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.

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>
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:, r-release:, r-oldrel:
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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