DESCRIPTION

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