mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
hardhat vs workflows
A side-by-side editorial comparison of hardhat and workflows — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:tidymodels
hardhat vs workflows: at a glance
What is hardhat?
hardhat keeps adding the contracts tidymodels needs next
hardhat is the infrastructure layer under tidymodels, defining the preprocessing and extraction contracts other packages implement. Its releases read as a list of new generics and vector classes: extract_postprocessor(), extract_fit_time(), extract_tailor(), and a quantile_pred() class for quantile-regression output. The newest release is narrow warning and missing-value handling in mold().
What is workflows?
The tidymodels pipeline grew a third stage, and it happens after the model runs.
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
hardhat vs workflows: editorial side-by-side
hardhat
0.0ANALYTICS
hardhat keeps adding the contracts tidymodels needs next
◆ Current state
hardhat is the infrastructure layer under tidymodels, defining the preprocessing and extraction contracts other packages implement. Its releases read as a list of new generics and vector classes: extract_postprocessor(), extract_fit_time(), extract_tailor(), and a quantile_pred() class for quantile-regression output. The newest release is narrow warning and missing-value handling in mold().
◆ Where it's heading
Each addition here lands ahead of a user-facing feature elsewhere in tidymodels — the postprocessor and tailor generics precede the postprocessing workflow, quantile_pred() precedes quantile prediction in parsnip. The package's own surface stays deliberately small and its cadence follows what the rest of the stack is about to need.
◆ Prediction
Expect further extraction generics and prediction-type classes as tidymodels builds out postprocessing, with hardhat's own API remaining thin.
workflows
0.0ANALYTICS
The tidymodels pipeline grew a third stage, and it happens after the model runs.
◆ Current state
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
◆ Where it's heading
The object is filling out into a complete pipeline description rather than a preprocessing-plus-model pair. Postprocessing is the structural addition — calibration and threshold selection were previously done by hand after prediction, outside anything tidymodels could tune or record — and the fact that it arrived integrated with tunable() and tune_args() rather than as a standalone step is the point. The rest of the arc is the steady tidymodels habit of converting silent guesses into errors.
◆ Prediction
Expect tailor postprocessors to spread through tune and workflowsets next, since the parameter and tuning generics were wired up first, and expect sparse support to extend to more engines after lightgbm.
Alternatives to hardhat and workflows
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either hardhat or workflows.
mlr3viz
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
mlr3tuning
mlr3tuning is rebuilding its async machinery under a stable public surface
timetk
timetk swallowed anomalize whole, then went quiet for two years
modelbased
modelbased is turning marginal effects into a full contrast grammar
parameters
easystats' parameters package absorbs one more model class every few weeks
See all hardhat alternatives → · See all workflows alternatives →
Recent activity from hardhat and workflows
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 4mo agohardhatmold() warning and quantile missing-value fixes
- 11mo agoworkflowsWorkflows gain a postprocessing stage via tailor ⚡
- 11mo agohardhatextract_tailor() generic added
- 1y agoworkflowsSparse matrices work through fit() and predict()
- 1y agohardhatquantile_pred() class for quantile regression output
- 2y agohardhatextract_postprocessor() and extract_fit_time() generics
- 2y agoworkflowsaugment() aligns with parsnip; censored regression supported
- 2y agohardhatDocumentation topic renamed at CRAN's request
- 3y agohardhatMulti-outcome prediction helpers and one-hot factor encoding
- 3y agoworkflowsRegister tuning generics unconditionally
- 3y agoworkflowsMissing parsnip extensions now error early; unsupervised specs supported
- 3y agoworkflowsMode guessing removed; silent offset handling now errors
Frequently asked questions
What is the difference between hardhat and workflows?
Both compete on the same themes — tidymodels — within Analytics. hardhat and workflows are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is hardhat better than workflows?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. hardhat and workflows are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to hardhat?
Top hardhat alternatives in Analytics are ranked by recent ship velocity. Browse the "hardhat alternatives" section above for the current picks, or visit /alternatives/hardhat for the full list with editorial commentary on each.
What are the best alternatives to workflows?
Top workflows alternatives in Analytics are ranked by recent ship velocity. Browse the "workflows alternatives" section above for the current picks, or visit /alternatives/workflows-r for the full list with editorial commentary on each.