LOCUS: Low-Rank Decomposition of Brain Connectivity Matrices with
Uniform Sparsity
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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=LOCUS
to link to this page.