The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
| DOI: | 10.32614/CRAN.package.LOCUS |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=LOCUS to link to this page.
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.