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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.

Versions across snapshots

VersionRepositoryFileSize
0.0.4 rolling linux/jammy R-4.5 codacore_0.0.4.tar.gz 1.3 MiB
0.0.4 rolling linux/noble R-4.5 codacore_0.0.4.tar.gz 1.3 MiB
0.0.4 rolling source/ R- codacore_0.0.4.tar.gz 1.2 MiB
0.0.4 latest linux/jammy R-4.5 codacore_0.0.4.tar.gz 1.3 MiB
0.0.4 latest linux/noble R-4.5 codacore_0.0.4.tar.gz 1.3 MiB
0.0.4 latest source/ R- codacore_0.0.4.tar.gz 1.2 MiB
0.0.4 2026-04-26 source/ R- codacore_0.0.4.tar.gz 1.2 MiB
0.0.4 2026-04-23 source/ R- codacore_0.0.4.tar.gz 1.2 MiB
0.0.4 2026-04-09 windows/windows R-4.5 codacore_0.0.4.zip 1.4 MiB
0.0.4 2025-04-20 source/ R- codacore_0.0.4.tar.gz 1.2 MiB

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