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cff-version: 1.2.0
message: 'To cite package "lwpls" in publications use:'
type: software
license: MIT
title: 'lwpls: Locally-Weighted Partial Least Squares for ''tidymodels'''
version: 0.3.0
abstract: Locally-weighted partial least squares (LW-PLS) is a just-in-time learning
  method that builds a dedicated weighted partial least squares model around every
  new sample, where training samples are weighted by their similarity to that sample
  (Kim et al. (2011) <https://doi.org/10.1016/j.ijpharm.2011.10.007>). Regression
  (single or multiple outcomes) and classification (locally weighted partial least
  squares-discriminant analysis; Bevilacqua and Marini (2014) <https://doi.org/10.1016/j.aca.2014.05.057>)
  are supported. The local models can be robust to outliers, by partial robust M-regression
  (Serneels et al. (2005) <https://doi.org/10.1016/j.chemolab.2005.04.007>), and sparse
  (Hoffmann et al. (2015) <https://doi.org/10.1016/j.chemolab.2015.09.019>). The prediction
  kernel is written in C++ with 'RcppArmadillo', and the model is integrated with
  the 'parsnip', 'dials', 'tune' and 'yardstick' packages so it can be used within
  the 'tidymodels' framework. Diagnostics of the local models (prediction reliability,
  local regression vectors, robust weights and selected predictors) come with 'ggplot2'
  plots designed for spectroscopic data.
authors:
- family-names: Goueguel
  given-names: Christian L.
  email: christian.goueguel@gmail.com
repository: https://cran.r-universe.dev
repository-code: https://github.com/ChristianGoueguel/lwpls
commit: cded278d38e5411b3d2d33ca7d393e46aae4dd34
url: https://christiangoueguel.com/lwpls/
date-released: '2026-10-18'
contact:
- family-names: Goueguel
  given-names: Christian L.
  email: christian.goueguel@gmail.com