DESCRIPTION

Package: GARS
Type: Package
Date: 2020-04-14
Title: GARS: Genetic Algorithm for the identification
 of Robust Subsets of variables in high-dimensional and challenging
 datasets
Version: 1.7.2
Author: Mattia Chiesa <mattia.chiesa@cardiologicomonzino.it>, 
    Luca Piacentini <luca.piacentini@cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa@cardiologicomonzino.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