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: 2026-06-21 11:13:42 UTC; root
Author: Elliott Gordon-Rodriguez [aut, cre], Thomas Quinn [aut]
Maintainer: Elliott Gordon-Rodriguez <eg2912@columbia.edu>
Config/pak/sysreqs: libpng-dev python3
Repository: https://egr95.r-universe.dev
Date/Publication: 2022-08-29 08:30:02 UTC
RemoteUrl: https://github.com/cran/codacore
RemoteRef: HEAD
RemoteSha: 039d1b2767fbe6a243e970201f9ec0c34788a2b2