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:
codacore_0.0.4.tar.gz
codacore_0.0.4.zip(r-4.7)codacore_0.0.4.zip(r-4.6)codacore_0.0.4.zip(r-4.5)
codacore_0.0.4.tgz(r-4.6-any)codacore_0.0.4.tgz(r-4.5-any)
codacore_0.0.4.tar.gz(r-4.7-any)codacore_0.0.4.tar.gz(r-4.6-any)
codacore_0.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
codacore/json (API)
| # Install 'codacore' in R: |
| install.packages('codacore', repos = c('https://egr95.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:039d1b2767. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 152 | ||
| source / vignettes | OK | 215 | ||
| linux-release-x86_64 | OK | 160 | ||
| macos-release-arm64 | OK | 92 | ||
| macos-oldrel-arm64 | OK | 139 | ||
| windows-devel | OK | 175 | ||
| windows-release | OK | 135 | ||
| windows-oldrel | OK | 135 | ||
| wasm-release | OK | 106 |
Exports:activeInputs.codacorecodacoregetBinaryPartitionsgetDenominatorPartsgetLogRatiosgetNumeratorPartsgetNumLogRatiosgetSlopesgetTidyTableplotROCsimulateHTS
Dependencies:backportsbase64enccliconfiggenericsgluegtoolsherejsonlitekeraslatticelifecyclemagrittrMatrixpngpROCprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfrunstidyselectvctrswhiskerwithryamlzeallot
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| activeInputs | activeInputs.codacore |
| codacore | codacore |
| Microbiome composition related to Crohn`s disease study | Crohn |
| Metabolite relative abundances (Franzosa et al., 2019) | FranzosaMetabolite |
| Micriobiome relative abundances (Franzosa et al., 2019) | FranzosaMicrobiome |
| getBinaryPartitions | getBinaryPartitions |
| getDenominatorParts | getDenominatorParts |
| getLogRatios | getLogRatios |
| getNumeratorParts | getNumeratorParts |
| getNumLogRatios | getNumLogRatios |
| getSlopes | getSlopes |
| getTidyTable | getTidyTable |
| Microbiome, HIV infection and MSM factor | HIV |
| plot | plot.codacore |
| plotROC | plotROC |
| predict | predict.codacore |
| print.codacore | |
| Microbiome and sCD14 inflammation parameter | sCD14 |
| simulateHTS | simulateHTS |
