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seedCCA
View on CRAN: Click
here
Download and install seedCCA package within the R console
Install from CRAN:
install.packages("seedCCA")
Install from Github:
library("remotes")
install_github("cran/seedCCA") Install by package version:
library("remotes")
install_version("seedCCA", "3.1") Attach the package and use:
library("seedCCA")
Maintained by
Jae Keun Yoo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-08-30
Latest Update: 2022-06-09
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.
How to cite:
Jae Keun Yoo (2017). seedCCA: Seeded Canonical Correlation Analysis. R package version 3.1, https://cran.r-project.org/web/packages/seedCCA. Accessed 25 Jun. 2026.
Other packages that cited seedCCA R package
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Other R packages that seedCCA depends,
imports, suggests or enhances
Complete documentation for seedCCA
Functions, R codes and Examples using
the seedCCA R package
Some associated functions: Pm . coef.seedCCA . cookie . covplot . finalCCA . fitted.seedCCA . iniCCA . nutrimouse . plot.seedCCA . print.seedCCA . seedCCA . seeding.auto.stop . seeding . seedols . seedpls . selectu .
Some associated R codes: seedCCA.R . Full seedCCA package functions and examples
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