Package: biospear 1.0.2
biospear: Biomarker Selection in Penalized Regression Models
Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006) <doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al. (2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso (Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others. A function generating simulated survival data set is also provided.
Authors:
biospear_1.0.2.tar.gz
biospear_1.0.2.zip(r-4.5)biospear_1.0.2.zip(r-4.4)
biospear_1.0.2.tgz(r-4.4-any)
biospear_1.0.2.tar.gz(r-4.5-noble)biospear_1.0.2.tar.gz(r-4.4-noble)
biospear_1.0.2.tgz(r-4.4-emscripten)
biospear.pdf |biospear.html✨
biospear/json (API)
# Install 'biospear' in R: |
install.packages('biospear', repos = c('https://stefanmichielsgr.r-universe.dev', 'https://cloud.r-project.org')) |
- Breast - Early breast cancer data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:45bd46eaa0. Checks:OK: 5. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 26 2024 |
R-4.5-win | OK | Oct 26 2024 |
R-4.5-linux | OK | Oct 26 2024 |
R-4.4-win | OK | Oct 26 2024 |
R-4.4-mac | OK | Oct 26 2024 |
Exports:BMselexpSurvpredResselRessimdatasimdataV
Dependencies:abindaskpassbackportsbase64encBHBiocParallelbipartitebitopsbootbootstrapbrewbriobroombslibcachemcallrcarcarDatacheckmateclassclicliprclustercobscodacodetoolscolorspacecommonmarkcorpcorcowplotcpp11crayoncredentialscurldata.tableDerivdescdevtoolsdiagramdiffobjdigestdoBydotCall64downlitdplyrellipseellipsisevaluatefansifarverfastmapfieldsfontawesomeforeachforeignformatRFormulafsfutile.loggerfutile.optionsfuturefuture.applygenericsgertggplot2ggrepelghgitcredsglmnetglobalsgluegridExtragrplassogsignalgtablehighrHmischtmlTablehtmltoolshtmlwidgetshttpuvhttr2igraphiniinumipredisobanditeratorsjquerylibjsonlitekernlabKernSmoothknitrlabelinglambda.rlarslaterlatticelavalibcoinlifecyclelistenvlme4magrittrmapsMASSMatrixMatrixModelsmatrixStatsmboostmemoisemgcvmicrobenchmarkmimeminiUIminqamixOmicsmodelrmultcompmunsellmvtnormnetworknlmenloptrnnetnnlsnumDerivopensslparallellypartykitpbkrtestpermutepillarpkgbuildpkgconfigpkgdownpkgloadplsplsRcoxplsRglmplyrpolsplinepracmapraiseprettyunitspROCprocessxprodlimprofvisprogressrpromisesPRROCpspurrrquadprogquantregR6raggrappdirsrARPACKrcmdcheckRColorBrewerRcppRcppEigenRCurlrematch2remotesreshape2rglrisksetROCrlangrmarkdownrmetarmsroxygen2rpartrprojrootRSpectrarstudioapirversionssandwichsassscalessessioninfoshapeshinysnasnowsourcetoolsspamSparseMSQUAREMstabsstatnet.commonstringistringrSuppDistssurvAUCsurvcompsurvivalsurvivalROCsyssystemfontstestthattextshapingTH.datatibbletidyrtidyselecttinytexurlcheckerusethisutf8vctrsveganviridisviridisLitewaldowhiskerwithrxfunxml2xopenxtableyamlzipzoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Biomarker selection in a Cox regression model | BMsel summary.resBMsel |
Early breast cancer data | Breast |
Computation of expected survival based on a prediction model | expSurv plot.resexpSurv predict.resexpSurv |
Evaluation of the prediction accuracy of a prediction model | plot.predRes predRes |
Evaluation of the selection accuracy of a prediction model | selRes |
Generation of data sets with survival outcome | simdata simdataV |