doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.">

mixedCCA: Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data (original) (raw)

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
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: mixedCCA results

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