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cff-version: 1.2.0
message: 'To cite package "SmoothPLS" in publications use:'
type: software
license: MIT
title: 'SmoothPLS: Partial Least-Squares Algorithm for Categorical and Scalar Functional
  Data'
version: 0.1.5
doi: 10.32614/CRAN.package.SmoothPLS
abstract: Performs the Partial Least-Squares ('PLS') algorithm for functional data
  through the concept of active area integration. This approach builds upon the basis
  expansion methods for functional 'PLS' regression described in Aguilera et al. (2010)
  <https://doi.org/10.1016/j.chemolab.2010.09.007>. The package seamlessly handles
  both Scalar Functional Data ('SFD') and Categorical Functional Data ('CFD'), providing
  interpretable regression curves even for discrete state changes. It was developed
  during a PhD thesis between 'DECATHLON' and French research institute 'INRIA' 2022-2026.
  The 'SmoothPLS' method does not directly decompose the data into a basis; rather,
  it assumes the data is known as precisely as desired, and for every 'PLS' component,
  the weight functions are decomposed into the basis. For both single-state and multi-state
  'CFD' as well as 'SFD', the algorithm is implemented for a scalar response. To provide
  a baseline, a naive 'PLS' method on time-value functions and standard Functional
  'PLS' are also implemented.
authors:
- family-names: Bassac
  given-names: Francois
  email: fr.bassac@gmail.com
repository: https://francoisbassac.r-universe.dev
repository-code: https://github.com/FrancoisBassac/SmoothPLS
commit: aa88cb3d611ea0cf0bc0be0559737a256993208c
url: https://FrancoisBassac.github.io/SmoothPLS/
date-released: '2026-04-10'
contact:
- family-names: Bassac
  given-names: Francois
  email: fr.bassac@gmail.com