Man pages for hardhat
Construct Modeling Packages

add_intercept_columnAdd an intercept column to 'data'
contr_one_hotContrast function for one-hot encodings
default_formula_blueprintDefault formula blueprint
default_recipe_blueprintDefault recipe blueprint
default_xy_blueprintDefault XY blueprint
delete_responseDelete the response from a terms object
extract_ptypeExtract a prototype
fct_encode_one_hotEncode a factor as a one-hot indicator matrix
forgeForge prediction-ready data
frequency_weightsFrequency weights
get_data_classesExtract data classes from a data frame or matrix
get_levelsExtract factor levels from a data frame
hardhat-example-dataExample data for hardhat
hardhat-extractGenerics for object extraction
hardhat-packagehardhat: Construct Modeling Packages
importance_weightsImportance weights
is_blueprintIs 'x' a preprocessing blueprint?
is_case_weightsIs 'x' a case weights vector?
is_frequency_weightsIs 'x' a frequency weights vector?
is_importance_weightsIs 'x' an importance weights vector?
model_frameConstruct a model frame
modeling-packageCreate a modeling package
model_matrixConstruct a design matrix
model_offsetExtract a model offset
moldMold data for modeling
new-blueprintCreate a new preprocessing blueprint
new_case_weightsExtend case weights
new-default-blueprintCreate a new default blueprint
new_frequency_weightsConstruct a frequency weights vector
new_importance_weightsConstruct an importance weights vector
new_modelConstructor for a base model
recomposeRecompose a data frame into another form
refresh_blueprintRefresh a preprocessing blueprint
run-forge'forge()' according to a blueprint
run-mold'mold()' according to a blueprint
shrinkSubset only required columns
spruceSpruce up predictions
spruce-multipleSpruce up multi-outcome predictions
standardizeStandardize the outcome
tuneMark arguments for tuning
update_blueprintUpdate a preprocessing blueprint
validate_column_namesEnsure that 'data' contains required column names
validate_no_formula_duplicationEnsure no duplicate terms appear in 'formula'
validate_outcomes_are_binaryEnsure that the outcome has binary factors
validate_outcomes_are_factorsEnsure that the outcome has only factor columns
validate_outcomes_are_numericEnsure outcomes are all numeric
validate_outcomes_are_univariateEnsure that the outcome is univariate
validate_prediction_sizeEnsure that predictions have the correct number of rows
validate_predictors_are_numericEnsure predictors are all numeric
weighted_tableWeighted table
hardhat documentation built on March 31, 2023, 10:21 p.m.