Package: highMLR 0.1.1
highMLR: Feature Selection for High Dimensional Survival Data
Perform high dimensional Feature Selection in the presence of survival outcome. Based on Feature Selection method and different survival analysis, it will obtain the best markers with optimal threshold levels according to their effect on disease progression and produce the most consistent level according to those threshold values. The functions' methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039> and Bhattacharjee et al (2021) <arxiv:2012.02102>.
Authors:
highMLR_0.1.1.tar.gz
highMLR_0.1.1.zip(r-4.5)highMLR_0.1.1.zip(r-4.4)highMLR_0.1.1.zip(r-4.3)
highMLR_0.1.1.tgz(r-4.4-any)highMLR_0.1.1.tgz(r-4.3-any)
highMLR_0.1.1.tar.gz(r-4.5-noble)highMLR_0.1.1.tar.gz(r-4.4-noble)
highMLR_0.1.1.tgz(r-4.4-emscripten)highMLR_0.1.1.tgz(r-4.3-emscripten)
highMLR.pdf |highMLR.html✨
highMLR/json (API)
| # Install 'highMLR' in R: |
| install.packages('highMLR', repos = c('https://atanubhattacharjee.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:f77a99ff94. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 09 2024 |
| R-4.5-win | OK | Nov 09 2024 |
| R-4.5-linux | OK | Nov 09 2024 |
| R-4.4-win | OK | Nov 09 2024 |
| R-4.4-mac | OK | Nov 09 2024 |
| R-4.3-win | OK | Nov 09 2024 |
| R-4.3-mac | OK | Nov 09 2024 |
Exports:mlclassCoxmlclassKapmlhighCoxmlhighFrailmlhighHetmlhighKap
Dependencies:backportsbdsmatrixcheckmateclicodetoolscoxmedata.tabledigestdoRNGdplyrevaluatefansiforeachfuturefuture.applygenericsglobalsgluegtoolsiteratorsitertoolslatticelgrlifecyclelistenvmagrittrMatrixmissForestmlbenchmlr3mlr3learnersmlr3measuresmlr3miscnlmepalmerpenguinsparadoxparallellypillarpkgconfigPRROCR6randomForestrlangrngtoolssurvivaltibbletidyselectutf8uuidvctrswithr
