Package: iterativeBMAsurv 1.63.0

Ka Yee Yeung

iterativeBMAsurv: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis

The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data.

Authors:Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA

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

Peer review:

Datasets:
  • testCens - Sample Test Data for the Iterative BMA Algorithm for Survival Analysis
  • testData - Sample Test Data for the Iterative BMA Algorithm for Survival Analysis
  • testSurv - Sample Test Data for the Iterative BMA Algorithm for Survival Analysis
  • trainCens - Sample Training Data for the Iterative BMA Algorithm for Survival Analysis
  • trainData - Sample Training Data for the Iterative BMA Algorithm for Survival Analysis
  • trainSurv - Sample Training Data for the Iterative BMA Algorithm for Survival Analysis

On BioConductor:iterativeBMAsurv-1.63.0(bioc 3.20)iterativeBMAsurv-1.62.0(bioc 3.19)

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

bioconductor-package

17 exports 1.08 score 11 dependencies 1 mentions

Last updated 2 months agofrom:d1b83b04ee

Exports:assignRiskGroupcrossValcrossVal.final.calccrossVal.foldcrossVal.initcrossVal.runcrossVal.tabulateimageplot.bma.modimageplot.iterate.bma.surviterateBMAinititerateBMAsurv.trainiterateBMAsurv.train.predict.assessiterateBMAsurv.train.wrapperpredictBicSurvpredictiveAssessCategoryprintTopGenessingleGeneCoxph

Dependencies:BMADEoptimRinlinelatticeleapsMatrixmvtnormpcaPProbustbaserrcovsurvival

The Iterative Bayesian Model Averaging Algorithm For Survival Analysis

Rendered fromiterativeBMAsurv.Rnwusingutils::Sweaveon Jul 03 2024.

Last update: 2013-11-01
Started: 2013-11-01