## stacks 1.0.1

• Removes an unneeded data import attribute from the tree_frogs example data and its associated objects (#148).

• blend_predictions() doesn’t error anymore if the control argument isn’t a control_grid object. As long as the object passed to control include the same elements as control_grid() output, parsnip::condense_control() will handle input (#149).

• Tightened integration with the workflowsets package (#161, #165).

• Refined logic with adding candidates via workflowsets to allow for partially trained workflow sets. In the case that a workflow set contains some failed tuning results, stacks will inform the user that they will be excluded from the data stack and only add the results that trained successfully.
• Extended documentation related to the packages’ interactions, including a new article on the package website.
• Revamped errors, warnings, and messages. Prompts now provide more thorough context about where they arose, include more extensive references to documentation, and are correctly pluralized (#150, #167).

• Various bug fixes and improvements to documentation.

## stacks 1.0.0

CRAN release: 2022-07-06

stacks 1.0.0 is the first production release of the package. While this release includes only a few minor bug fixes, it’s accompanied by a white paper recently published in the Journal of Open Source software. You can read that paper here!

This release:

• Addresses re-introduction of a bug arising from outcome levels that are not valid column names in the multinomial classification setting (#133).
• Fixes bug where stacks will return incorrect predictions if an elastic net meta-learner is used, the type argument to predict is set to "class", and the outcome levels differ from alphabetical order.
• Transitions package internals from functions deprecated from the recipes package.

## stacks 0.2.4 (GitHub only)

This is a GitHub-only release and does not change package source code. This update includes a data-raw/paper subdirectory containing source for a contributed paper to the Journal of Open Source Software.

## stacks 0.2.3

CRAN release: 2022-05-12

• Addressed deprecation warning in add_candidates (#99).
• Improved clarity of warnings/errors related to failed hyperparameter tuning and resample fitting (#110).
• Reduced model stack object size and fixed bug where object size of model stack inflated drastically after saving to file (#116). Also, regenerated example objects with this change–saved model objects may need to be regenerated in order to interface with newer versions of the package.
• Introduced a times argument to blend_predictions that is passed on to rsample::bootstraps when fitting stacking coefficients. Reducing this argument from its default (25) greatly reduces the run time of blend_predictions (#94).
• The package will now load packages necessary for model fitting at fit_members(), if available, and fail informatively if not (#118).
• Fixed bug where meta-learner tuning would fail with outcome names and levels including the string "class" (#125).

## stacks 0.2.2

CRAN release: 2022-01-06

• Fixed errors arising from outcome levels that are not valid column names in the multinomial classification setting.
• Fixed collect_parameters failing to return stacking coefficients in the two-class classification setting.
• Regenerated example objects with updated {rsample} fingerprinting–saved model objects may need to be regenerated in order to build stacks combining models generated before and after this update.

## stacks 0.2.1

CRAN release: 2021-07-23

• Updates for importing workflow sets that use the add_variables() preprocessor.
• Plot fixes for cases where coefficients are negative.
• Performance and member plots now show the effect of multiple mixture values.
• Package diagrams now have alt text.

## stacks 0.2.0

CRAN release: 2021-04-20

### Breaking changes

This release of the package changes some elements of the internal structure of model stacks. As such, model stacks stored as saved objects will need to be regenerated before predicting, plotting, printing, etc.

### New features

• The package now supports elastic net models as a meta-learner via the mixture argument to blend_predictions.
• The package can now add candidates from workflow_map objects from the new {workflowsets} package. The interface to add_candidates for doing so is the same as with tune_results objects, and add_candidates is now a generic function.
• Objects tuned with racing methods from the {finetune} package can now be added as candidate members.

### Bug fixes

• Fixed bug in determining member hyperparameters during member fitting when using non-RMSE/ROC AUC metrics.
• Fixed bug arising from model definition names that are not valid column names. The package will now message in the case that the provided names are not valid column names and use make.names for associated candidate members.

### Miscellaneous improvements

• Drop {digest} dependency in favor of {tune}/{rsample} “fingerprinting” to check consistency of resamples.
• fit_members() will now warn when supplied a model stack whose members have already been fitted.
• Integrate with {tune} functionality for appropriately coloring errors, warnings, and messages.
• Improved faceting and axis scales to make autoplot with type = "members" more informative.
• Various improvements to documentation.

## stacks 0.1.0

CRAN release: 2020-11-23

Initial release!