Package: SparseICA 0.1.4

Zihang Wang

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.

Authors:Zihang Wang [aut, cre], Irina Gaynanova [aut], Aleksandr Aravkin [aut], Benjamin Risk [aut]

SparseICA_0.1.4.tar.gz
SparseICA_0.1.4.tar.gz(r-4.5-noble)SparseICA_0.1.4.tar.gz(r-4.4-noble)
SparseICA_0.1.4.tgz(r-4.4-emscripten)SparseICA_0.1.4.tgz(r-4.3-emscripten)
SparseICA.pdf |SparseICA.html
SparseICA/json (API)

# Install 'SparseICA' in R:
install.packages('SparseICA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/thebrisklab/sparseica/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

openblascpp

1.00 score 10 exports 53 dependencies

Last updated 4 hours agofrom:f6f0141843. Checks:2 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 29 2025
R-4.5-linux-x86_64OKJan 29 2025

Exports:BIC_sparseICAcreate_group_listest.M.olsgen_groupPCgroup_sparseICAmatchICArelax_and_split_ICAsignchangesparseICAwhitener

Dependencies:abindbase64encbitopsbslibcachemciftiToolscliclueclusterdigestdotCall64evaluatefastmapfieldsfontawesomefsgiftigluehighrhtmltoolshtmlwidgetsirlbajquerylibjsonliteknitrlatticelifecyclemagrittrmapsMASSMatrixmemoisemimeoro.niftiR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppArmadillorglrlangrmarkdownRNiftisassspamtinytexviridisLitexfunxml2yaml