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: 0.0.3
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: 2022-01-07
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
CRAN checks: codacore results

Documentation:

Reference manual: codacore.pdf
Vignettes: my-vignette

Downloads:

Package source: codacore_0.0.3.tar.gz
Windows binaries: r-devel: codacore_0.0.3.zip, r-release: codacore_0.0.3.zip, r-oldrel: codacore_0.0.3.zip
macOS binaries: r-release (arm64): codacore_0.0.3.tgz, r-release (x86_64): codacore_0.0.3.tgz, r-oldrel: not available

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