GARS
This is the development version of GARS; for the stable release version, see GARS.
GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets
Bioconductor version: Development (3.22)
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.
Author: Mattia Chiesa <mattia.chiesa at hotmail.it>, Luca Piacentini <luca.piacentini at cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa at hotmail.it>
citation("GARS")):
Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("GARS")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GARS")| GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets | R Script | |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | Classification, Clustering, FeatureExtraction, Software |
| Version | 1.29.0 |
| In Bioconductor since | BioC 3.7 (R-3.5) (7.5 years) |
| License | GPL (>= 2) |
| Depends | R (>= 3.5), ggplot2, cluster |
| Imports | DaMiRseq, MLSeq, stats, methods, SummarizedExperiment |
| System Requirements | |
| URL |
See More
| Suggests | BiocStyle, knitr, testthat |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | GARS_1.29.0.tar.gz |
| Windows Binary (x86_64) | GARS_1.29.0.zip |
| macOS Binary (x86_64) | GARS_1.29.0.tgz |
| macOS Binary (arm64) | GARS_1.29.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/GARS |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/GARS |
| Bioc Package Browser | https://code.bioconductor.org/browse/GARS/ |
| Package Short Url | https://bioconductor.org/packages/GARS/ |
| Package Downloads Report | Download Stats |