workflows 1.1.4

• While augment.workflow() previously never returned a .resid column, the
  method will now return residuals under the same conditions that
  augment.model_fit() does (#201).

• augment.workflow() gained an eval_time argument, enabling augmenting
  censored regression models (#200, #213).

• The prediction columns are now appended to the LHS rather than RHS of
  new_data in augment.workflow(), following analogous changes in
  parsnip (#200).

• Each of the pull_*() functions soft-deprecated in workflows v0.2.3
  now warn on every usage (#198).

• add_recipe() will now error informatively when supplied a trained recipe
  (#179).

workflows 1.1.3

• The workflows methods for generics::tune_args() and generics::tunable() are now registered unconditionally (#192).

workflows 1.1.2

• Tightens integration with parsnip's machinery for checking that needed parsnip extension packages are loaded. add_model() will now error if a model specification is supplied that requires a missing extension package (#184).
• Introduces support for unsupervised model specifications via the modelenv package (#180).

The previous CRAN release was 1.1.0—this version tag skips 1.1.1.

workflows 1.1.0

• Simon Couch is now the maintainer (#170).

• add_model() now errors if you try to add a model specification
  that contains an unknown mode. This is a breaking change, as previously in
  some cases it would successfully "guess" the mode. This change brings
  workflows more in line with parsnip::fit() and parsnip::fit_xy()
  (#160, tidymodels/parsnip#801).

• broom::augment() now works correctly in the edge case where you had supplied
  a hardhat blueprint with composition set to either "matrix" or
  "dgCMatrix" (#148).

• butcher::axe_fitted() now axes the recipe preprocessor that is stored inside
  a workflow, which will reduce the size of the template data frame that is
  stored in the recipe (#147).

• add_formula() no longer silently ignores offsets supplied with offset().
  Instead, it now errors at fit() time with a message that encourages you to
  use a model formula through add_model(formula = ) instead (#162).

workflows 1.0.0

• New add_case_weights(), update_case_weights(), and remove_case_weights()
  for specifying a column to use as case weights which will be passed on to the
  underlying parsnip model (#118).

• R >=3.4.0 is now required, in line with the rest of the tidyverse.

workflows 0.2.6

• Fixed tests that relied on an incorrect assumption about the version of tune
  that is installed.

workflows 0.2.5

• Improved error message in workflow_variables() if either outcomes or
  predictors are missing (#144).

• Removed ellipsis dependency in favor of equivalent functions in rlang.

• New extract_parameter_set_dials() and extract_parameter_dials() methods
  to extract parameter sets and single parameters from workflow objects.

workflows 0.2.4

• add_model() and update_model() now use ... to separate the required
  arguments from the optional arguments, forcing optional arguments to be
  named. This change was made to make it easier for us to extend these functions
  with new arguments in the future.

• The workflows method for generics::required_pkgs() is now registered
  unconditionally (#121).

• Internally cleaned up remaining usage of soft-deprecated pull_*() functions.

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.