The R Journal: accepted article

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SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares PDF download
Bo-Young Kim, Yunju Im and Jae Keun Yoo

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 science fields such as chemomtrics, pattern recognition, genomic sequence analysis and so on. The so-called seedCCA is a newly developed R package, and it 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 high dimensional data analysis in various science field practitioners and that the statistical methodologies in multivariate analysis become more fruitful.

Received: 2019-07-18; online 2021-06-07


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@article{RJ-2021-026,
  author = {Bo-Young Kim and Yunju Im and Jae Keun Yoo},
  title = {{SEEDCCA: An Integrated R-Package for Canonical Correlation
          Analysis and Partial Least Squares}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-026},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-026/index.html}
}