DOI: 10.18129/B9.bioc.ClassifyR    

A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Bioconductor version: Release (3.7)

The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework.

Author: Dario Strbenac, John Ormerod, Graham Mann, Jean Yang

Maintainer: Dario Strbenac <dario.strbenac at>

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biocViews Classification, Software, Survival
Version 2.0.4
In Bioconductor since BioC 3.0 (R-3.1) (3.5 years)
License GPL-3
Depends methods, S4Vectors(>= 0.18.0), MultiAssayExperiment(>= 1.6.0), BiocParallel
Imports locfit, grid, utils, plyr
Suggests limma, edgeR, car, Rmixmod, ggplot2 (>= 2.2.0), gridExtra (>= 2.0.0), BiocStyle, pamr, sparsediscrim, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, randomForest, robustbase, mlogit, mnlogit, glmnet
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