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: 2026-06-18 20:49:14 UTC; josha
Repository: CRAN
Date/Publication: 2026-06-24 08:20:07 UTC
