mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
tidyclust vs workflows
A side-by-side editorial comparison of tidyclust and workflows — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:tidymodels
tidyclust vs workflows: at a glance
What is tidyclust?
tidyclust just tripled the model types it can fit, and handed finalization back to tune
tidyclust brings clustering into the tidymodels interface, and 0.3.0 was the release where its model coverage stopped being k-means and hierarchical clustering. DBSCAN and HDBSCAN, Gaussian mixtures, and mean shift all arrived at once as proper clustering specifications. The two releases since have been bug fixes on the metric and sparse-data paths, which is the usual pattern after a large surface addition.
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.
tidyclust vs workflows: editorial side-by-side
tidyclust
0.0ANALYTICS
tidyclust just tripled the model types it can fit, and handed finalization back to tune
◆ Current state
tidyclust brings clustering into the tidymodels interface, and 0.3.0 was the release where its model coverage stopped being k-means and hierarchical clustering. DBSCAN and HDBSCAN, Gaussian mixtures, and mean shift all arrived at once as proper clustering specifications. The two releases since have been bug fixes on the metric and sparse-data paths, which is the usual pattern after a large surface addition.
◆ Where it's heading
The package is converging with the rest of tidymodels rather than maintaining a parallel API: finalize_model_tidyclust() and finalize_workflow_tidyclust() are deprecated because tune::finalize_model() and tune::finalize_workflow() now handle cluster_spec objects natively. That removes the last place where clustering needed its own version of a shared verb. With density-based and model-based clustering now present, the interface has to cover model families with genuinely different assumptions than the centroid methods it started with.
◆ Prediction
The recent fixes to cluster_metric_set() labeling and custom-metric authoring suggest evaluation is the current focus, so metrics suited to density-based clusters are the likely next addition.
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 tidyclust 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 tidyclust 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 tidyclust alternatives → · See all workflows alternatives →
Recent activity from tidyclust and workflows
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agotidyclusttidyclust 0.3.2 fixes k_means() on sparse predictors
- 1mo agotidyclusttidyclust 0.3.1 stops same-named metrics silently merging
- 2mo agotidyclusttidyclust 0.3.0 adds DBSCAN, Gaussian mixture, and mean shift models ⚡
- 11mo agoworkflowsWorkflows gain a postprocessing stage via tailor ⚡
- 1y agoworkflowsSparse matrices work through fit() and predict()
- 1y agotidyclusttidyclust 0.2.4 switches distance calculations to philentropy
- 2y agotidyclusttidyclust 0.2.3 resolves a clustMixType reverse-dependency issue
- 2y agotidyclusttidyclust 0.2.2 resolves a ClusterR reverse-dependency issue
- 2y agoworkflowsaugment() aligns with parsnip; censored regression supported
- 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 tidyclust and workflows?
Both compete on the same themes — tidymodels — within Analytics. tidyclust 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 tidyclust better than workflows?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tidyclust 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 tidyclust?
Top tidyclust alternatives in Analytics are ranked by recent ship velocity. Browse the "tidyclust alternatives" section above for the current picks, or visit /alternatives/tidyclust 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.