PearsonICA: Independent Component Analysis using Score Functions from the Pearson System

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.

Version: 1.2-5
Imports: grDevices, graphics, stats
Published: 2022-02-21
Author: Juha Karvanen
Maintainer: Juha Karvanen <juha.karvanen at>
License: AGPL-3
NeedsCompilation: no
Citation: PearsonICA citation info
CRAN checks: PearsonICA results


Reference manual: PearsonICA.pdf


Package source: PearsonICA_1.2-5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PearsonICA_1.2-5.tgz, r-oldrel (arm64): PearsonICA_1.2-5.tgz, r-release (x86_64): PearsonICA_1.2-5.tgz
Old sources: PearsonICA archive


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