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_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_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 |