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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) <doi:10.48550/arXiv.2012.02102>.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | mlr3, mlr3learners, survival, gtools, tibble, dplyr, utils, coxme, missForest, R6 |
| Published: | 2022-07-18 |
| DOI: | 10.32614/CRAN.package.highMLR |
| Author: | Atanu Bhattacharjee [aut, cre, ctb], Gajendra K. Vishwakarma [aut, ctb], Souvik Banerjee [aut, ctb] |
| Maintainer: | Atanu Bhattacharjee <atanustat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | highMLR results |
| Reference manual: | highMLR.pdf |
| Package source: | highMLR_0.1.1.tar.gz |
| Windows binaries: | r-devel: highMLR_0.1.1.zip, r-release: highMLR_0.1.1.zip, r-oldrel: highMLR_0.1.1.zip |
| macOS binaries: | r-release (arm64): highMLR_0.1.1.tgz, r-oldrel (arm64): highMLR_0.1.1.tgz, r-release (x86_64): highMLR_0.1.1.tgz, r-oldrel (x86_64): highMLR_0.1.1.tgz |
| Old sources: | highMLR archive |
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