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:Mizanur Khondoker <[email protected]>

optBiomarker_1.0-28.tar.gz
optBiomarker_1.0-28.zip(r-4.5)optBiomarker_1.0-28.zip(r-4.4)optBiomarker_1.0-28.zip(r-4.3)
optBiomarker_1.0-28.tgz(r-4.4-any)optBiomarker_1.0-28.tgz(r-4.3-any)
optBiomarker_1.0-28.tar.gz(r-4.5-noble)optBiomarker_1.0-28.tar.gz(r-4.4-noble)
optBiomarker_1.0-28.tgz(r-4.4-emscripten)optBiomarker_1.0-28.tgz(r-4.3-emscripten)
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'))

Peer review:

Datasets:
  • 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

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

14 exports 0.00 score 60 dependencies 1 scripts 215 downloads

Last updated 4 years agofrom:468d5a83aa. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 30 2024
R-4.5-winOKAug 30 2024
R-4.5-linuxOKAug 30 2024
R-4.4-winOKAug 30 2024
R-4.4-macOKAug 30 2024
R-4.3-winOKAug 30 2024
R-4.3-macOKAug 30 2024

Exports:avg2LowerclassificationErrorclassificationError.data.frameclassificationError.defaultclasspredict.knnclasspredict.ldadimSelectmatapproxobjFunoptimiseBiomarkerplot3dFunsimDatatnormAvgyapprox

Dependencies:base64encbslibcachemclassclicodetoolsdata.tablediagramdigeste1071evaluateexpmfastmapfontawesomefsfuturefuture.applyglobalsgluehighrhtmltoolshtmlwidgetsipredjquerylibjsonliteKernSmoothknitrlatticelavalifecyclelistenvmagrittrMASSMatrixmemoisemimemsmmvtnormnnetnumDerivparallellyprodlimprogressrproxyR6randomForestrappdirsRcpprglrlangrmarkdownrpanelrpartsassshapeSQUAREMsurvivaltinytexxfunyaml