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BSPBSS  

Bayesian Spatial Blind Source Separation
View on CRAN: Click here


Download and install BSPBSS package within the R console
Install from CRAN:
install.packages("BSPBSS")

Install from Github:
library("remotes")
install_github("cran/BSPBSS")

Install by package version:
library("remotes")
install_version("BSPBSS", "1.0.5")



Attach the package and use:
library("BSPBSS")
Maintained by
Ben Wu
[Scholar Profile | Author Map]
First Published: 2022-09-02
Latest Update: 2022-11-25
Description:
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" .
How to cite:
Ben Wu (2022). BSPBSS: Bayesian Spatial Blind Source Separation. R package version 1.0.5, https://cran.r-project.org/web/packages/BSPBSS. Accessed 07 May. 2025.
Previous versions and publish date:
1.0.2 (2022-09-02 09:50), 1.0.3 (2022-09-18 05:06), 1.0.4 (2022-10-01 16:40)
Other packages that cited BSPBSS R package
View BSPBSS citation profile
Other R packages that BSPBSS depends, imports, suggests or enhances
Complete documentation for BSPBSS
Functions, R codes and Examples using the BSPBSS R package
Some associated functions: init_bspbss . levelplot2D . mcmc_bspbss . output_nii . pre_nii . sim_2Dimage . sum_mcmc_bspbss . 
Some associated R codes: ICA_imax.R . RcppExports.R . create.2D.image.R . init_bspbss.R . levelplot2D.R . mcmc_bspbss.R . output_nii.R . pre_nii.R . sim_2Dimage.R . sum_mcmc_bspbss.R .  Full BSPBSS package functions and examples
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