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 |
| Author: |
Grace Yoon  [aut],
Mingze Huang
[ctb],
Irina Gaynanova
[aut, cre] |
| Maintainer: |
Irina Gaynanova <irinag at stat.tamu.edu> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
mixedCCA results |
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