In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) <doi:10.1093/bioinformatics/btab645>. More generally, CoDaCoRe can be applied to any regression problem where the independent variable is Compositional (CoDa), to derive a set of scale-invariant log-ratios (ILR or SLR) that are maximally associated to a dependent variable.
Version: 0.0.4 Depends: R (≥ 3.6.0) Imports: tensorflow (≥ 2.1), keras (≥ 2.3), pROC (≥ 1.17), R6 (≥ 2.5), gtools (≥ 3.8) Suggests: zCompositions, testthat (≥ 2.1.0), knitr, rmarkdown Published: 2022-08-29 DOI: 10.32614/CRAN.package.codacore Author: Elliott Gordon-Rodriguez [aut, cre], Thomas Quinn [aut] Maintainer: Elliott Gordon-Rodriguez <eg2912 at columbia.edu> License: MIT + file LICENSE NeedsCompilation: no SystemRequirements: TensorFlow (https://www.tensorflow.org/) Citation: codacore citation info Materials: README, NEWS In views: CompositionalData CRAN checks: codacore results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=codacore to link to this page.
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