Package: GARS
Type: Package
Date: 2020-09-04
Title: GARS: Genetic Algorithm for the identification of Robust Subsets
        of variables in high-dimensional and challenging datasets
Version: 1.30.0
Author: Mattia Chiesa <mattia.chiesa@hotmail.it>, Luca Piacentini
        <luca.piacentini@cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa@hotmail.it>
Description: Feature selection aims to identify and remove redundant,
        irrelevant and noisy variables from high-dimensional datasets.
        Selecting informative features affects the subsequent
        classification and regression analyses by improving their
        overall performances. Several methods have been proposed to
        perform feature selection: most of them relies on univariate
        statistics, correlation, entropy measurements or the usage of
        backward/forward regressions. Herein, we propose an efficient,
        robust and fast method that adopts stochastic optimization
        approaches for high-dimensional. GARS is an innovative
        implementation of a genetic algorithm that selects robust
        features in high-dimensional and challenging datasets.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 6.1.1
biocViews: Classification, FeatureExtraction, Clustering
Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment
Suggests: BiocStyle, knitr, testthat
Depends: R (>= 3.5), ggplot2, cluster
Config/pak/sysreqs: cmake libglpk-dev make default-jdk libbz2-dev
        libicu-dev libjpeg-dev liblzma-dev libpng-dev libxml2-dev
        libssl-dev
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2025-10-29 14:43:37 UTC
RemoteUrl: https://github.com/bioc/GARS
RemoteRef: RELEASE_3_22
RemoteSha: c28c6e6d1bbc8bb7f833ac4b79f508e55cdb1a77
NeedsCompilation: no
Packaged: 2025-11-11 18:55:41 UTC; root
Built: R 4.5.2; ; 2025-11-11 19:06:02 UTC; windows
