archive Network (CRAN) Ben Wu [aut, cre], Ying Guo [aut], Jian Kang [aut] GPL (>= 3) 2025-10-16 application/tgz https://CRAN.R-project.org/package=BSPBSS doi:10.32614/CRAN.package.BSPBSS