SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares

Bo Young Kim, Yunju Im, Jae Keun Yoo

Research output: Contribution to journalArticlepeer-review

Abstract

Canonical correlation analysis (CCA) has a long history as an explanatory statistical method in high-dimensional data analysis and has been successfully applied in many scientific fields such as chemometrics, pattern recognition, genomic sequence analysis, and so on. The so-called seedCCA is a newly developed R package that implements not only the standard and seeded CCA but also partial least squares. The package enables us to fit CCA to large- p and small-n data. The paper provides a complete guide. Also, the seeded CCA application results are compared with the regularized CCA in the existing R package. It is believed that the package, along with the paper, will contribute to highdimensional data analysis in various science field practitioners and that the statistical methodologies in multivariate analysis become more fruitful.

Original languageEnglish
Pages (from-to)7-20
Number of pages14
JournalR Journal
Volume13
Issue number1
DOIs
StatePublished - 2021

Bibliographical note

Funding Information:
For the corresponding author Jae Keun Yoo, this work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education (NRF-2019R1F1A1050715). For Bo-Young Kim, this work was supported by the BK21 Plus Project through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education (22A20130011003).

Publisher Copyright:
© 2021. All Rights Reserved.

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