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 ruc.edu.cn> License: GPL (≥ 3) NeedsCompilation: yes SystemRequirements: GNU make Materials: README CRAN checks: BSPBSS results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=BSPBSS to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4