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: 2026-09-23 06:14:01 UTC; root
Author: Jongmin Mun [aut, cre, cph]
Maintainer: Jongmin Mun <jongmin.mun@marshall.usc.edu>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-09-23 06:06:45 UTC
RemoteUrl: https://github.com/cran/FSHybridPLS
RemoteRef: HEAD
RemoteSha: 15c6badc3f79071ebd31ef4f0f3a76a9b275ded6