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
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:

Reference manual: LOCUS.pdf

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=LOCUS to link to this page.