steadyICA-package: ICA via distance covariance, tests of mutual independence,...

In steadyICA: ICA and Tests of Independence via Multivariate Distance Covariance

Description

Functions related to multivariate measures of independence and ICA:
-estimate independent components by minimizing distance covariance;
-conduct a test of mutual independence based on distance covariance;
-estimate independent components via infomax (a popular method but generally performs poorer than steadyICA or ProDenICA but is useful for comparisons);
-order independent components by skewness;
-match independent components from multiple estimates;
-other functions useful in ICA.

Details

Package:	steadyICA
Type:	Package
Version:	1.0
Date:	2015-11-08
License:	GPL (>= 2)
Depends:	Rcpp (>= 0.9.13), MASS
Suggests:	irlba, JADE, ProDenICA, fastICA

Author(s)

Benjamin B. Risk and Nicholas A. James and David S. Matteson.
Maintainer: Benjamin Risk <[e-mail]>

References

Bernaards, C. & Jennrich, R. (2005) Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement 65, 676-696

Matteson, D. S. & Tsay, R. Independent component analysis via U-Statistics. <http://www.stat.cornell.edu/~matteson/#ICA>

Szekely, G., Rizzo, M. & Bakirov, N. Measuring and testing dependence by correlation of distances. (2007) The Annals of Statistics, 35, 2769-2794.

Tichavsky, P. & Koldovsky, Z. Optimal pairing of signal components separated by blind techniques. (2004) Signal Processing Letters 11, 119-122.

See Also

fastICA ProDenICA::ProDenICA

Examples

#see steadyICA

Example output

Loading required package: Rcpp
Loading required package: MASS
Loading required package: clue
Loading required package: combinat

Attaching package: ‘combinat’

The following object is masked from ‘package:utils’:

    combn

steadyICA documentation built on May 2, 2019, 7:30 a.m.