mixedCCA: Sparse Canonical Correlation Analysis for High-Dimensional MixedData
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: | 1.6.3 |
| Depends: | R (≥ 3.0.1), stats,MASS |
| Imports: | Rcpp,pcaPP,Matrix,fMultivar,mnormt,irlba,latentcor (≥2.0.1) |
| LinkingTo: | Rcpp,RcppArmadillo |
| Published: | 2025-11-18 |
| DOI: | 10.32614/CRAN.package.mixedCCA |
| Author: | Grace Yoon [aut], Mingze Huang [ctb], Irina Gaynanova [aut, cre] |
| Maintainer: | Irina Gaynanova <irinagn at umich.edu> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | mixedCCA results |
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