FSHybridPLS: Hybrid Penalized Partial Least Squares for Mixed Data
Fits Penalized Partial Least Squares (PLS) regression when
predictors are hybrid objects that combine functional curves
(infinite-dimensional 'fda' objects) and scalar covariates (finite-dimensional
numeric matrices). The package treats a hybrid predictor as an element of a
product Hilbert space formed by the functional and Euclidean components, and
implements the arithmetic (addition, scalar multiplication, and inner
products, including roughness-penalized inner products) needed to run
penalized PLS directly in that space. The algorithm extracts latent
components that maximize covariance with a scalar response while
penalizing roughness of the estimated functional coefficient curves.
Helpers are included for constructing hybrid predictors, two-step
within- and between-modality normalization, train/test splitting,
synthetic data generation, cross-validated component selection, and
prediction. The method is described in Mun and Jang (2026)
<doi:10.48550/arXiv.2601.16364>.
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