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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 |
| DOI: |
10.32614/CRAN.package.mixedCCA |
| 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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=mixedCCA
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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