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: 1.4.6
Depends: R (≥ 3.0.1), stats, MASS
Imports: Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba, chebpol
LinkingTo: Rcpp, RcppArmadillo
Published: 2021-03-20
Author: Grace Yoon ORCID iD [aut], Irina Gaynanova ORCID iD [aut, cre]
Maintainer: Irina Gaynanova <irinag at stat.tamu.edu>
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: mixedCCA results

Documentation:

Reference manual: mixedCCA.pdf

Downloads:

Package source: mixedCCA_1.4.6.tar.gz
Windows binaries: r-devel: mixedCCA_1.4.6.zip, r-devel-UCRT: mixedCCA_1.4.6.zip, r-release: mixedCCA_1.4.6.zip, r-oldrel: mixedCCA_1.4.6.zip
macOS binaries: r-release (arm64): mixedCCA_1.4.6.tgz, r-release (x86_64): mixedCCA_1.4.6.tgz, r-oldrel: mixedCCA_1.4.6.tgz
Old sources: mixedCCA archive

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