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PearsonICA  

Independent Component Analysis using Score Functions from the Pearson System
View on CRAN: Click here


Download and install PearsonICA package within the R console
Install from CRAN:
install.packages("PearsonICA")

Install from Github:
library("remotes")
install_github("cran/PearsonICA")

Install by package version:
library("remotes")
install_version("PearsonICA", "1.2-5")



Attach the package and use:
library("PearsonICA")
Maintained by
Juha Karvanen
[Scholar Profile | Author Map]
First Published: 2006-06-29
Latest Update: 2022-02-21
Description:
The Pearson-ICA algorithm is a mutual information-based method for blind separation of statistically independent source signals. It has been shown that the minimization of mutual information leads to iterative use of score functions, i.e. derivatives of log densities. The Pearson system allows adaptive modeling of score functions. The flexibility of the Pearson system makes it possible to model a wide range of source distributions including asymmetric distributions. The algorithm is designed especially for problems with asymmetric sources but it works for symmetric sources as well.
How to cite:
Juha Karvanen (2006). PearsonICA: Independent Component Analysis using Score Functions from the Pearson System. R package version 1.2-5, https://cran.r-project.org/web/packages/PearsonICA. Accessed 09 May. 2025.
Previous versions and publish date:
1.2-2 (2006-06-29 10:37), 1.2-3 (2008-10-10 16:21), 1.2-4 (2009-06-29 11:39)
Other packages that cited PearsonICA R package
View PearsonICA citation profile
Other R packages that PearsonICA depends, imports, suggests or enhances
Complete documentation for PearsonICA
Functions, R codes and Examples using the PearsonICA R package
Some associated functions: PearsonICA . PearsonICAdemo . 
Some associated R codes: PearsonICA.R .  Full PearsonICA package functions and examples
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