Package: BootMRMR 0.1

BootMRMR: Bootstrap-MRMR Technique for Informative Gene Selection

Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).

Authors:Samarendra Das <[email protected]>

BootMRMR_0.1.tar.gz
BootMRMR_0.1.zip(r-4.5)BootMRMR_0.1.zip(r-4.4)BootMRMR_0.1.zip(r-4.3)
BootMRMR_0.1.tgz(r-4.4-any)BootMRMR_0.1.tgz(r-4.3-any)
BootMRMR_0.1.tar.gz(r-4.5-noble)BootMRMR_0.1.tar.gz(r-4.4-noble)
BootMRMR_0.1.tgz(r-4.4-emscripten)BootMRMR_0.1.tgz(r-4.3-emscripten)
BootMRMR.pdf |BootMRMR.html✨
BootMRMR/json (API)

# Install 'BootMRMR' in R:
install.packages('BootMRMR', repos = c('https://samarendra88.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • rice_salt - A gene expression dataset of rice under salinity stress

On CRAN:

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

1.65 score 1 packages 15 scripts 178 downloads 12 exports 0 dependencies

Last updated 8 years agofrom:463f01236b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-winOKNov 14 2024
R-4.5-linuxOKNov 14 2024
R-4.4-winOKNov 14 2024
R-4.4-macOKNov 14 2024
R-4.3-winOKNov 14 2024
R-4.3-macOKNov 14 2024

Exports:bmrmr.pval.cutoffbmrmr.weight.cutoffbootmr.weightgeneslect.fmbmr.pval.cutoffmbmr.weight.cutoffmrmr.cutoffpval.bmrmrpval.mbmrtopsis.methweight.mbmrWeights.mrmr

Dependencies: