Package: iterativeBMA 1.65.0

Ka Yee Yeung

iterativeBMA: The Iterative Bayesian Model Averaging (BMA) algorithm

The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402).

Authors:Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter

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

Peer review:

Datasets:
  • testClass - Sample Test Data for the Iterative BMA Algorithm
  • testData - Sample Test Data for the Iterative BMA Algorithm
  • trainClass - Sample Training Data for the Iterative BMA Algorithm
  • trainData - Sample Training Data for the Iterative BMA Algorithm

On BioConductor:iterativeBMA-1.65.0(bioc 3.21)iterativeBMA-1.64.0(bioc 3.20)

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

microarrayclassification

3.78 score 1 scripts 262 downloads 3 mentions 8 exports 14 dependencies

Last updated 2 months agofrom:00a6f3f4f5. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 27 2024
R-4.5-winNOTENov 27 2024
R-4.5-linuxNOTENov 27 2024
R-4.4-winNOTENov 27 2024
R-4.4-macNOTENov 27 2024
R-4.3-winNOTENov 27 2024
R-4.3-macNOTENov 27 2024

Exports:bma.predictbrier.scoreBssWssFastimageplot.iterate.bmaiterateBMAglm.trainiterateBMAglm.train.predictiterateBMAglm.train.predict.testiterateBMAglm.wrapper

Dependencies:BiobaseBiocGenericsBMADEoptimRgenericsinlinelatticeleapsMatrixmvtnormpcaPProbustbaserrcovsurvival

The Iterative Bayesian Model Averaging Algorithm

Rendered fromiterativeBMA.Rnwusingutils::Sweaveon Nov 27 2024.

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