Package: gaQSAR
Type: Package
Title: QSAR Modelling Using Genetic Algorithm Based Variable Selection
Version: 1.2.3
Authors@R: person("Jos", "Hageman", email = "jos.hageman@wur.nl", role = c("aut", "cre"))
Description: 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) <doi:10.1038/s41598-018-27545-4> and Kalli et al. (2021)
        <doi:10.1038/s41598-021-92964-9>.
License: GPL-3
Encoding: UTF-8
Imports: GA, future, future.apply, ggplot2, ggrepel, stats, scales,
        prospectr, reshape2
Suggests: knitr, rmarkdown, QSARdata
VignetteBuilder: knitr
URL: https://github.com/joshageman/gaQSAR
BugReports: https://github.com/joshageman/gaQSAR/issues
NeedsCompilation: no
Author: Jos Hageman [aut, cre]
Maintainer: Jos Hageman <jos.hageman@wur.nl>
Config/roxygen2/version: 8.0.0
Packaged: 2026-06-24 13:25:45 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2026-06-24 08:20:07 UTC
RemoteUrl: https://github.com/cran/gaQSAR
RemoteRef: HEAD
RemoteSha: fca1d03eed57c6cdf2e6d8b17e9d9ed9b7be5021