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 |
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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 [aut], Mingze Huang [ctb], Irina Gaynanova [aut, cre] |
Maintainer: | Irina Gaynanova |
License: | GPL-3 |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | mixedCCA results |
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