SparseICA: Sparse Independent Component Analysis

Provides an implementation of the Sparse ICA method in Wang et al. (2024) <doi:10.1080/01621459.2024.2370593> for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency.

Version: 0.1.4
Depends: R (≥ 4.1.0)
Imports: Rcpp (≥ 1.0.13), MASS (≥ 7.3-58), irlba (≥ 2.3.5), clue (≥ 0.3), ciftiTools (≥ 0.16), parallel (≥ 4.1)
LinkingTo: Rcpp, RcppArmadillo
Published: 2025-01-29
DOI: 10.32614/CRAN.package.SparseICA
Author: Zihang Wang ORCID iD [aut, cre], Irina Gaynanova ORCID iD [aut], Aleksandr Aravkin ORCID iD [aut], Benjamin Risk ORCID iD [aut]
Maintainer: Zihang Wang <zhwang0378 at gmail.com>
BugReports: https://github.com/thebrisklab/SparseICA/issues
License: GPL-3
URL: https://github.com/thebrisklab/SparseICA
NeedsCompilation: yes
Citation: SparseICA citation info
CRAN checks: SparseICA results

Documentation:

Reference manual: SparseICA.html , SparseICA.pdf

Downloads:

Package source: SparseICA_0.1.4.tar.gz
Windows binaries: r-devel: SparseICA_0.1.4.zip, r-release: SparseICA_0.1.4.zip, r-oldrel: SparseICA_0.1.4.zip
macOS binaries: r-release (arm64): SparseICA_0.1.4.tgz, r-oldrel (arm64): SparseICA_0.1.4.tgz, r-release (x86_64): SparseICA_0.1.4.tgz, r-oldrel (x86_64): SparseICA_0.1.4.tgz

Linking:

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