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:

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