{ "_id": "6a4a663b1e9cffeb4b83399a", "Package": "GARS", "Type": "Package", "Date": "2020-09-04", "Title": "GARS: Genetic Algorithm for the identification of Robust Subsets\nof variables in high-dimensional and challenging datasets", "Version": "1.33.0", "Author": "Mattia Chiesa , Luca Piacentini\n", "Maintainer": "Mattia Chiesa ", "Description": "Feature selection aims to identify and remove redundant,\nirrelevant and noisy variables from high-dimensional datasets.\nSelecting informative features affects the subsequent\nclassification and regression analyses by improving their\noverall performances. Several methods have been proposed to\nperform feature selection: most of them relies on univariate\nstatistics, correlation, entropy measurements or the usage of\nbackward/forward regressions. Herein, we propose an efficient,\nrobust and fast method that adopts stochastic optimization\napproaches for high-dimensional. GARS is an innovative\nimplementation of a genetic algorithm that selects robust\nfeatures in high-dimensional and challenging datasets.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "RoxygenNote": "6.1.1", "biocViews": "Classification, FeatureExtraction, Clustering", "Config/pak/sysreqs": "cmake libfreetype6-dev libglpk-dev make default-jdk\nlibbz2-dev libicu-dev libjpeg-dev liblzma-dev libpng-dev\nlibuv1-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": { "Date": "2026-07-05 14:01:14 UTC", "User": "root" }, "_user": "bioc", "_type": "src", "_file": "GARS_1.33.0.tar.gz", "_fileid": "https://r2.ropensci.org/2d91100d099d8b480e9b5e865953277a0b0179d9b664c04815d71ebe67a3d760", "_filesize": 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