Package: codacore
Title: Learning Sparse Log-Ratios for Compositional Data
Version: 0.0.4
Authors@R: c(
    person("Elliott", "Gordon-Rodriguez", email = "eg2912@columbia.edu", role = c("aut", "cre")),
    person("Thomas", "Quinn", email = "contacttomquinn@gmail.com", role = c("aut"))
    )
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) <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.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 3.6.0)
Imports: tensorflow (>= 2.1), keras (>= 2.3), pROC (>= 1.17), R6 (>=
        2.5), gtools(>= 3.8)
SystemRequirements: TensorFlow (https://www.tensorflow.org/)
Suggests: zCompositions, testthat (>= 2.1.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-08-27 21:09:49 UTC; elliott
Author: Elliott Gordon-Rodriguez [aut, cre],
  Thomas Quinn [aut]
Maintainer: Elliott Gordon-Rodriguez <eg2912@columbia.edu>
Repository: CRAN
Date/Publication: 2022-08-29 09:30:02 UTC
Built: R 4.4.3; ; 2026-02-02 04:36:47 UTC; windows
