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: 2026-10-04 11:47:00 UTC; root
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: https://cran.r-universe.dev
Date/Publication: 2025-11-18 08:30:10 UTC
RemoteUrl: https://github.com/cran/mixedCCA
RemoteRef: HEAD
RemoteSha: 86763cf5f934e11860b59d030905ca320e428143