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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "PearsonICA" in publications use:'
type: software
license: AGPL-3.0-only
title: 'PearsonICA: Independent Component Analysis using Score Functions from the
  Pearson System'
version: 1.2-5
identifiers:
- type: doi
  value: 10.32614/CRAN.package.PearsonICA
abstract: 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.
authors:
- family-names: Karvanen
  given-names: Juha
  email: juha.karvanen@iki.fi
preferred-citation:
  type: article
  title: Blind separation methods based on Pearson system and its extensions
  authors:
  - family-names: Karvanen
    given-names: Juha
    email: juha.karvanen@ktl.fi
  - family-names: Koivunen
    given-names: Visa
  journal: Signal Processing
  volume: '82'
  issue: '4'
  year: '2002'
  url: http://dx.doi.org/10.1016/S0165-1684(01)00213-4
  start: '663'
  end: '673'
repository: https://juhakarvanen.r-universe.dev
commit: 40c0b2cfabba7a8077a271ab53e21f24a0da3737
date-released: '2022-02-19'
contact:
- family-names: Karvanen
  given-names: Juha
  email: juha.karvanen@iki.fi
references:
- type: manual
  title: 'PearsonICA: Independent component analysis using score functions from the
    Pearson system'
  authors:
  - family-names: Karvanen
    given-names: Juha
    email: juha.karvanen@ktl.fi
  year: '2008'
  notes: R package version 1.2-3