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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "codacore" in publications use:'
type: software
license: MIT
title: 'codacore: Learning Sparse Log-Ratios for Compositional Data'
version: 0.0.4
identifiers:
- type: doi
  value: 10.32614/CRAN.package.codacore
abstract: 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) <https://doi.org/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:
- family-names: Gordon-Rodriguez
  given-names: Elliott
  email: eg2912@columbia.edu
- family-names: Quinn
  given-names: Thomas
  email: contacttomquinn@gmail.com
preferred-citation:
  type: article
  title: Learning Sparse Log-Ratios For High-Throughput Sequencing Data
  authors:
  - family-names: Gordon-Rodriguez
    given-names: Elliott
    email: eg2912@columbia.edu
  - family-names: Quinn
    given-names: Thomas P.
  - family-names: Cunningham
    given-names: John P.
  journal: Bioinformatics
  year: '2021'
repository: https://egrodri.r-universe.dev
commit: 039d1b2767fbe6a243e970201f9ec0c34788a2b2
date-released: '2022-08-29'
contact:
- family-names: Gordon-Rodriguez
  given-names: Elliott
  email: eg2912@columbia.edu