Package: PearsonICA
Type: Package
Title: Independent component analysis using score functions from the Pearson system
Version: 1.2-3
Date: 2006-10-10
Author: Juha Karvanen
Maintainer: Juha Karvanen <juha.karvanen@ktl.fi>
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.
License: GPL 2
Packaged: Fri Oct 10 14:49:59 2008; jkau

Package: PearsonICA
Type: Package
Title: Independent component analysis using score functions from the
Pearson system
Version: 1.2-4
Date: 2006-06-29
Author: Juha Karvanen
Maintainer: Juha Karvanen <juha.karvanen@iki.fi>
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.
License: GPL-2
Packaged: 2009-06-29 08:27:53 UTC; jkau
Repository: CRAN
Date/Publication: 2009-06-29 09:39:47

Title: Independent Component Analysis using Score Functions from the

Title: An implementation for association analysis with CNV data.
Author: Meiling Liu, Sungho Won and Weicheng Zhu
Description: An implementation for association analysis with CNV data in R. It
provides two methods for association study: first, the observed probe
intensity measurement can be directly used to detect the association of CNV
with phenotypes of interest. Second, the most probable copy number is
estimated with the proposed likelihood and the association of the most
probable copy number with phenotype is tested. This method can be applied
to both the independent and correlated population.
Depends: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.3.900.0), ggplot2
Collate: 'AI_EM_mixed.R' 'ClusProc.R' 'cnvlmm_plot.R' 'silWidth.R'