CCA: An R Package to Extend Canonical Correlation Analysis. Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN,, to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.

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  1. Langworthy, Benjamin W.; Stephens, Rebecca L.; Gilmore, John H.; Fine, Jason P.: Canonical correlation analysis for elliptical copulas (2021)
  2. Wang, Wenjia; Zhou, Yi-Hui: Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (2021)
  3. Prates, Marcos Oliveira; Assunção, Renato Martins; Rodrigues, Erica Castilho: Alleviating spatial confounding for areal data problems by displacing the geographical centroids (2019)
  4. Chaturvedi, Nimisha; de Menezes, Renée X.; Goeman, Jelle J.: A global (\times) global test for testing associations between two large sets of variables (2017)
  5. Matías Castillo, Brenda Catalina; Sandoval Solís, María de Lourdes; Linares Fleites, Gladys; Reyes Cervantes, Hortensia Josefina: Canonical correlation analysis using genetic algorithms (2017)
  6. Fieller, Nick: Basics of matrix algebra for statistics with \textttR (2015)
  7. Tang, Yu-Hang; Kudo, Shuhei; Bian, Xin; Li, Zhen; Karniadakis, George Em: Multiscale universal interface: a concurrent framework for coupling heterogeneous solvers (2015)
  8. Tenenhaus, Arthur; Philippe, Cathy; Frouin, Vincent: Kernel generalized canonical correlation analysis (2015)
  9. Wilms, Ines; Croux, Christophe: Sparse canonical correlation analysis from a predictive point of view (2015)
  10. Cruz-Cano, Raul; Lee, Mei-Ling Ting: Fast regularized canonical correlation analysis (2014)
  11. Yuan, Yun-Hao; Sun, Quan-Sen; Ge, Hong-Wei: Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition (2014)
  12. Lykou, Anastasia; Whittaker, Joe: Sparse CCA using a lasso with positivity constraints (2010)
  13. Soneson, Charlotte; Lilljebjörn, Henrik; Fioretos, Thoas; Fontes, Magnus: Integrative analysis of gene expression and copy number alterations using canonical correlation analysis (2010) ioport
  14. Cao, Kim-Anh Lê; Martin, Pascal G. P.; Robert-Granié, Christèle; Besse, Philippe: Sparse canonical methods for biological data integration: application to a cross-platform study (2009) ioport
  15. Cao, Kim-Anh Lê; Rossouw, Debra; Robert-Granié, Christèle; Besse, Philippe: A sparse PLS for variable selection when integrating omics data (2008)
  16. Ignacio González; Sébastien Déjean; Pascal Martin; Alain Baccini: CCA: An R Package to Extend Canonical Correlation Analysis (2008) not zbMATH