Name Mode Size
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figures 040000
ClassifyResult-class.Rd 100644 5 kb
CrossValParams-class.Rd 100644 3 kb
FeatureSetCollection.Rd 100644 3 kb
HuRI.Rd 100644 1 kb
ModellingParams-class.Rd 100644 2 kb
PredictParams-class.Rd 100644 2 kb
ROCplot.Rd 100644 5 kb
SelectParams-class.Rd 100644 3 kb
TrainParams-class.Rd 100644 3 kb
TransformParams-class.Rd 100644 2 kb
asthma.Rd 100644 1 kb
available.Rd 100644 1 kb
calcPerformance.Rd 100644 6 kb
colCoxTests.Rd 100644 1 kb
crissCrossPlot.Rd 100644 1 kb
crissCrossValidate.Rd 100644 3 kb
crossValidate.Rd 100644 9 kb
distribution.Rd 100644 2 kb
edgesToHubNetworks.Rd 100644 2 kb
featureSetSummary.Rd 100644 4 kb
interactorDifferences.Rd 100644 3 kb
performancePlot.Rd 100644 6 kb
plotFeatureClasses.Rd 100644 9 kb
prepareData.Rd 100644 3 kb
rankingPlot.Rd 100644 7 kb
runTest.Rd 100644 4 kb
runTests.Rd 100644 3 kb
samplesMetricMap.Rd 100644 7 kb
selectionPlot.Rd 100644 9 kb
README.md
# ClassifyR: Performance evaluation for multi-view data sets and seamless integration with MultiAssayExperiment and Bioconductor <img src="man/figures/ClassifyRsticker.png" align="right" width=250 style="margin-left: 10px;"> ClassifyR's performance evaluation focuses on model stability and interpretability. Based on repeated cross-validation, it is possible to evaluate feature selection stability and also per-sample prediction accuracy. Also, multiple omics data assays on the same samples are becoming more popular and ClassifyR supports a range of multi-view methods to evaluate which data view is the most predictive and combine data views to evaluate if multiple views provide superior predictive performance to a single data view. ## Installation The recommended method of installing ClassifyR is by using Bioconductor's BiocManager installer: ``` library(BiocManager) install("ClassifyR", dependencies = TRUE) ``` The above code will install all packages that provide feature selection or model-building functionality. If only one or two methods are desired then the dependencies option could be omitted and those packages providing functionality installed manually. ## Website Please visit [the ClassifyR website](https://sydneybiox.github.io/ClassifyR/) to view the main vignette as well as articles that provide more in-depth explanations for various aspects of the package. Details of performance evaluation, multi-view methods and contributing a wrapper for a new algorithm to the package are provided. ## Reference Strbenac D., Mann, G.J., Ormerod, J.T., and Yang, J. Y. H. (2015) ClassifyR: An R package for performance assessment of classification with applications to transcriptomics, *Bioinformatics*.