Function reference • vtreat

All functions
`BinomialOutcomeTreatment()`	Stateful object for designing and applying binomial outcome treatments.
`MultinomialOutcomeTreatment()`	Stateful object for designing and applying multinomial outcome treatments.
`NumericOutcomeTreatment()`	Stateful object for designing and applying numeric outcome treatments.
`UnsupervisedTreatment()`	Stateful object for designing and applying unsupervised treatments.
`apply_transform()`	Transform second argument by first.
`as_rquery_plan()`	Convert vtreatment plans into a sequence of rquery operations.
`buildEvalSets()`	Build set carve-up for out-of sample evaluation.
`center_scale()`	Center and scale a set of variables.
`classification_parameters()`	vtreat classification parameters.
`designTreatmentsC()`	Build all treatments for a data frame to predict a categorical outcome.
`designTreatmentsN()`	build all treatments for a data frame to predict a numeric outcome
`designTreatmentsZ()`	Design variable treatments with no outcome variable.
`design_missingness_treatment()`	Design a simple treatment plan to indicate missingingness and perform simple imputation.
`fit()`	Fit first arguemnt to data in second argument.
`fit_prepare()`	Fit and prepare in a cross-validated manner.
`fit_transform()`	Fit and transform in a cross-validated manner.
`format(<vtreatment>)`	Display treatment plan.
`getSplitPlanAppLabels()`	read application labels off a split plan.
`get_feature_names()`	Return feasible feature names.
`get_score_frame()`	Return score frame from vps.
`get_transform()`	Return underlying transform from vps.
`kWayCrossValidation()`	k-fold cross validation, a splitFunction in the sense of vtreat::buildEvalSets
`kWayStratifiedY()`	k-fold cross validation stratified on y, a splitFunction in the sense of vtreat::buildEvalSets
`kWayStratifiedYReplace()`	k-fold cross validation stratified with replacement on y, a splitFunction in the sense of vtreat::buildEvalSets .
`makeCustomCoderCat()`	Make a categorical input custom coder.
`makeCustomCoderNum()`	Make a numeric input custom coder.
`makekWayCrossValidationGroupedByColumn()`	Build a k-fold cross validation splitter, respecting (never splitting) groupingColumn.
`mkCrossFrameCExperiment()`	Run categorical cross-frame experiment.
`mkCrossFrameMExperiment()`	Function to build multi-outcome vtreat cross frame and treatment plan.
`mkCrossFrameNExperiment()`	Run a numeric cross frame experiment.
`multinomial_parameters()`	vtreat multinomial parameters.
`novel_value_summary()`	Report new/novel appearances of character values.
`oneWayHoldout()`	One way holdout, a splitFunction in the sense of vtreat::buildEvalSets.
`patch_columns_into_frame()`	Patch columns into data.frame.
`ppCoderC()`	Solve a categorical partial pooling problem.
`ppCoderN()`	Solve a numeric partial pooling problem.
`pre_comp_xval()`	Pre-computed cross-plan (so same split happens each time).
`prepare()`	Apply treatments and restrict to useful variables.
`prepare(<multinomial_plan>)`	Function to apply mkCrossFrameMExperiment treatemnts.
`prepare(<simple_plan>)`	Prepare a simple treatment.
`prepare(<treatmentplan>)`	Apply treatments and restrict to useful variables.
`print(<multinomial_plan>)`	Print treatmentplan.
`print(<simple_plan>)`	Print treatmentplan.
`print(<treatmentplan>)`	Print treatmentplan.
`print(<vtreatment>)`	Print treatmentplan.
`problemAppPlan()`	check if appPlan is a good carve-up of 1:nRows into nSplits groups
`regression_parameters()`	vtreat regression parameters.
`rquery_prepare()` `materialize_treated()`	Materialize a treated data frame remotely.
`solveIsotone()`	Solve for best single-direction (non-decreasing or non-increasing) fit.
`solveNonDecreasing()`	Solve for best non-decreasing fit using isotone regression (from the "isotone" package https://CRAN.R-project.org/package=isotone).
`solveNonIncreasing()`	Solve for best non-increasing fit.
`solve_piecewise()`	Solve as piecewise linear problem, numeric target.
`solve_piecewisec()`	Solve as piecewise logit problem, categorical target.
`spline_variable()`	Spline variable numeric target.
`spline_variablec()`	Spline variable categorical target.
`square_window()`	Build a square windows variable, numeric target.
`square_windowc()`	Build a square windows variable, categorical target.
`track_values()`	Track unique character values for variables.
`unsupervised_parameters()`	vtreat unsupervised parameters.
`value_variables_C()`	Value variables for prediction a categorical outcome.
`value_variables_N()`	Value variables for prediction a numeric outcome.
`variable_values()`	Return variable evaluations.
`vnames()`	New treated variable names from a treatmentplan$treatment item.
`vorig()`	Original variable name from a treatmentplan$treatment item.
`vtreat`	vtreat: A Statistically Sound 'data.frame' Processor/Conditioner

Reference

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