hardhat1.4.2 package

Add an intercept column to data

Check levels of quantiles

Contrast function for one-hot encodings

Default formula blueprint

Default recipe blueprint

Default XY blueprint

Delete the response from a terms object

Extract a prototype

Encode a factor as a one-hot indicator matrix

Forge prediction-ready data

Frequency weights

Extract data classes from a data frame or matrix

Extract factor levels from a data frame

Generics for object extraction

hardhat: Construct Modeling Packages

Importance weights

Is x a preprocessing blueprint?

Is x a case weights vector?

Is x a frequency weights vector?

Is x an importance weights vector?

Construct a model frame

Construct a design matrix

Extract a model offset

Create a modeling package

Mold data for modeling

Extend case weights

Construct a frequency weights vector

Construct an importance weights vector

Constructor for a base model

Create a new preprocessing blueprint

Create a new default blueprint

Create a vector containing sets of quantiles

Recompose a data frame into another form

Refresh a preprocessing blueprint

forge() according to a blueprint

mold() according to a blueprint

Scream

Subset only required columns

Spruce up multi-outcome predictions

Spruce up predictions

Standardize the outcome

Mark arguments for tuning

Update a preprocessing blueprint

Ensure that data contains required column names

Ensure no duplicate terms appear in formula

Ensure that the outcome has binary factors

Ensure that the outcome has only factor columns

Ensure outcomes are all numeric

Ensure that the outcome is univariate

Ensure that predictions have the correct number of rows

Ensure predictors are all numeric

Weighted table