Authors@R: c(person("Ziwen","An",role=c("aut"),email="ziwen.an@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-9947-5182")),
person("Leah F.","South",role=c("aut","cre"),email="l1.south@qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")),
Maintainer: Leah F. South <l1.south@qut.edu.au>

Extensions to this package are planned. For a journal article describing how
to use this package, see An et al. (2022) <doi:10.18637/jss.v101.i11>.

Title: Bayesian Proposal to Fit Spatial Econometric Models
Author: Jorge Sicacha-Parada and Edilberto Cepeda-Cuervo
Depends: R (>= 3.1.1), mvtnorm, spdep, pscl
Description: The purpose of this package is to fit the three Spatial Econometric Models proposed
in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. The fit is made through MCMC algorithms
and observational working variables approach.

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

Title: Bayesian Spatial Blind Source Separation
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 working paper: Ben et al. (2022+) "Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process".
Imports: rstiefel, Rcpp, ica, glmnet, gplots, BayesGPfit, svd,
RandomFieldsUtils, neurobase, oro.nifti, gridExtra, ggplot2,

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" <doi:10.1080/01621459.2022.2123336>.