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ClassifyR (version 1.2.4)

A framework for two-class classification problems, with applications to differential variability and differential distribution testing.

Description

The software formalises a framework for classification in R. There are four stages. Data transformation, feature selection, 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 does a couple of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, if they have better performing methods.

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Version

Version

1.2.4

License

GPL-3

Maintainer

Dario Strbenac

Last Published

February 15th, 2017

Functions in ClassifyR (1.2.4)

getLocationsAndScales

Calculate Location and Scale
TrainParams

Parameters for Classifier Training
TransformParams

Parameters for Data Transformation
functionOrList

Union of Functions and List of Functions
nearestShrunkenCentroidSelectionInterface

Interface for pamr.listgenes Function from pamr CRAN Package
nearestShrunkenCentroidTrainInterface

Interface for pamr.train Function from pamr CRAN Package
naiveBayesKernel

Classification Using A Bayes Classifier with Kernel Density Estimates
rankPlot

Plot Pair-wise Overlap of Ranked Features
nearestShrunkenCentroidPredictInterface

Interface for pamr.predict Function from pamr CRAN Package
ResubstituteParams

Parameters for Resubstitution Error Calculation
DMDselection

Selection of Differential Distributions with Kullback Leibler Distance
edgeRselection

Feature Selection Based on Differential Expression for RNA-seq
leveneSelection

Selection of Differential Variability with Levene Statistic
likelihoodRatioSelection

Selection of Differential Distributions with Likelihood Ratio Statistic
plotFeatureClasses

Plot Density and Scatterplot for Genes By Class
PredictParams

Parameters for Classifier Prediction
calcPerformance

Add Performance Calculations to a ClassifyResult object
classifyInterface

Interface for PoiClaClu Package's Classify Function
mixmodels

Selection of Differential Distributions with Mixtures of Normals
ROCplot

Plot Receiver Operating Curve Graphs for Classification Results
limmaSelection

Selection of Differentially Expressed Features
selectionPlot

Plot Pair-wise Overlap of Selected Features
runTest

Perform a Single Classification
subtractFromLocation

Subtract All Feature Measurements from Location
ClassifyResult

Container for Storing Classification Results
errorMap

Plot a Grid of Sample Error Rates
runTests

Reproducibly Do Resampling or Leave Out and Cross Validation
fisherDiscriminant

Classification Using Fisher's LDA
distribution

Get Frequencies of Feature Selection and Sample Errors
KolmogorovSmirnovSelection

Selection of Differential Distributions with Kolmogorov Smirnov Distance
pamrtrained

Trained pamr Object
KullbackLeiblerSelection

Selection of Differential Distributions with Kullback Leibler Distance
performancePlot

Plot Performance Measures for Various Classifications
SelectionParams

Parameters for Feature Selection