hardhat 0.1.5

• use_modeling_files() (and therefore, create_modeling_package()) now
  ensures that all generated functions are templated on the model name. This
  makes it easier to add multiple models to the same package (#152).

• All preprocessors can now mold() and forge() predictors to one of three
  output formats (either tibble, matrix, or dgCMatrix sparse matrix) via the
  composition argument of a blueprint (#100, #150).

hardhat 0.1.4

• Setting indicators = "none" in default_formula_blueprint() no longer
  accidentally expands character columns into dummy variable columns. They
  are now left completely untouched and pass through as characters. When
  indicators = "traditional" or indicators = "one_hot", character columns
  are treated as unordered factors (#139).

• The indicators argument of default_formula_blueprint() now takes character
  input rather than logical. To update:

  indicators = TRUE  -> indicators = "traditional"
  
  indicators = FALSE -> indicators = "none"
  

  Logical input for indicators will continue to work, with a warning, until
  hardhat 0.1.6, where it will be formally deprecated.

  There is also a new indicators = "one_hot" option which expands all factor
  columns into K dummy variable columns corresponding to the K levels of
  that factor, rather than the more traditional K - 1 expansion.

hardhat 0.1.3

• Updated to stay current with the latest vctrs 0.3.0 conventions.

• scream() is now stricter when checking ordered factor levels in new data
  against the ptype used at training time. Ordered factors must now have
  exactly the same set of levels at training and prediction time. See
  ?scream for a new graphic outlining how factor levels are handled (#132).

• The novel factor level check in scream() no longer throws a novel level
  warning on NA values (#131).

hardhat 0.1.2

• default_recipe_blueprint() now defaults to prepping recipes with
  fresh = TRUE. This is a safer default, and guards the user against
  accidentally skipping this preprocessing step when tuning (#122).

• model_matrix() now correctly strips all attributes from the result of the
  internal call to model.matrix().

hardhat 0.1.1

• forge() now works correctly when used with a recipe that has a predictor
  with multiple roles (#120).

• Require recipes 0.1.8 to incorporate an important bug fix with juice() and
  0-column selections.

hardhat 0.1.0

• Added a NEWS.md file to track changes to the package.