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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 cancor() from the 'stats' package (plus some extra parameters). mcancor() is appropriate for analyzing data from three or more domains. See <https://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: | 0.7.0-6 |
Depends: | R (≥ 3.6.0) |
Imports: | methods, stats |
Suggests: | CCA, glmnet, MASS, roxygen2, testthat (≥ 0.8), V8 |
Published: | 2023-03-31 |
DOI: | 10.32614/CRAN.package.nscancor |
Author: | Christian Sigg [aut, cph, cre], R Core team [cph, 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: | https://sigg-iten.ch/research/ |
NeedsCompilation: | no |
Citation: | nscancor citation info |
Materials: | README NEWS |
CRAN checks: | nscancor results |
Reference manual: | nscancor.pdf |
Package source: | nscancor_0.7.0-6.tar.gz |
Windows binaries: | r-devel: nscancor_0.7.0-6.zip, r-release: nscancor_0.7.0-6.zip, r-oldrel: nscancor_0.7.0-6.zip |
macOS binaries: | r-release (arm64): nscancor_0.7.0-6.tgz, r-oldrel (arm64): nscancor_0.7.0-6.tgz, r-release (x86_64): nscancor_0.7.0-6.tgz, r-oldrel (x86_64): nscancor_0.7.0-6.tgz |
Old sources: | nscancor archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.