codacore: Learning Sparse Log-Ratios for Compositional Data

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>
License: MIT + file LICENSE
NeedsCompilation: no
SystemRequirements: TensorFlow (
Citation: codacore citation info
Materials: README NEWS
CRAN checks: codacore results


Reference manual: codacore.pdf
Vignettes: my-vignette


Package source: codacore_0.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): codacore_0.0.4.tgz, r-oldrel (arm64): codacore_0.0.4.tgz, r-release (x86_64): codacore_0.0.4.tgz, r-oldrel (x86_64): codacore_0.0.4.tgz
Old sources: codacore archive


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