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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Last updated 2 years agofrom:039d1b2767. Checks:OK: 7. Indexed: yes.

Exports:activeInputs.codacorecodacoregetBinaryPartitionsgetDenominatorPartsgetLogRatiosgetNumeratorPartsgetNumLogRatiosgetSlopesgetTidyTableplotROCsimulateHTS

Dependencies:backportsbase64enccliconfiggenericsgluegtoolsherejsonlitekeraslatticelifecyclemagrittrMatrixplyrpngpROCprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfrunstidyselectvctrswhiskerwithryamlzeallot