nscancor: Non-Negative and Sparse CCA

Two implementations of canonical correlation analysis (CCA) that are based on iterated regression. By choosing the appropriate regression algorithm for each data domain, it is possible to enforce sparsity, non-negativity or other kinds of constraints on the projection vectors. Multiple canonical variables are computed sequentially using a generalized deflation scheme, where the additional correlation not explained by previous variables is maximized. 'nscancor' is used to analyze paired data from two domains, and has the same interface as the 'cancor' function from the 'stats' package (plus some extra parameters). 'mcancor' is appropriate for analyzing data from three or more domains. See <http://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/> and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more details.

Version:	0.6.1-25
Imports:	stats
Suggests:	CCA, glmnet, MASS, PMA, roxygen2, testthat (≥ 0.8)
Published:	2018-02-15
Author:	Christian Sigg [aut, cre], R Core team [ctb] (cancor interface and documentation)
Maintainer:	Christian Sigg <christian at sigg-iten.ch>
BugReports:	https://github.com/chrsigg/nscancor/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	http://sigg-iten.ch/research/
NeedsCompilation:	no
Citation:	nscancor citation info
Materials:	README
CRAN checks:	nscancor results

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