https://jong-min-moon.r-universe.devRecent package updates in jong-min-moonR-universehttps://github.com/jong-min-moon.pnghttps://jong-min-moon.r-universe.devMon, 14 Sep 2026 20:58:33 GMTjongmin.mun@marshall.usc.edu (Jongmin Mun)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>.https://github.com/r-universe/jong-min-moon/actions/runs/35842907171Mon, 14 Sep 2026 20:58:33 GMTFSHybridPLS0.1.0successhttps://jong-min-moon.r-universe.devhttps://github.com/jong-min-moon/fshybridplsFSHybridPLS.RmdFSHybridPLS.htmlIntroduction to FSHybridPLS2026-09-13 18:56:102026-09-13 18:56:10