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: 1.33.0
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 libfreetype6-dev libglpk-dev make default-jdk
        libbz2-dev libicu-dev libjpeg-dev liblzma-dev libpng-dev
        libuv1-dev libxml2-dev libssl-dev xz-utils zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:47:23 UTC
RemoteUrl: https://github.com/bioc/GARS
RemoteRef: HEAD
RemoteSha: b89d2be6409b9731a436779ff9d70508c9bde095
NeedsCompilation: no
Packaged: 2026-07-05 14:01:14 UTC; root