Package: FSHybridPLS
Title: Hybrid Penalized Partial Least Squares for Mixed Data
Version: 0.1.0
Authors@R: 
    person("Jongmin", "Mun", , "jongmin.mun@marshall.usc.edu",
           role = c("aut", "cre", "cph"))
Description: 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>.
License: MIT + file LICENSE
URL: https://github.com/Jong-Min-Moon/FShybridPLS
BugReports: https://github.com/Jong-Min-Moon/FShybridPLS/issues
Imports: fda (>= 6.1.3), stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Config/testthat/edition: 3
Config/checktor/allow: print_cat_usage
Config/checktor/acronyms: PLS
Encoding: UTF-8
RoxygenNote: 7.3.3
NeedsCompilation: no
VignetteBuilder: knitr
Packaged: 2026-09-13 07:46:25 UTC; jongmin
Author: Jongmin Mun [aut, cre, cph]
Maintainer: Jongmin Mun <jongmin.mun@marshall.usc.edu>
Repository: CRAN
Date/Publication: 2026-09-23 04:00:09 UTC
Built: R 4.5.3; ; 2026-09-23 07:32:45 UTC; windows
