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Addressed inflation of butchered model stack object size after saving and reloading (#214).
Fixed type-checking bug for
add_candidates(name)
.
Introduced support for parallel processing using the future framework. The stacks package previously supported parallelism with foreach, and users can use either framework for now. In a future release, stacks will begin the deprecation cycle for parallelism with foreach, so we encourage users to begin migrating their code now. See the Parallel Processing section in the tune package’s “Optimizations” article to learn more (#866).
Improved error message for unsupported model modes (#152).
Refine package alt text (#202).
Update example objects, resolving deprecation warnings from recipes (#203).
Fix bug in type checking for
blend_predictions(mixture)
(#204).
Resolve package-level documentation aliasing notice from CRAN.
Added an augment()
method for
model_stack
objects (#173).
Converted all character variables in the tree_frogs
example data to factor and updated downstream example objects
(#177).
Fixed bug that resulted in errors when using model formulas with
the "mgcv"
engine (#193).
Made several optimizations to reduce evaluation time and memory allocation when stacking.
Various bug fixes and improvements to documentation.
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).
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 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:
type
argument to
predict
is set to "class"
, and the outcome
levels differ from alphabetical order.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.
add_candidates
(#99).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).fit_members()
, if available, and fail informatively if not
(#118)."class"
(#125).collect_parameters
failing to return stacking
coefficients in the two-class classification setting.add_variables()
preprocessor.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.
mixture
argument to
blend_predictions
.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.make.names
for
associated candidate members.fit_members()
will now warn when supplied a model stack
whose members have already been fitted.autoplot
with
type = "members"
more informative.Initial release!
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.