Package: seedCCA
Type: Package
Title: Seeded Canonical Correlation Analysis
Version: 3.1
Date: 2022-06-09
Author: Jae Keun Yoo, Bo-Young Kim
Maintainer: Jae Keun Yoo <peter.yoo@ewha.ac.kr>
Description: Functions for dimension reduction through the seeded
        canonical correlation analysis are provided. A classical
        canonical correlation analysis (CCA) is one of useful
        statistical methods in multivariate data analysis, but it is
        limited in use due to the matrix inversion for large p small n
        data. To overcome this, a seeded CCA has been proposed in Im,
        Gang and Yoo (2015) \doi{10.1002/cem.2691}. The seeded CCA is a
        two-step procedure. The sets of variables are initially reduced
        by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and
        cov(Y), respectively, without loss of information on canonical
        correlation analysis, following Cook, Li and Chiaromonte (2007)
        \doi{10.1093/biomet/asm038} and Lee and Yoo (2014)
        \doi{10.1111/anzs.12057}. Then, the canonical correlation is
        finalized with the initially-reduced two sets of variables.
License: GPL (>= 2.0)
Depends: R(>= 2.10.0)
Imports: CCA, corpcor, stats, graphics
LazyData: yes
Encoding: UTF-8
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2026-06-20 08:44:09 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2022-06-09 03:40:03 UTC
RemoteUrl: https://github.com/cran/seedCCA
RemoteRef: HEAD
RemoteSha: 358df7aa68912a199731d55735294d30d12eb474