c(person("Martin","Becker", role = c("aut","cre"),
email = "martin.becker@mx.uni-saarland.de",
comment=c(ORCID="0000-0003-2336-9751")),
person("Stefan","Klößner", role = c("aut"),
email = "S.Kloessner@mx.uni-saarland.de",
comment=c(ORCID="0000-0002-6558-7370")),
person("Joel","Heinrich", role = c("ctb")))
Martin Becker [aut, cre] (<https://orcid.org/0000-0003-2336-9751>),
Stefan Klößner [aut] (<https://orcid.org/0000-0002-6558-7370>),
Joel Heinrich [ctb]

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.

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.

Independent Component Analysis using Score Functions from the
Pearson System

An implementation for association analysis with CNV data.
Meiling Liu, Sungho Won and Weicheng Zhu
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.
'AI_EM_mixed.R' 'ClusProc.R' 'cnvlmm_plot.R' 'silWidth.R'
'docs.R' 'score_test.R' 'AssoTestProcCS_mix.R'