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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.
Getting started
Browse package contents
Package details |
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| Author | Elliott Gordon-Rodriguez [aut, cre], Thomas Quinn [aut] |
| Maintainer | Elliott Gordon-Rodriguez <eg2912@columbia.edu> |
| License | MIT + file LICENSE |
| Version | 0.0.4 |
| Package repository | View on CRAN |
| Installation |
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