_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-itensor 1.0.2
Propagated dependencies: r-rtensor@1.4.8 r-mixomics@6.30.0 r-mgcv@1.9-1 r-mass@7.3-61 r-jointdiag@0.4 r-groupica@0.1.1 r-geigen@2.3 r-einsum@0.1.2
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/rikenbit/iTensor
Licenses: Expat
Synopsis: ICA-Based Matrix/Tensor Decomposition
Description:

Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.

Total results: 1