Man pages for exprso
Rapid Deployment of Machine Learning Algorithms

arraySample ExprsBinary Data
arrayExprsImport Data as ExprsArray
arrayMultiSample ExprsMulti Data
buildBuild Models
build.Workhorse for build Methods
buildANNBuild Artificial Neural Network Model
buildDNNBuild Deep Neural Network Model
buildDTBuild Decision Tree Model
buildEnsembleBuild Ensemble
buildFRBBuild Fuzzy Rule Based Model
buildGLMBuild Generalized Linear Model
buildLASSOBuild LASSO or Ridge Model
buildLDABuild Linear Discriminant Analysis Model
buildLMBuild Linear Model
buildLRBuild Logistic Regression Model
buildNBBuild Naive Bayes Model
buildRFBuild Random Forest Model
buildSVMBuild Support Vector Machine Model
calcMonteCarloCalculate 'plMonteCarlo' Performance
calcNestedCalculate 'plNested' Performance
calcStatsCalculate Model Performance
check.ctrlGSCheck 'ctrlGS' Arguments
classCheckClass Check
compareCompare 'ExprsArray' Objects
conjoinCombine 'exprso' Objects
ctrlFeatureSelectManage 'fs' Arguments
ctrlGridSearchManage 'plGrid' Arguments
ctrlModSetManage 'mod' Arguments
ctrlSplitSetManage 'split' Arguments
defaultArgSet an args List Element to Default Value
doMultiPerform Multiple "1 vs. all" Tasks
ExprsArray-classAn S4 class to store feature and annotation data
ExprsBinary-classAn S4 class to store feature and annotation data
ExprsEnsemble-classAn S4 class to store multiple models
ExprsMachine-classAn S4 class to store the model
ExprsModel-classAn S4 class to store the model
ExprsModule-classAn S4 class to store the model
ExprsMulti-classAn S4 class to store feature and annotation data
exprsoThe 'exprso' Package
exprso-predictDeploy Model
ExprsPipeline-classAn S4 class to store models built during high-throughput...
ExprsPredict-classAn S4 class to store model predictions
forceArgForce an args List Element to Value
fsSelect Features
fs.Workhorse for fs Methods
fsANOVASelect Features by ANOVA
fsBalanceConvert Features into Balances
fsCorSelect Features by Correlation
fsEbayesSelect Features by Moderated t-test
fsEdgerSelects Features by Exact Test
fsIncludeSelect Features by Explicit Reference
fsMrmreSelect Features by mRMR
fsNULLNull Feature Selection
fsPCAReduce Dimensions by PCA
fsPrcompReduce Dimensions by PCA
fsRankProdSelect Features by Rank Product Analysis
fsRDAReduce Dimensions by RDA
fsSampleSelect Features by Random Sampling
fsStatsSelect Features by Statistical Testing
getArgsBuild an args List
getFeaturesRetrieve Feature Set
getWeightsRetrieve LASSO Weights
GSE2eSetConvert GSE to eSet
makeGridFromArgsBuild Argument Grid
modProcess Data
modAcompCompositionally Constrain Data
modCLRLog-ratio Transform Data
modClusterCluster Subjects
modFilterHard Filter Data
modHistoryReplicate Data Process History
modIncludeSelect Features from Data
modNormalizeNormalize Data
modPermutePermute Features in Data
modRatiosRecast Data as Feature (Log-)Ratios
modSampleSample Features from Data
modScaleScale Data by Factor Range
modSkewSkew Data by Factor Range
modSubsetTidy Subset Wrapper
modSwapSwap Case Subjects
modTMMNormalize Data
modTransformLog Transform Data
nfeatsGet Number of Features
nsampsGet Number of Samples
packageCheckPackage Check
pipeProcess Pipelines
pipeFilterFilter 'ExprsPipeline' Object
pipeUnbootRename "boot" Column
plDeploy Pipeline
plCVPerform Simple Cross-Validation
plGridPerform High-Throughput Machine Learning
plGridMultiPerform High-Throughput Multi-Class Classification
plMonteCarloMonte Carlo Cross-Validation
plNestedNested Cross-Validation
progressMake Progress Bar
RegrsArray-classAn S4 class to store feature and annotation data
RegrsModel-classAn S4 class to store the model
RegrsPredict-classAn S4 class to store model predictions
reRankSerialize "1 vs. all" Feature Selection
splitSplit Data
splitBalancedSplit by Balanced Sampling
splitBoostSample by Boosting
splitBySplit by User-defined Group
splitSampleSplit by Random Sampling
splitStratifySplit by Stratified Sampling
trainingSetExtract Training Set
validationSetExtract Validation Set
exprso documentation built on May 1, 2019, 7:11 p.m.