The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
Please use the canonical form https://CRAN.R-project.org/package=highMLR to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.