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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 |
| Reference manual: | BSSoverSpace.pdf |
| Vignettes: |
Introduction to BSSoverSpace |
| 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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.