tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
mlr3cluster vs workflows
A side-by-side editorial comparison of mlr3cluster and workflows — release velocity, themes, recent moves, and the top alternatives to consider.
mlr3cluster vs workflows: at a glance
What is mlr3cluster?
mlr3cluster went from a handful of clusterers to covering the field
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
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.
mlr3cluster vs workflows: editorial side-by-side
mlr3cluster
0.0ANALYTICS
mlr3cluster went from a handful of clusterers to covering the field
◆ Current state
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
◆ Where it's heading
The package is at the tail end of a coverage push, and the emphasis has shifted from adding algorithms to making the ones it has behave correctly at prediction time — cutting trees at the current k, reclustering coresets, failing informatively on unsupported metric combinations. That is the normal sequence after a rapid expansion.
◆ Prediction
Expect further predict-path corrections and parameter-set alignment across the newly added learners before any more algorithms arrive.
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 mlr3cluster 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 mlr3cluster or workflows.
textrecipes
Text features finally stay sparse all the way to the model.
probably
The package that made calibration a step instead of an afterthought.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
patchwork
patchwork stopped being a ggplot composer and became a page composer.
See all mlr3cluster alternatives → · See all workflows alternatives →
Recent activity from mlr3cluster and workflows
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomlr3clusterHierarchical learners now honour k at prediction time
- 2mo agomlr3clusterNine new clustering learners in one release ⚡
- 5mo agomlr3clusterCLARA, k-prototypes and spectral clustering learners added
- 6mo agomlr3clusterTyped error classes and probabilistic EM assignments
- 8mo agomlr3clusterHDBSCAN gains cluster_selection_epsilon
- 11mo agoworkflowsWorkflows gain a postprocessing stage via tailor ⚡
- 1y agoworkflowsSparse matrices work through fit() and predict()
- 1y agomlr3clusterMclust learner brought in line with paradox conventions
- 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 mlr3cluster and workflows?
They serve adjacent needs but don't currently overlap on shipped themes. mlr3cluster 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 mlr3cluster better than workflows?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3cluster 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 mlr3cluster?
Top mlr3cluster alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3cluster alternatives" section above for the current picks, or visit /alternatives/mlr3cluster 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.