workflows 0.2.3

• workflow() has gained new preprocessor and spec arguments for adding
  a preprocessor (such as a recipe or formula) and a parsnip model specification
  directly to a workflow upon creation. In many cases, this can reduce the
  lines of code required to construct a complete workflow (#108).

• New extract_*() functions have been added that supersede the existing
  pull_*() functions. This is part of a larger move across the tidymodels
  packages towards a family of generic extract_*() functions. The pull_*()
  functions have been soft-deprecated, and will eventually be removed (#106).

workflows 0.2.2

• add_variables() now allows for specifying a bundle of model terms through
  add_variables(variables = ), supplying a pre-created set of variables with
  the new workflow_variables() helper. This is useful for supplying a set
  of variables programmatically (#92).

• New is_trained_workflow() for determining if a workflow has already been
  trained through a call to fit() (#91).

• fit() now errors immediately if control is not created by
  control_workflow() (#89).

• Added broom::augment() and broom::glance() methods for trained workflow
  objects (#76).

• Added support for butchering a workflow using butcher::butcher().

• Updated to testthat 3.0.0.

workflows 0.2.1

• New .fit_finalize() for internal usage by the tune package.

workflows 0.2.0

• New add_variables() for specifying model terms using tidyselect expressions
  with no extra preprocessing. For example:

  wf <- workflow() %>%
    add_variables(y, c(var1, start_with("x_"))) %>%
    add_model(spec_lm)
  

  One benefit of specifying terms in this way over the formula method is to
  avoid preprocessing from model.matrix(), which might strip the class of
  your predictor columns (as it does with Date columns) (#34).

workflows 0.1.3

• A test has been updated to reflect a change in parsnip 0.1.3 regarding how
  intercept columns are removed during prediction (#65).

workflows 0.1.2

• When using a formula preprocessor with add_formula(), workflows now uses
  model-specific information from parsnip to decide whether to expand
  factors via dummy encoding (n - 1 levels), one-hot encoding (n levels), or
  no expansion at all. This should result in more intuitive behavior when
  working with models that don't require dummy variables. For example, if a
  parsnip rand_forest() model is used with a ranger engine, dummy variables
  will not be created, because ranger can handle factors directly (#51, #53).

workflows 0.1.1

• hardhat's minimum required version has been bumped to 0.1.2, as it contains
  an important fix to how recipes are prepped by default.

workflows 0.1.0

v0.1.0

Delete CRAN-RELEASE