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) . 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.


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0.0.3 by Elliott Gordon-Rodriguez, 16 days ago

Browse source code at https://github.com/cran/codacore

Authors: Elliott Gordon-Rodriguez [aut, cre] , Thomas Quinn [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports tensorflow, keras, pROC, R6, gtools

Suggests zCompositions, testthat, knitr, rmarkdown

System requirements: TensorFlow (https://www.tensorflow.org/)

See at CRAN