multiCCA: Multiple Canonical Correlation Analysis (Kernel and Functional)
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>.
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