Package: optBiomarker 1.0-28
optBiomarker: Estimation of Optimal Number of Biomarkers for Two-Group Microarray Based Classifications at a Given Error Tolerance Level for Various Classification Rules
Estimates optimal number of biomarkers for two-group classification based on microarray data.
Authors:
optBiomarker_1.0-28.tar.gz
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optBiomarker.pdf |optBiomarker.html✨
optBiomarker/json (API)
| # Install 'optBiomarker' in R: |
| install.packages('optBiomarker', repos = c('https://mkhondoker.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mkhondoker/optbiomarker/issues
- errorDbase - Database of leave-one-out cross validation errors for various combinations of data characteristics
- realBiomarker - A set of 54359 median gene expressions in log (base 2) scale
Last updated 4 years agofrom:468d5a83aa. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 29 2024 |
| R-4.5-win | OK | Oct 29 2024 |
| R-4.5-linux | OK | Oct 29 2024 |
| R-4.4-win | OK | Oct 29 2024 |
| R-4.4-mac | OK | Oct 29 2024 |
| R-4.3-win | OK | Oct 29 2024 |
| R-4.3-mac | OK | Oct 29 2024 |
Exports:avg2LowerclassificationErrorclassificationError.data.frameclassificationError.defaultclasspredict.knnclasspredict.ldadimSelectmatapproxobjFunoptimiseBiomarkerplot3dFunsimDatatnormAvgyapprox
Dependencies:base64encbslibcachemclassclicodetoolsdata.tablediagramdigeste1071evaluateexpmfansifastmapfontawesomefsfuturefuture.applygenericsglobalsgluehighrhtmltoolshtmlwidgetsipredjquerylibjsonliteKernSmoothknitrlatticelavalifecyclelistenvmagrittrMASSMatrixmemoisemimemsmmvtnormnnetnumDerivparallellypillarpkgconfigprodlimprogressrproxyR6randomForestrappdirsRcpprglrlangrmarkdownrpanelrpartsassshapeSQUAREMsurvivaltibbletinytexutf8vctrsxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| R package for estimating optimal number of biomarkers at a given error tolerance level for various classification rules | optBiomarker-package optBiomarker |
| Estimation of misclassification errors (generalisation errors) based on statistical and various machine learning methods | classificationError classificationError.data.frame classificationError.default |
| Database of leave-one-out cross validation errors for various combinations of data characteristics | errorDbase |
| Estimates optimal number of biomarkers at a given error tolerance level for various classification rules | optimiseBiomarker |
| A set of 54359 median gene expressions in log (base 2) scale | realBiomarker |
| Simulation of microarray data | simData |
