Man pages for tpq/exprso
Rapid Deployment of Machine Learning Algorithms

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
buildDT	Build Decision Tree Model
buildEnsemble	Build Ensemble
buildFRB	Build Fuzzy Rule Based Model
buildGLM	Build Generalized Linear Model
buildLASSO	Build LASSO or Ridge Model
buildLDA	Build Linear Discriminant Analysis Model
buildLM	Build Linear Model
buildLR	Build Logistic Regression 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
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
exprso	The 'exprso' Package
exprso-predict	Deploy Model
ExprsPipeline-class	An S4 class to store models built during high-throughput...
ExprsPredict-class	An S4 class to store model predictions
forceArg	Force an args List Element to Value
fs	Select Features
fs.	Workhorse for fs Methods
fsAmalgam	Reduce Dimensions by Amalgamation
fsAnnot	Use Annotations as Features
fsANOVA	Select Features by ANOVA
fsBalance	Convert Features into Balances
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
fsPCA	Reduce Dimensions by PCA
fsPRA	Reduce Dimensions by Log-Ratio Selection
fsPrcomp	Reduce Dimensions by PCA
fsRankProd	Select Features by Rank Product Analysis
fsRDA	Reduce Dimensions by RDA
fsSample	Select Features by Random Sampling
fsStats	Select Features by Statistical Testing
getArgs	Build an args List
getFeatures	Retrieve Feature Set
getWeights	Retrieve LASSO Weights
GSE2eSet	Convert GSE to eSet
lequal	Test All Equal Within List
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
modInclude	Select Features from Data
modNormalize	Normalize Data
modPermute	Permute Features in Data
modRatios	Recast Data as Feature (Log-)Ratios
modSample	Sample Features from Data
modScale	Scale Data by Factor Range
modSkew	Skew Data by Factor Range
modSubset	Tidy Subset Wrapper
modSwap	Swap Case Subjects
modTMM	Normalize Data
modTransform	Log Transform Data
MultiPredict-class	An S4 class to store model predictions
nfeats	Get Number of Features
nsamps	Get Number of Samples
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
plMonteCarlo	Monte Carlo Cross-Validation
plNested	Nested Cross-Validation
progress	Make Progress Bar
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
split	Split Data
splitBalanced	Split by Balanced Sampling
splitBoost	Sample by Boosting
splitBy	Split by User-defined Group
splitSample	Split by Random Sampling
splitStratify	Split by Stratified Sampling
trainingSet	Extract Training Set
validationSet	Extract Validation Set