ExprsArray-class        An S4 class to store feature and annotation
                        data
ExprsBinary-class       An S4 class to store feature and annotation
                        data
ExprsEnsemble-class     An S4 class to store multiple models
ExprsMachine-class      An S4 class to store the model
ExprsModel-class        An S4 class to store the model
ExprsModule-class       An S4 class to store the model
ExprsMulti-class        An S4 class to store feature and annotation
                        data
ExprsPipeline-class     An S4 class to store models built during
                        high-throughput learning
ExprsPredict-class      An S4 class to store model predictions
GSE2eSet                Convert GSE to eSet
RegrsArray-class        An S4 class to store feature and annotation
                        data
RegrsModel-class        An S4 class to store the model
RegrsPredict-class      An S4 class to store model predictions
array                   Sample ExprsBinary Data
arrayExprs              Import Data as ExprsArray
arrayMulti              Sample ExprsMulti Data
build                   Build Models
build.                  Workhorse for build Methods
buildANN                Build Artificial Neural Network Model
buildDNN                Build Deep Neural Network Model
buildEnsemble           Build Ensemble
buildLDA                Build Linear Discriminant Analysis Model
buildNB                 Build Naive Bayes Model
buildRF                 Build Random Forest Model
buildSVM                Build Support Vector Machine Model
calcMonteCarlo          Calculate 'plMonteCarlo' Performance
calcNested              Calculate 'plNested' Performance
calcStats               Calculate Model Performance
check.ctrlGS            Check 'ctrlGS' Arguments
classCheck              Class Check
compare                 Compare 'ExprsArray' Objects
conjoin                 Combine 'exprso' Objects
ctrlFeatureSelect       Manage 'fs' Arguments
ctrlGridSearch          Manage 'plGrid' Arguments
ctrlModSet              Manage 'mod' Arguments
ctrlSplitSet            Manage 'split' Arguments
defaultArg              Set an args List Element to Default Value
doMulti                 Perform Multiple "1 vs. all" Tasks
exprso                  The 'exprso' Package
exprso-predict          Deploy Model
forceArg                Force an args List Element to Value
fs                      Select Features
fs.                     Workhorse for fs Methods
fsANOVA                 Select Features by ANOVA
fsCor                   Select Features by Correlation
fsEbayes                Select Features by Moderated t-test
fsEdger                 Selects Features by Exact Test
fsInclude               Select Features by Explicit Reference
fsMrmre                 Select Features by mRMR
fsNULL                  Null Feature Selection
fsPathClassRFE          Select Features by Recursive Feature
                        Elimination
fsPrcomp                Reduce Dimensions by PCA
fsPropd                 Select Features by Differential Proportionality
                        Analysis
fsSample                Select Features by Random Sampling
fsStats                 Select Features by Statistical Testing
getArgs                 Build an args List
getFeatures             Retrieve Feature Set
makeGridFromArgs        Build Argument Grid
mod                     Process Data
modAcomp                Compositionally Constrain Data
modCLR                  Log-ratio Transform Data
modCluster              Cluster Subjects
modFilter               Hard Filter Data
modHistory              Replicate Data Process History
modNormalize            Normalize Data
modSubset               Tidy Subset Wrapper
modSwap                 Swap Case Subjects
modTMM                  Normalize Data
modTransform            Log Transform Data
packageCheck            Package Check
pipe                    Process Pipelines
pipeFilter              Filter 'ExprsPipeline' Object
pipeUnboot              Rename "boot" Column
pl                      Deploy Pipeline
plCV                    Perform Simple Cross-Validation
plGrid                  Perform High-Throughput Machine Learning
plGridMulti             Perform High-Throughput Multi-Class
                        Classification
plMonteCarlo            Monte Carlo Cross-Validation
plNested                Nested Cross-Validation
reRank                  Serialize "1 vs. all" Feature Selection
split                   Split Data
splitSample             Split by Random Sampling
splitStratify           Split by Stratified Sampling
trainingSet             Extract Training Set
validationSet           Extract Validation Set
