← Back to home
Comparison · Analytics

mlr3tuning vs stacks

A side-by-side editorial comparison of mlr3tuning and stacks — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

mlr3tuning vs stacks: at a glance

Featuremlr3tuningstacks
SectorAnalyticsAnalytics
Velocity score2.50.0
Sparks · 30d00
Top themesmlr3, hyperparameter-tuning, async-optimization, callbackstidymodels, ensembling, parallel-processing, future-framework
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is mlr3tuning?

mlr3tuning is rebuilding its async machinery under a stable public surface

mlr3tuning provides hyperparameter optimization for the mlr3 ecosystem, and its recent history is dominated by the asynchronous tuning path: archive freezing, callback stages around queue evaluation, and version-locked compatibility with the rush backend. Releases pair a small feature with several fixes and an explicit compatibility line naming the mlr3 or rush version they track. The most recent release drops all workarounds for older rush versions, which suggests that dependency has stabilized enough to require rather than accommodate.

Read the full mlr3tuning trajectory →

What is stacks?

Model stacking in tidymodels, quietly migrating off foreach and onto future

stacks builds ensembles from tidymodels tuning results, and its release history is dominated by one long project: replacing foreach-based parallelism with the future framework. That transition completed in 1.1.0, where foreach backends began being ignored with a warning and the minimum R version rose to 4.1. Releases are infrequent and small, with the most recent being a CRAN re-submission rather than a change.

Read the full stacks trajectory →

mlr3tuning vs stacks: editorial side-by-side

M
mlr3tuning
ANALYTICS
2.5

mlr3tuning is rebuilding its async machinery under a stable public surface

◆ Current state

mlr3tuning provides hyperparameter optimization for the mlr3 ecosystem, and its recent history is dominated by the asynchronous tuning path: archive freezing, callback stages around queue evaluation, and version-locked compatibility with the rush backend. Releases pair a small feature with several fixes and an explicit compatibility line naming the mlr3 or rush version they track. The most recent release drops all workarounds for older rush versions, which suggests that dependency has stabilized enough to require rather than accommodate.

◆ Where it's heading

Two things are being tidied at once. The async archive is converging on a consistent data.table representation across batch and async variants, so results are shaped the same regardless of how tuning ran. Separately, the package is becoming a better ecosystem citizen — unioning tuner properties on load instead of overwriting them, removing its callbacks on unload, and raising informative errors from AutoTuner accessors on an untrained model. Both are the marks of a package used as a dependency more than as a destination.

◆ Prediction

With rush pinned to 1.2.0 and the compatibility shims gone, the next release is likely to expose more of the async path through callbacks rather than change the tuning interface.

S
stacks
ANALYTICS
0.0

Model stacking in tidymodels, quietly migrating off foreach and onto future

◆ Current state

stacks builds ensembles from tidymodels tuning results, and its release history is dominated by one long project: replacing foreach-based parallelism with the future framework. That transition completed in 1.1.0, where foreach backends began being ignored with a warning and the minimum R version rose to 4.1. Releases are infrequent and small, with the most recent being a CRAN re-submission rather than a change.

◆ Where it's heading

The package is mature and its remaining work is compatibility rather than capability — tracking the parallelism story across tidymodels, keeping object sizes sane after butchering and reloading, and staying aligned with recipes deprecations. The augment() method added for vetiver compatibility shows the same instinct: fit into the surrounding ecosystem rather than grow independently of it. Nothing in the visible history suggests new ensembling methods are being pursued.

◆ Prediction

With the future migration finished, the next release is most likely maintenance keeping pace with tune and recipes rather than anything users would notice.

Alternatives to mlr3tuning and stacks

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 mlr3tuning or stacks.

See all mlr3tuning alternatives → · See all stacks alternatives →

Recent activity from mlr3tuning and stacks

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 18d agomlr3tuningmlr3tuning 1.6.1 stops clobbering other packages' tuner properties
  2. 4mo agomlr3tuningmlr3tuning 1.6.0 aligns archive column order across tuning classes
  3. 8mo agomlr3tuningmlr3tuning 1.5.1 tracks xgboost 3.1.2.1
  4. 8mo agomlr3tuningmlr3tuning 1.5.0 adds queue evaluation stages to async callbacks
  5. 1y agomlr3tuningmlr3tuning 1.4.0 unifies logging under a base mlr3 logger
  6. 1y agostacksstacks 1.1.1 re-released to clear a CRAN check note
  7. 1y agostacksstacks 1.1.0 completes the move to future-based parallelism
  8. 1y agomlr3tuningmlr3tuning 1.3.0 adds a frozen async archive and leaner worker storage
  9. 2y agostacksstacks 1.0.5 fixes butchered stack size inflation
  10. 2y agostacksstacks 1.0.4 introduces future-based parallel processing
  11. 2y agostacksstacks 1.0.3 clears recipes deprecations and a type-check bug
  12. 3y agostacksstacks 1.0.2 adds an augment() method for vetiver compatibility

Frequently asked questions

What is the difference between mlr3tuning and stacks?

Both compete on the same themes — r-package — within Analytics. mlr3tuning is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is mlr3tuning better than stacks?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3tuning is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to mlr3tuning?

Top mlr3tuning alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3tuning alternatives" section above for the current picks, or visit /alternatives/mlr3tuning for the full list with editorial commentary on each.

What are the best alternatives to stacks?

Top stacks alternatives in Analytics are ranked by recent ship velocity. Browse the "stacks alternatives" section above for the current picks, or visit /alternatives/stacks for the full list with editorial commentary on each.