tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
bonsai vs lime
A side-by-side editorial comparison of bonsai and lime — release velocity, themes, recent moves, and the top alternatives to consider.
bonsai vs lime: at a glance
What is bonsai?
bonsai keeps widening tidymodels' boosted-tree engine bench, catboost most recently
bonsai exists to attach non-core tree engines to parsnip's boost_tree() and rand_forest(), and the release history reads as a steady accumulation of them: partykit, aorsf, lightgbm, and now catboost. The 0.4.x line is spent making catboost behave like a full tidymodels citizen rather than adding anything new.
What is lime?
lime survives on compatibility patches years after its research moment
lime brings local interpretable model-agnostic explanations to R. Its substantive development finished around 0.5.0 in 2019, which added argument pass-through to predict(), a gower_pow tuning knob and a batch of fixes. Since then there have been three releases: a namespace fix, a maintainer handover to Emil Hvitfeldt with general upkeep, and a patch to work across xgboost versions.
bonsai vs lime: editorial side-by-side
bonsai
0.0ANALYTICS
bonsai keeps widening tidymodels' boosted-tree engine bench, catboost most recently
◆ Current state
bonsai exists to attach non-core tree engines to parsnip's boost_tree() and rand_forest(), and the release history reads as a steady accumulation of them: partykit, aorsf, lightgbm, and now catboost. The 0.4.x line is spent making catboost behave like a full tidymodels citizen rather than adding anything new.
◆ Where it's heading
Each engine follows the same arc — land it, then close the gaps that keep it from tuning cleanly (parameter naming, multi_predict, threading, case weights). Recent work is squarely in that second phase for catboost, with dials supplying the matching parameter objects on its own release schedule. Bug-fix density is high relative to new surface.
◆ Prediction
Expect the catboost integration to keep filling in tuning and GPU-related arguments before any further engine is added; the entries give no signal about which engine would come next.
lime
0.0ANALYTICS
lime survives on compatibility patches years after its research moment
◆ Current state
lime brings local interpretable model-agnostic explanations to R. Its substantive development finished around 0.5.0 in 2019, which added argument pass-through to predict(), a gower_pow tuning knob and a batch of fixes. Since then there have been three releases: a namespace fix, a maintainer handover to Emil Hvitfeldt with general upkeep, and a patch to work across xgboost versions.
◆ Where it's heading
The package is in custodial maintenance — kept installable and compatible with the model packages it explains, rather than developed. The 2022 handover is the most consequential entry in the window because it determined that the package would keep getting patches at all.
◆ Prediction
Expect the next release to be another compatibility fix triggered by an upstream model package, not new explanation methods.
Alternatives to bonsai and lime
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 bonsai or lime.
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.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
Recent activity from bonsai and lime
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agobonsaicatboost gains multi_predict() and corrected tuning parameters
- 8mo agolimeCompatibility across all xgboost versions
- 1y agobonsaicatboost engine added to boost_tree()
- 1y agobonsailightgbm accepts sparse matrices for fit and predict
- 2y agobonsaiaorsf fit failure in multisession workers fixed
- 2y agobonsaiaorsf engine added; lightgbm gains dataset params and case weights
- 3y agobonsailightgbm num_leaves becomes tunable; alias arguments disallowed
- 3y agolimeMaintainer handover to Emil Hvitfeldt
- 5y agolimeorder() fix and lighter dependencies
- 6y agolimeNamespace fix following glmnet changes
- 7y agolimeexplain() gains pass-through args and gower_pow tuning
- 8y agolimeh2o support, NA handling and date feature types
Frequently asked questions
What is the difference between bonsai and lime?
They serve adjacent needs but don't currently overlap on shipped themes. bonsai and lime 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 bonsai better than lime?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bonsai and lime 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 bonsai?
Top bonsai alternatives in Analytics are ranked by recent ship velocity. Browse the "bonsai alternatives" section above for the current picks, or visit /alternatives/bonsai-r for the full list with editorial commentary on each.
What are the best alternatives to lime?
Top lime alternatives in Analytics are ranked by recent ship velocity. Browse the "lime alternatives" section above for the current picks, or visit /alternatives/lime-r for the full list with editorial commentary on each.