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.

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

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