Package: gomp 1.1

Michail Tsagris

gomp: The gamma-OMP Feature Selection Algorithm

The gamma-Orthogonal Matching Pursuit (gamma-OMP) is a recently suggested modification of the OMP feature selection algorithm for a wide range of response variables. The package offers many alternative regression models, such linear, robust, survival, multivariate etc., including k-fold cross-validation. References: Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. <doi:10.1101/431734>. Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214--1224. <doi:10.1109/TCBB.2020.3029952>.

Authors:Michail Tsagris [aut, cre]

gomp_1.1.tar.gz
gomp_1.1.tar.gz(r-4.7-any)gomp_1.1.tar.gz(r-4.6-any)
gomp_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
gomp/json (API)

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

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 7 scripts 468 downloads 6 exports 76 dependencies

Last updated from:adcb2f939d. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK185
source / vignettesOK172
linux-release-x86_64OK172
wasm-releaseOK153

Exports:bbcboot.gompcv.gompgompgomp.pathmakefolds

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecpp11data.tabledigestdoParallelevaluatefarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmemoisemimenlmennetnumDerivordinalquantregR6rangenrappdirsRColorBrewerRcppRcppArmadilloRcppParallelRfastrlangrmarkdownrpartrstudioapiS7sassscalesSparseMstringistringrsurvivaltinytexucminfvctrsviridisLitewithrxfunyamlzigg