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multiCCA  

Multiple Canonical Correlation Analysis (Kernel and Functional)
View on CRAN: Click here


Download and install multiCCA package within the R console
Install from CRAN:
install.packages("multiCCA")

Install from Github:
library("remotes")
install_github("cran/multiCCA")

Install by package version:
library("remotes")
install_version("multiCCA", "0.1.0")



Attach the package and use:
library("multiCCA")
Maintained by
Tomasz Gorecki
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-23
Latest Update: 2026-03-23
Description:
Implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, with a focus on multivariate repeated measures and functional data. The package provides two approaches: (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional representations for exploratory analysis and visualization of dependence structures across multiple feature sets. The implementation follows the framework developed in Górecki, Krzyśko, Gnettner and Kokoszka (2025) <doi:10.48550/arXiv.2510.04457>.
How to cite:
Tomasz Gorecki (2026). multiCCA: Multiple Canonical Correlation Analysis (Kernel and Functional). R package version 0.1.0, https://cran.r-project.org/web/packages/multiCCA. Accessed 21 Aug. 2026.
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