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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 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_0.0.4.tar.gz
Windows binaries: r-devel: codacore_0.0.4.zip, r-release: codacore_0.0.4.zip, r-oldrel: codacore_0.0.4.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=codacore to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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