add_intercept_column    Add an intercept column to 'data'
default_formula_blueprint
                        Default formula blueprint
default_recipe_blueprint
                        Default recipe blueprint
default_xy_blueprint    Default XY blueprint
delete_response         Delete the response from a terms object
fct_encode_one_hot      Encode a factor as a one-hot indicator matrix
forge                   Forge prediction-ready data
frequency_weights       Frequency weights
get_data_classes        Extract data classes from a data frame or
                        matrix
get_levels              Extract factor levels from a data frame
hardhat-example-data    Example data for hardhat
hardhat-extract         Generics for object extraction
importance_weights      Importance weights
impute_quantiles        Impute additional quantiles from a
                        'quantile_pred'
is_blueprint            Is 'x' a preprocessing blueprint?
is_case_weights         Is 'x' a case weights vector?
is_frequency_weights    Is 'x' a frequency weights vector?
is_importance_weights   Is 'x' an importance weights vector?
model_frame             Construct a model frame
model_matrix            Construct a design matrix
model_offset            Extract a model offset
modeling-usethis        Create a modeling package
mold                    Mold data for modeling
new_case_weights        Extend case weights
new_default_formula_blueprint
                        Create a new default blueprint
new_formula_blueprint   Create a new preprocessing blueprint
new_frequency_weights   Construct a frequency weights vector
new_importance_weights
                        Construct an importance weights vector
new_model               Constructor for a base model
quantile_pred           Create a vector containing sets of quantiles
refresh_blueprint       Refresh a preprocessing blueprint
run-forge               'forge()' according to a blueprint
run-mold                'mold()' according to a blueprint
scream                  Scream
shrink                  Subset only required columns
spruce                  Spruce up predictions
spruce-multiple         Spruce up multi-outcome predictions
standardize             Standardize the outcome
tune                    Mark arguments for tuning
update_blueprint        Update a preprocessing blueprint
validate_column_names   Ensure that 'data' contains required column
                        names
validate_no_formula_duplication
                        Ensure no duplicate terms appear in 'formula'
validate_outcomes_are_binary
                        Ensure that the outcome has binary factors
validate_outcomes_are_factors
                        Ensure that the outcome has only factor columns
validate_outcomes_are_numeric
                        Ensure outcomes are all numeric
validate_outcomes_are_univariate
                        Ensure that the outcome is univariate
validate_prediction_size
                        Ensure that predictions have the correct number
                        of rows
validate_predictors_are_numeric
                        Ensure predictors are all numeric
weighted_table          Weighted table
