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codacore
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]
[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 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:27), 0.0.3 (2022-01-07 11:10)
Other packages that cited codacore R package
View codacore citation profile
Other R packages that codacore depends,
imports, suggests or enhances
Complete documentation for codacore
Functions, R codes and Examples using
the codacore R package
Some associated functions: Crohn . FranzosaMetabolite . FranzosaMicrobiome . HIV . activeInputs.codacore . codacore . getBinaryPartitions . getDenominatorParts . getLogRatios . getNumLogRatios . getNumeratorParts . getSlopes . getTidyTable . plot.codacore . plotROC . predict.codacore . print.codacore . sCD14 . simulateHTS .
Some associated R codes: codacore.R . data.R . data_stash.R . simulations.R . Full codacore package functions and examples
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