Scarlett422301/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.
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Package details |
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| Author | Yikai Wang [aut, cph], Jialu Ran [aut, cre], Ying Guo [aut, ths] |
| Maintainer | Jialu Ran <jialuran422@gmail.com> |
| License | GPL-2 |
| Version | 1.1 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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