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To decompose symmetric matrices such as brain connectivity matrices so that one can extract sparse latent component matrices and also estimate mixing coefficients, a blind source separation (BSS) method named LOCUS was proposed in Wang and Guo (2023) <doi:10.48550/arXiv.2008.08915>. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings.
| Version: | 1.0 |
| Depends: | R (≥ 3.1.0), ica, MASS, far |
| Published: | 2022-10-04 |
| Author: | Yikai Wang [aut, cph], Jialu Ran [aut, cre], Ying Guo [aut, ths] |
| Maintainer: | Jialu Ran <jialuran422 at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | LOCUS results |
| Reference manual: | LOCUS.pdf |
| Package source: | LOCUS_1.0.tar.gz |
| Windows binaries: | r-devel: LOCUS_1.0.zip, r-release: LOCUS_1.0.zip, r-oldrel: LOCUS_1.0.zip |
| macOS binaries: | r-release (arm64): LOCUS_1.0.tgz, r-oldrel (arm64): LOCUS_1.0.tgz, r-release (x86_64): LOCUS_1.0.tgz, r-oldrel (x86_64): LOCUS_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.
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