BSSoverSpace: Blind Source Separation for Multivariate Spatial Data using Eigen Analysis

Provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. 'BSSoverSpace' is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<doi:10.48550/arXiv.2201.02023>.

Version:	0.1.0
Imports:	SpatialBSS, expm, rSPDE
Suggests:	knitr, rmarkdown
Published:	2022-11-10
DOI:	10.32614/CRAN.package.BSSoverSpace
Author:	Sixing Hao [aut, cre]
Maintainer:	Sixing Hao <s.hao3 at lse.ac.uk>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	BSSoverSpace results

Documentation:

Reference manual:	BSSoverSpace.pdf
Vignettes:	Introduction to BSSoverSpace

Downloads:

Package source:	BSSoverSpace_0.1.0.tar.gz
Windows binaries:	r-devel: BSSoverSpace_0.1.0.zip, r-release: BSSoverSpace_0.1.0.zip, r-oldrel: BSSoverSpace_0.1.0.zip
macOS binaries:	r-release (arm64): BSSoverSpace_0.1.0.tgz, r-oldrel (arm64): BSSoverSpace_0.1.0.tgz, r-release (x86_64): BSSoverSpace_0.1.0.tgz, r-oldrel (x86_64): BSSoverSpace_0.1.0.tgz

Linking:

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