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, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Version: 1.6.2
Depends: R (≥ 3.0.1), stats, MASS
Imports: Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba, latentcor (≥ 2.0.1)
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-09-09
DOI: 10.32614/CRAN.package.mixedCCA
Author: Grace Yoon ORCID iD [aut], Mingze Huang ORCID iD [ctb], 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


Reference manual: mixedCCA.pdf


Package source: mixedCCA_1.6.2.tar.gz
Windows binaries: r-devel: mixedCCA_1.6.2.zip, r-release: mixedCCA_1.6.2.zip, r-oldrel: mixedCCA_1.6.2.zip
macOS binaries: r-release (arm64): mixedCCA_1.6.2.tgz, r-oldrel (arm64): mixedCCA_1.6.2.tgz, r-release (x86_64): mixedCCA_1.6.2.tgz, r-oldrel (x86_64): mixedCCA_1.6.2.tgz
Old sources: mixedCCA archive


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