NEWS

caretEnsemble 4.0.2

Add option to keep resamples for each repeated fold in caretStack and caretList, rather than aggregating to one resample per row in the original data. This can give your stacking model more variance to work with, but can lead to a lot more issues with aligning predictions from different models, particularly ones that use different resampling strategies.
Add an option to include original features from the raw data in the stack, if stacking on a new dataset rather than on stacked predictions.

Added aggregate_resamples option to caretStack and related functions to control whether resamples are aggregated.
Speed up the example for autoplot so it runs in <1 second on most platforms.

Multiclass support! caretList, caretStack, and caretEnsemble.
The greedy optimizer is back! caretEnsemble now uses a greedy optimizer by default. This optimizer can never be worse than the worst single model. caretStack still supports all caret models, including glm.

Refactored some internals for scalability (e.g. data.table for predictions, trim some un-needed data by default).
Moved all the S3 methods to caretStack, which now supports print, summary, plot, dotplot, and autoplot. caretEnsemble inherits from caretStack, and therefore also supports all of these methods.
Allow ensembling of mixed lists of classification and regression models.
Allow ensemble of models with different resampling strategies, so long as they were trained on the same data.
Allow transfer learning for ensembling models trained on different datasets.
Added permutation importance as the default importance method for caretLists and caretStacks.
Add a default trainControl constructor to make it easier to build good controls for training caretLists for stacking with caretStack.
Expanded test coverage to 100%.
Sped up test suite (unit tests now run in 20 seconds).
Delinted codebase: now conforms with all available linters save the object name linter.
Added a makefile for easier local package development.
Fixed badges in the readme.
Added a pkgdown site.
Switched to GitHub Actions (from Travis) for CI.
Internal refactoring, optimization, and bug fixes.

caretEnsemble now inherits from caretStack.
Removed the optimizers and now use a glm for caretEnsemble (optimizers will be added back as caret.train models in a future release).
Cleaned up namespace (all dependencies are explicit imports, rather than implicit imports or dependencies).
Removed S3 functions that are not really S3 functions (e.g. autoplot and fortify). We will either make those true S3 classes, or inherit from the packages that define them in a future release.
Fixed the build on Travis and locally.

Change output for predict functions to better align with other predict methods in R (predict.caretEnsemble and predict.caretStack).
Update documentation for predict methods to better explain the model disagreement calculation.
Speed and memory improvements by switching to data.table for some internals.
Modified the formula for a weighted standard deviation in the model disagreement calculation.