Package: FSHybridPLS 0.1.0

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>.

Authors:Jongmin Mun [aut, cre, cph]