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Canonical correlation analysis and maximum correlation via projection pursuit, as well as fast implementations of correlation estimators, with a focus on robust and nonparametric methods.
Version: | 0.3.3 |
Depends: | R (≥ 3.2.0), parallel, pcaPP (≥ 1.8-1), robustbase |
Imports: | Rcpp (≥ 0.11.0) |
LinkingTo: | Rcpp (≥ 0.11.0), RcppArmadillo (≥ 0.4.100.0) |
Suggests: | knitr, mvtnorm |
Published: | 2019-12-09 |
DOI: | 10.32614/CRAN.package.ccaPP |
Author: | Andreas Alfons [aut, cre], David Simcha [ctb] |
Maintainer: | Andreas Alfons <alfons at ese.eur.nl> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Citation: | ccaPP citation info |
Materials: | NEWS |
CRAN checks: | ccaPP results |
Reference manual: | ccaPP.pdf |
Vignettes: |
Robust Maximum Association Between Data Sets: The R Package ccaPP |
Package source: | ccaPP_0.3.3.tar.gz |
Windows binaries: | r-devel: ccaPP_0.3.3.zip, r-release: ccaPP_0.3.3.zip, r-oldrel: ccaPP_0.3.3.zip |
macOS binaries: | r-release (arm64): ccaPP_0.3.3.tgz, r-oldrel (arm64): ccaPP_0.3.3.tgz, r-release (x86_64): ccaPP_0.3.3.tgz, r-oldrel (x86_64): ccaPP_0.3.3.tgz |
Old sources: | ccaPP archive |
Reverse imports: | ctsGE, nanostringr, phantasus |
Reverse suggests: | yaImpute |
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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.
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