# Construct Modeling Packages

## Documentation for package ‘hardhat’ version 1.4.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C D E F G H I M N Q R S T U V W

## -- A --

add_intercept_column | Add an intercept column to 'data'
as.matrix.quantile_pred | Create a vector containing sets of quantiles
as_tibble.quantile_pred | Create a vector containing sets of quantiles

## -- C --

check_column_names | Ensure that 'data' contains required column names
check_no_formula_duplication | Ensure no duplicate terms appear in 'formula'
check_outcomes_are_binary | Ensure that the outcome has binary factors
check_outcomes_are_factors | Ensure that the outcome has only factor columns
check_outcomes_are_numeric | Ensure outcomes are all numeric
check_outcomes_are_univariate | Ensure that the outcome is univariate
check_prediction_size | Ensure that predictions have the correct number of rows
check_predictors_are_numeric | Ensure predictors are all numeric
create_modeling_package | Create a modeling package

## -- D --

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

## -- E --

example_test | Example data for hardhat
example_train | Example data for hardhat
extract_fit_engine | Generics for object extraction
extract_fit_parsnip | Generics for object extraction
extract_fit_time | Generics for object extraction
extract_mold | Generics for object extraction
extract_parameter_dials | Generics for object extraction
extract_parameter_set_dials | Generics for object extraction
extract_postprocessor | Generics for object extraction
extract_preprocessor | Generics for object extraction
extract_quantile_levels | Create a vector containing sets of quantiles
extract_recipe | Generics for object extraction
extract_spec_parsnip | Generics for object extraction
extract_tailor | Generics for object extraction
extract_workflow | Generics for object extraction

## -- F --

fct_encode_one_hot | Encode a factor as a one-hot indicator matrix
forge | Forge prediction-ready data
frequency_weights | Frequency weights

## -- G --

get_data_classes | Extract data classes from a data frame or matrix
get_levels | Extract factor levels from a data frame
get_outcome_levels | Extract factor levels from a data frame

## -- H --

hardhat-example-data | Example data for hardhat
hardhat-extract | Generics for object extraction

## -- I --

importance_weights | Importance weights
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?

## -- M --

modeling-usethis | Create a modeling package
model_frame | Construct a model frame
model_matrix | Construct a design matrix
model_offset | Extract a model offset
mold | Mold data for modeling
mold.data.frame | Default XY blueprint
mold.formula | Default formula blueprint
mold.matrix | Default XY blueprint
mold.recipe | Default recipe blueprint

## -- N --

new-blueprint | Create a new preprocessing blueprint
new-default-blueprint | Create a new default blueprint
new_blueprint | Create a new preprocessing blueprint
new_case_weights | Extend case weights
new_default_formula_blueprint | Create a new default blueprint
new_default_recipe_blueprint | Create a new default blueprint
new_default_xy_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
new_recipe_blueprint | Create a new preprocessing blueprint
new_xy_blueprint | Create a new preprocessing blueprint

## -- Q --

quantile_pred | Create a vector containing sets of quantiles

## -- R --

refresh_blueprint | Refresh a preprocessing blueprint
run-forge | 'forge()' according to a blueprint
run-mold | 'mold()' according to a blueprint
run_forge | 'forge()' according to a blueprint
run_forge.default_formula_blueprint | 'forge()' according to a blueprint
run_forge.default_recipe_blueprint | 'forge()' according to a blueprint
run_forge.default_xy_blueprint | 'forge()' according to a blueprint
run_mold | 'mold()' according to a blueprint
run_mold.default_formula_blueprint | 'mold()' according to a blueprint
run_mold.default_recipe_blueprint | 'mold()' according to a blueprint
run_mold.default_xy_blueprint | 'mold()' according to a blueprint

## -- S --

scream | Scream
shrink | Subset only required columns
spruce | Spruce up predictions
spruce-multiple | Spruce up multi-outcome predictions
spruce_class | Spruce up predictions
spruce_class_multiple | Spruce up multi-outcome predictions
spruce_numeric | Spruce up predictions
spruce_numeric_multiple | Spruce up multi-outcome predictions
spruce_prob | Spruce up predictions
spruce_prob_multiple | Spruce up multi-outcome predictions
standardize | Standardize the outcome

## -- T --

tune | Mark arguments for tuning

## -- U --

update_blueprint | Update a preprocessing blueprint
use_modeling_deps | Create a modeling package
use_modeling_files | Create a modeling package

## -- V --

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

## -- W --

weighted_table | Weighted table