R (>= 4.0.0), stats, mvtnorm, spdep, pscl, pbapply, coda

Bayesian Spatial Blind Source Separation
c( person("Ben", "Wu",
email = "wuben@ruc.edu.cn",
role = c("aut", "cre")),
person("Ying", "Guo",
email = "yguo2@emory.edu",
role = "aut"),
person("Jian", "Kang",
email = "jiankang@umich.edu",
role = "aut") )
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 working paper: Ben et al. (2022+) "Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process".
rstiefel, Rcpp, ica, glmnet, gplots, BayesGPfit, svd,
RandomFieldsUtils, neurobase, oro.nifti, gridExtra, ggplot2,
gtools
Ben Wu [aut, cre],
Ying Guo [aut],
Jian Kang [aut]

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>.

rstiefel, Rcpp, ica, glmnet, gplots, BayesGPfit, svd,
neurobase, oro.nifti, gridExtra, ggplot2, gtools

person("Phillip", "Murray", email = "phillip.murray18@imperial.ac.uk",
role = c("aut", "cre"))
Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in
Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes
to data, and functions to estimate the stochastic volatility process of a BSS process.