[aut],
Irina Gaynanova
[aut, cre]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 [aut],
Irina Gaynanova
[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_1.4.6.tar.gz |
| Windows binaries: | r-devel: mixedCCA_1.4.6.zip, r-release: mixedCCA_1.4.6.zip, r-oldrel: mixedCCA_1.4.6.zip |
| macOS binaries: | r-release: mixedCCA_1.4.6.tgz, r-oldrel: mixedCCA_1.4.6.tgz |
| Old sources: | mixedCCA archive |
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