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FSHybridPLS

Hybrid Penalized Partial Least Squares for predictors that combine functional curves (fda::fd) and scalar covariates.

The method is described in Mun and Jang (2026):
https://doi.org/10.48550/arXiv.2601.16364

Installation

# Once on CRAN:
# install.packages("FSHybridPLS")

# Development version:
# remotes::install_github("Jong-Min-Moon/FShybridPLS")

Quick start

library(FSHybridPLS)
set.seed(1)

sim <- simulate_hybrid_data(n = 60, n_functional = 1, n_scalar = 3, n_basis = 7)
prep <- split_and_normalize_all(sim$W, sim$y, train_ratio = 0.7)

fit <- fit_hybridPLS(
  prep$predictor_train,
  prep$response_train,
  n_iter = 3,
  lambda = 1e-3,
  validation_data = list(
    W_test = prep$predictor_test,
    y_test = prep$response_test
  )
)

fit
preds <- predict(fit, prep$predictor_test, n_components = fit$n_iter)
sqrt(mean((prep$response_test - preds)^2))

Main API

Function	Role
`predictor_hybrid()`	Build hybrid predictor object
`simulate_hybrid_data()`	Synthetic data for examples/tests
`split_and_normalize_all()`	Train/test split + normalization
`fit_hybridPLS()`	Fit Hybrid Penalized PLS
`predict()` / `print()`	S3 methods for class `hybridPLS`
`cv_fit_hybridPLS()`	Choose number of components by CV
`create_idx_kfold()`	K-fold index helper

Citation

citation("FSHybridPLS")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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