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# CITATION.cff file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "gaQSAR" in publications use:'
type: software
license: GPL-3.0-only
title: 'gaQSAR: QSAR Modelling Using Genetic Algorithm Based Variable Selection'
version: 1.2.3
doi: 10.32614/CRAN.package.gaQSAR
abstract: Implements genetic algorithm-based variable selection for building quantitative
  structure-activity relationship (QSAR) models. The package provides a workflow for
  selecting optimal predictor subsets from large descriptor spaces using leave-one-out
  cross-validation (LOOCV) with Q2 as the fitness criterion. Features include automatic
  handling of multicollinearity via variance inflation factor (VIF) thresholding,
  customizable genetic algorithm operators, and diagnostic tools for model evaluation.
  Supports both training set optimization and external validation, plus nested (double)
  cross-validation for unbiased performance estimation and predictor stability diagnostics.
  Built-in visualization functions include Q2 curves and Williams plots to assess
  model applicability domain. The method is demonstrated in papers predicting antibacterial
  activity by Araya-Cloutier et al. (2018) <https://doi.org/10.1038/s41598-018-27545-4>
  and Kalli et al. (2021) <https://doi.org/10.1038/s41598-021-92964-9>.
authors:
- family-names: Hageman
  given-names: Jos
  email: jos.hageman@wur.nl
repository: https://joshageman.r-universe.dev
repository-code: https://github.com/joshageman/gaQSAR
commit: 9e841fb214f1eb42e1760f9cb9132e1394c2df63
url: https://github.com/joshageman/gaQSAR
date-released: '2026-06-25'
contact:
- family-names: Hageman
  given-names: Jos
  email: jos.hageman@wur.nl