Package index • hardhat

Preprocessing

mold(): Mold data for modeling

forge(): Forge prediction-ready data

Prediction

spruce_numeric_multiple() spruce_class_multiple() spruce_prob_multiple(): Spruce up multi-outcome predictions

spruce_numeric() spruce_class() spruce_prob(): Spruce up predictions

quantile_pred() extract_quantile_levels() as_tibble(<quantile_pred>) as.matrix(<quantile_pred>): Create a vector containing sets of quantiles

Utility

model_frame(): Construct a model frame

model_matrix(): Construct a design matrix

model_offset(): Extract a model offset

delete_response(): Delete the response from a terms object

standardize(): Standardize the outcome

new_model(): Constructor for a base model

add_intercept_column(): Add an intercept column to data

weighted_table(): Weighted table

fct_encode_one_hot(): Encode a factor as a one-hot indicator matrix

Validation

scream(): Scream

shrink(): Subset only required columns

validate_column_names() check_column_names(): Ensure that data contains required column names

validate_no_formula_duplication() check_no_formula_duplication(): Ensure no duplicate terms appear in formula

validate_outcomes_are_binary() check_outcomes_are_binary(): Ensure that the outcome has binary factors

validate_outcomes_are_factors() check_outcomes_are_factors(): Ensure that the outcome has only factor columns

validate_outcomes_are_numeric() check_outcomes_are_numeric(): Ensure outcomes are all numeric

validate_outcomes_are_univariate() check_outcomes_are_univariate(): Ensure that the outcome is univariate

validate_prediction_size() check_prediction_size(): Ensure that predictions have the correct number of rows

validate_predictors_are_numeric() check_predictors_are_numeric(): Ensure predictors are all numeric

Blueprint

default_formula_blueprint() mold(<formula>): Default formula blueprint

default_recipe_blueprint() mold(<recipe>): Default recipe blueprint

default_xy_blueprint() mold(<data.frame>) mold(<matrix>): Default XY blueprint

is_blueprint(): Is x a preprocessing blueprint?

new_formula_blueprint() new_recipe_blueprint() new_xy_blueprint() new_blueprint(): Create a new preprocessing blueprint

new_default_formula_blueprint() new_default_recipe_blueprint() new_default_xy_blueprint(): Create a new default blueprint

refresh_blueprint(): Refresh a preprocessing blueprint

run_forge(): forge() according to a blueprint

run_mold(): mold() according to a blueprint

update_blueprint(): Update a preprocessing blueprint

Case Weights

new_case_weights() experimental: Extend case weights

is_case_weights() experimental: Is x a case weights vector?

Importance Weights

importance_weights() experimental: Importance weights

new_importance_weights() experimental: Construct an importance weights vector

is_importance_weights() experimental: Is x an importance weights vector?

Frequency Weights

frequency_weights() experimental: Frequency weights

new_frequency_weights() experimental: Construct a frequency weights vector

is_frequency_weights() experimental: Is x a frequency weights vector?

Setup

create_modeling_package() use_modeling_deps() use_modeling_files(): Create a modeling package

Information

get_data_classes(): Extract data classes from a data frame or matrix

get_levels() get_outcome_levels(): Extract factor levels from a data frame

Development

tune(): Mark arguments for tuning

extract_workflow() extract_recipe() extract_spec_parsnip() extract_fit_parsnip() extract_fit_engine() extract_mold() extract_preprocessor() extract_postprocessor() extract_tailor() extract_parameter_dials() extract_parameter_set_dials() extract_fit_time(): Generics for object extraction

Data

hardhat-example-data example_train example_test: Example data for hardhat