nscancor: Non-Negative and Sparse CCA
Version 0.6

This package implements two algorithms for canonical correlation analysis (CCA) that are based on iterated regression steps. By choosing the appropriate regression algorithm for each data modality, 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.

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AuthorChristian Sigg [aut, cre], R Core team [aut]
Date of publication2014-07-17 23:46:07
MaintainerChristian Sigg <christian@sigg-iten.ch>
LicenseGPL (>= 2)
Version0.6
URL http://sigg-iten.ch/research/
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("nscancor")

Man pages

acor: Additional Explained Correlation
cardinality: Cardinality of Column Vectors
macor: Multi-Domain Additional Explained Correlation
mcancor: Non-Negative and Sparse Multi-Domain CCA
nscancor: Non-Negative and Sparse CCA

Functions

acor Man page Source code
cardinality Man page Source code
macor Man page Source code
mcancor Man page Source code
mcc_inner Source code
normv Source code
nscancor Man page Source code
nscc_inner Source code

Files

inst
inst/nscancor_examples.R
inst/mcancor_examples.R
inst/CITATION
tests
tests/run-all.R
tests/testthat
tests/testthat/test_macor.R
tests/testthat/test_acor.R
tests/testthat/test_nscancor.R
tests/testthat/test_mcancor.R
NAMESPACE
R
R/nscancor-package.R
R/cardinality.R
R/acor.R
R/mcancor.R
R/macor.R
R/normv.R
R/nscancor.R
README.md
MD5
DESCRIPTION
man
man/nscancor.Rd
man/cardinality.Rd
man/mcancor.Rd
man/acor.Rd
man/macor.Rd
nscancor documentation built on May 20, 2017, 2:19 a.m.