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codacore  

Learning Sparse Log-Ratios for Compositional Data
View on CRAN: Click here


Download and install codacore package within the R console
Install from CRAN:
install.packages("codacore")

Install from Github:
library("remotes")
install_github("cran/codacore")

Install by package version:
library("remotes")
install_version("codacore", "0.0.4")



Attach the package and use:
library("codacore")
Maintained by
Elliott Gordon-Rodriguez
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-01-07
Latest Update: 2022-08-29
Description:
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.
How to cite:
Elliott Gordon-Rodriguez (2022). codacore: Learning Sparse Log-Ratios for Compositional Data. R package version 0.0.4, https://cran.r-project.org/web/packages/codacore. Accessed 12 Jun. 2026.
Previous versions and publish date:
0.0.3 (2022-01-07 11:10)
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Complete documentation for codacore
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