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>.
