Package index • hardhat

Preprocessing
`mold()`	Mold data for modeling
`forge()`	Forge prediction-ready data
Prediction
`spruce_numeric_multiple()` `spruce_class_multiple()` `spruce_prob_multiple()`	Spruce up multi-outcome predictions
`spruce_numeric()` `spruce_class()` `spruce_prob()`	Spruce up predictions
`quantile_pred()` `extract_quantile_levels()` `as_tibble(<quantile_pred>)` `as.matrix(<quantile_pred>)`	Create a vector containing sets of quantiles
Utility
`model_frame()`	Construct a model frame
`model_matrix()`	Construct a design matrix
`model_offset()`	Extract a model offset
`delete_response()`	Delete the response from a terms object
`standardize()`	Standardize the outcome
`new_model()`	Constructor for a base model
`add_intercept_column()`	Add an intercept column to `data`
`weighted_table()`	Weighted table
`fct_encode_one_hot()`	Encode a factor as a one-hot indicator matrix
Validation
`scream()`	Scream
`shrink()`	Subset only required columns
`validate_column_names()` `check_column_names()`	Ensure that `data` contains required column names
`validate_no_formula_duplication()` `check_no_formula_duplication()`	Ensure no duplicate terms appear in `formula`
`validate_outcomes_are_binary()` `check_outcomes_are_binary()`	Ensure that the outcome has binary factors
`validate_outcomes_are_factors()` `check_outcomes_are_factors()`	Ensure that the outcome has only factor columns
`validate_outcomes_are_numeric()` `check_outcomes_are_numeric()`	Ensure outcomes are all numeric
`validate_outcomes_are_univariate()` `check_outcomes_are_univariate()`	Ensure that the outcome is univariate
`validate_prediction_size()` `check_prediction_size()`	Ensure that predictions have the correct number of rows
`validate_predictors_are_numeric()` `check_predictors_are_numeric()`	Ensure predictors are all numeric
Blueprint
`default_formula_blueprint()` `mold(<formula>)`	Default formula blueprint
`default_recipe_blueprint()` `mold(<recipe>)`	Default recipe blueprint
`default_xy_blueprint()` `mold(<data.frame>)` `mold(<matrix>)`	Default XY blueprint
`is_blueprint()`	Is `x` a preprocessing blueprint?
`new_formula_blueprint()` `new_recipe_blueprint()` `new_xy_blueprint()` `new_blueprint()`	Create a new preprocessing blueprint
`new_default_formula_blueprint()` `new_default_recipe_blueprint()` `new_default_xy_blueprint()`	Create a new default blueprint
`refresh_blueprint()`	Refresh a preprocessing blueprint
`run_forge()`	`forge()` according to a blueprint
`run_mold()`	`mold()` according to a blueprint
`update_blueprint()`	Update a preprocessing blueprint
Case Weights
`new_case_weights()`	Extend case weights
`is_case_weights()`	Is `x` a case weights vector?
Importance Weights
`importance_weights()`	Importance weights
`new_importance_weights()`	Construct an importance weights vector
`is_importance_weights()`	Is `x` an importance weights vector?
Frequency Weights
`frequency_weights()`	Frequency weights
`new_frequency_weights()`	Construct a frequency weights vector
`is_frequency_weights()`	Is `x` a frequency weights vector?
Setup
`create_modeling_package()` `use_modeling_deps()` `use_modeling_files()`	Create a modeling package
Information
`get_data_classes()`	Extract data classes from a data frame or matrix
`get_levels()` `get_outcome_levels()`	Extract factor levels from a data frame
Development
`tune()`	Mark arguments for tuning
`extract_workflow()` `extract_recipe()` `extract_spec_parsnip()` `extract_fit_parsnip()` `extract_fit_engine()` `extract_mold()` `extract_preprocessor()` `extract_postprocessor()` `extract_tailor()` `extract_parameter_dials()` `extract_parameter_set_dials()` `extract_fit_time()`	Generics for object extraction
Data
`hardhat-example-data` `example_train` `example_test`	Example data for hardhat

Reference

Preprocessing

Prediction

Utility

Validation

Blueprint

Case Weights

Importance Weights

Frequency Weights

Setup

Information

Development

Data