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:Atanu Bhattacharjee [aut, cre, ctb], Gajendra K. Vishwakarma [aut, ctb], Souvik Banerjee [aut, ctb]

highMLR_0.1.1.tar.gz
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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'))
Datasets:
  • hnscc - High dimensional head and neck cancer survival and gene expression data
  • srdata - High dimensional protein gene expression data

On CRAN:

Conda:

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

1.00 score 204 downloads 6 exports 51 dependencies

Last updated 3 years agofrom:f77a99ff94. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 09 2025
R-4.5-winOKMar 09 2025
R-4.5-macOKMar 09 2025
R-4.5-linuxOKMar 09 2025
R-4.4-winOKMar 09 2025
R-4.4-macOKMar 09 2025
R-4.4-linuxOKMar 09 2025
R-4.3-winOKMar 09 2025
R-4.3-macOKMar 09 2025

Exports:mlclassCoxmlclassKapmlhighCoxmlhighFrailmlhighHetmlhighKap

Dependencies:backportsbdsmatrixcheckmateclicodetoolscoxmedata.tabledigestdoRNGdplyrevaluatefansiforeachfuturefuture.applygenericsglobalsgluegtoolsiteratorsitertoolslatticelgrlifecyclelistenvmagrittrMatrixmissForestmlbenchmlr3mlr3learnersmlr3measuresmlr3miscnlmepalmerpenguinsparadoxparallellypillarpkgconfigPRROCR6randomForestrlangrngtoolssurvivaltibbletidyselectutf8uuidvctrswithr