Learn R Programming

iTensor (version 1.0.2)

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 .

Copy Link

Version

Install

install.packages('iTensor')

Monthly Downloads

169

Version

1.0.2

License

MIT + file LICENSE

Issues

0

Pull Requests

0

Stars

1

Forks

1

Maintainer

Koki Tsuyuzaki

Last Published

April 28th, 2023

Functions in iTensor (1.0.2)

ICA

Independent Component Analysis (Classic Methods)
ICA2

Independent Component Analysis (Modern Methods)
CorrIndex

CorrIndex
MICA

Multimodal independent component analysis
toyModel

Toy model data for using ICA, MICA, and GroupICA There are 7 types of simulation: ICA_Type1: Time-independent sub-gaussian data ICA_Type2: Time-independent super-gaussian data ICA_Type3: Data mixed with signals having no time dependence and different kurtosis ICA_Type4: Time-dependent data ICA_Type5: Toydata to model IPCA in N < P systems MICA: Two time-serices data to model MICA GroupICA: Toydata to model GroupICA
MultilinearICA

Multilinear independent component analysis
GroupICA

Group Independent Component Analysis (GroupICA)