Package: iTensor 1.0.6

Koki Tsuyuzaki

iTensor: ICA-Based Matrix/Tensor Decomposition

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>.

Authors:Koki Tsuyuzaki [aut, cre]

iTensor_1.0.6.tar.gz
iTensor_1.0.6.zip(r-4.7)iTensor_1.0.6.zip(r-4.6)iTensor_1.0.6.zip(r-4.5)
iTensor_1.0.6.tgz(r-4.6-any)iTensor_1.0.6.tgz(r-4.5-any)
iTensor_1.0.6.tar.gz(r-4.7-any)iTensor_1.0.6.tar.gz(r-4.6-any)
iTensor_1.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
iTensor/json (API)

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

Bug tracker:https://github.com/rikenbit/itensor/issues

On CRAN:

Conda:

5.86 score 2 stars 1 packages 1 scripts 493 downloads 8 exports 82 dependencies

Last updated from:9f73262cb2. Checks:2 ERROR, 7 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR241
source / vignettesOK257
linux-release-x86_64ERROR254
macos-release-arm64OK133
macos-oldrel-arm64OK131
windows-develOK154
windows-releaseOK157
windows-oldrelOK169
wasm-releaseOK209

Exports:CorrIndexgeigenGroupICAICAICA2MICAMultilinearICAtoyModel

Dependencies:base64encBHBiocParallelbslibcachemclicodetoolscorpcorcpp11digestdplyreinsumellipseevaluatefarverfastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsggplot2ggrepelgluegridExtragtablehighrhtmltoolshtmlwidgetsigraphisobandjointDiagjquerylibjsonliteknitrlabelinglambda.rlatticelifecyclemagrittrMASSmathjaxrMatrixmatrixStatsmemoisemgcvmimemixOmicsnlmepillarpkgconfigplyrpurrrR6rappdirsrARPACKRColorBrewerRcppRcppEigenreshape2rglrlangrmarkdownRSpectrarTensorS7sassscalessnowstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Independent Component Analysis (ICA)
Introduction | 1. ICA with time-independent sub-Gaussian data | 2. ICA with time-independent super-Gaussian data | 3. ICA with data mixed with signals having no time dependence and different kurtosis | 4. ICA with time-dependent data | 5. IPCA in N < P systems | Session Information | References
Rendered fromiTensor-1.Rmdusingknitr::rmarkdown

Last update: 2023-04-28
Started: 2023-04-26

Multimodal Independent Component Analysis (MICA) and Group Independent Component Analysis (GroupICA)
Introduction | Multimodal Independent Component Analysis (MICA) | Group Independent Component Analysis (GroupICA) | Session Information | References
Rendered fromiTensor-2.Rmdusingknitr::rmarkdown

Last update: 2023-04-28
Started: 2023-04-26

Multilinear Independent Component Analysis (MultilinearICA)
Introduction | Multilinear Independent Component Analysis (MultilinearICA) | Session Information | References
Rendered fromiTensor-3.Rmdusingknitr::rmarkdown

Last update: 2023-04-26
Started: 2023-04-26