Package: mixedCCA
Type: Package
Title: Sparse Canonical Correlation Analysis for High-Dimensional Mixed
        Data
Version: 1.6.3
Date: 2025-11-17
Authors@R: c(
    person(given = "Grace",
           family = "Yoon",
           role = c("aut"),
           email = "gyoon6067@gmail.com",
           comment = c(ORCID = "0000-0003-3263-1352")),
    person(given = "Mingze",
           family = "Huang",
           role = c("ctb"),
           email = "mingzehuang@gmail.com",
           comment = c(ORCID = "0000-0003-3919-1564")),
    person(given = "Irina",
           family = "Gaynanova",
           role = c("aut", "cre"),
           email = "irinagn@umich.edu",
           comment = c(ORCID = "0000-0002-4116-0268")))
Maintainer: Irina Gaynanova <irinagn@umich.edu>
Description: 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>.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.0.1), stats, MASS
Imports: Rcpp, pcaPP, Matrix, fMultivar, mnormt, irlba, latentcor (>=
        2.0.1)
NeedsCompilation: yes
RoxygenNote: 7.3.3
LinkingTo: Rcpp, RcppArmadillo
Packaged: 2025-11-17 21:54:20 UTC; irinagn
Author: Grace Yoon [aut] (ORCID: <https://orcid.org/0000-0003-3263-1352>),
  Mingze Huang [ctb] (ORCID: <https://orcid.org/0000-0003-3919-1564>),
  Irina Gaynanova [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-4116-0268>)
Repository: CRAN
Date/Publication: 2025-11-18 08:30:10 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-26 19:46:49 UTC; unix
Archs: mixedCCA.so.dSYM
