Package: codacore 0.0.4

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.

Authors:Elliott Gordon-Rodriguez [aut, cre], Thomas Quinn [aut]

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NEWS

# Install 'codacore' in R:
install.packages('codacore', repos = c('https://egr95.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • Crohn - Microbiome composition related to Crohn's disease study
  • HIV - Microbiome, HIV infection and MSM factor
  • sCD14 - Microbiome and sCD14 inflammation parameter

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 2 scripts 187 downloads 11 exports 36 dependencies

Last updated 3 years agofrom:039d1b2767. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 30 2025
R-4.5-winOKMar 30 2025
R-4.5-macOKMar 30 2025
R-4.5-linuxOKMar 30 2025
R-4.4-winOKMar 30 2025
R-4.4-macOKMar 30 2025
R-4.4-linuxOKMar 30 2025
R-4.3-winOKMar 30 2025
R-4.3-macOKMar 30 2025

Exports:activeInputs.codacorecodacoregetBinaryPartitionsgetDenominatorPartsgetLogRatiosgetNumeratorPartsgetNumLogRatiosgetSlopesgetTidyTableplotROCsimulateHTS

Dependencies:backportsbase64enccliconfiggenericsgluegtoolsherejsonlitekeraslatticelifecyclemagrittrMatrixplyrpngpROCprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfrunstidyselectvctrswhiskerwithryamlzeallot

CoDaCoRe guide

Rendered fromguide.Rmdusingknitr::rmarkdownon Mar 30 2025.

Last update: 2022-08-29
Started: 2022-01-07