A Statistically Sound 'data.frame' Processor/Conditioner

as_rquery_plan | Convert vtreatment plans into a sequence of rquery... |

buildEvalSets | Build set carve-up for out-of sample evaluation. |

center_scale | Center and scale a set of variables. |

design_missingness_treatment | Design a simple treatment plan to indicate missingingness and... |

designTreatmentsC | Build all treatments for a data frame to predict a... |

designTreatmentsN | build all treatments for a data frame to predict a numeric... |

designTreatmentsZ | Design variable treatments with no outcome variable. |

flatten_fn_list | Flatten a list of functions onto d. |

format.vtreatment | Display treatment plan. |

getSplitPlanAppLabels | read application labels off a split plan. |

kWayCrossValidation | k-fold cross validation, a splitFunction in the sense of... |

kWayStratifiedY | k-fold cross validation stratified on y, a splitFunction in... |

kWayStratifiedYReplace | k-fold cross validation stratified with replacement on y, a... |

makekWayCrossValidationGroupedByColumn | Build a k-fold cross validation splitter, respecting (never... |

mkCrossFrameCExperiment | Run categorical cross-frame experiment. |

mkCrossFrameMExperiment | Function to build multi-outcome vtreat cross frame and... |

mkCrossFrameNExperiment | Run a numeric cross frame experiment. |

novel_value_summary | Report new/novel appearances of character values. |

oneWayHoldout | One way holdout, a splitFunction in the sense of... |

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... |

reexports | Objects exported from other packages |

rqdatatable_prepare | Apply a treatment plan using rqdatatable. |

rquery_prepare | Materialize a treated data frame remotely. |

solveIsotone | Solve for best single-direction (non-decreasing or... |

solveNonDecreasing | Solve for best non-decreasing fit using isotone regression... |

solveNonIncreasing | Solve for best non-increasing fit. |

solve_piecewise | Solve as piecewise linear problem. |

spline_variable | Spline numeric variable |

square_window | Build a square windows variable. |

track_values | Track unique character values for variables. |

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... |

vorig | Original variable name from a treatmentplan$treatment item. |

vtreat | vtreat: A Statistically Sound 'data.frame'... |

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