Construct Modeling Packages

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Documentation for package ‘hardhat’ version 1.3.1

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

Help Pages

add_intercept_column	Add an intercept column to 'data'
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
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
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_mold	Generics for object extraction
extract_parameter_dials	Generics for object extraction
extract_parameter_set_dials	Generics for object extraction
extract_preprocessor	Generics for object extraction
extract_recipe	Generics for object extraction
extract_spec_parsnip	Generics for object extraction
extract_workflow	Generics for object extraction
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
get_outcome_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
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?
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
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
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
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
tune	Mark arguments for tuning
update_blueprint	Update a preprocessing blueprint
use_modeling_deps	Create a modeling package
use_modeling_files	Create a modeling package
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