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 + fileLICENSE |
| NeedsCompilation: | no |
| SystemRequirements: | TensorFlow (https://www.tensorflow.org/) |
| Citation: | codacore citation info |
| Materials: | README,NEWS |
| In views: | CompositionalData |
| CRAN checks: | codacore results |
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