Construct Modeling Packages
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Documentation for package ‘hardhat’ version 1.4.0
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_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 |