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 |
| In views: |
CompositionalData |
| CRAN checks: |
codacore results |
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