mixedCCA: Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data

Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Müller and Gaynanova (2020) <arXiv:2006.13875>.

Version: 1.4.3
Depends: R (≥ 3.0.1), stats, MASS
Imports: Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba, chebpol
LinkingTo: Rcpp, RcppArmadillo
Published: 2020-10-11
Author: Grace Yoon ORCID iD [aut, cre], Irina Gaynanova ORCID iD [aut]
Maintainer: Grace Yoon <gyoon6067 at gmail.com>
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: mixedCCA results

Downloads:

Reference manual: mixedCCA.pdf
Package source: mixedCCA_1.4.3.tar.gz
Windows binaries: r-devel: mixedCCA_1.4.3.zip, r-release: mixedCCA_1.4.3.zip, r-oldrel: mixedCCA_1.4.3.zip
macOS binaries: r-release: mixedCCA_1.4.3.tgz, r-oldrel: mixedCCA_1.4.3.tgz
Old sources: mixedCCA archive

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