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) <arXiv:2008.08915>. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling linux/jammy R-4.5 | LOCUS_1.0.tar.gz |
50.7 KiB |
1.0 |
rolling linux/noble R-4.5 | LOCUS_1.0.tar.gz |
50.5 KiB |
1.0 |
rolling source/ R- | LOCUS_1.0.tar.gz |
9.5 KiB |
1.0 |
latest linux/jammy R-4.5 | LOCUS_1.0.tar.gz |
50.7 KiB |
1.0 |
latest linux/noble R-4.5 | LOCUS_1.0.tar.gz |
50.5 KiB |
1.0 |
latest source/ R- | LOCUS_1.0.tar.gz |
9.5 KiB |
1.0 |
2026-04-26 source/ R- | LOCUS_1.0.tar.gz |
9.5 KiB |
1.0 |
2026-04-23 source/ R- | LOCUS_1.0.tar.gz |
9.5 KiB |
1.0 |
2026-04-09 windows/windows R-4.5 | LOCUS_1.0.zip |
53.0 KiB |
1.0 |
2025-04-20 source/ R- | LOCUS_1.0.tar.gz |
9.5 KiB |