← Back to home
Comparison · Analytics

lime vs probably

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

lime vs probably: at a glance

Featurelimeprobably
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesinterpretability, machine-learning, r-stats, maintenancecalibration, conformal-inference, tidymodels, uncertainty
Last editorial update1h ago47m ago
WebsiteVisit →Visit →

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.

Read the full lime trajectory →

What is probably?

The package that made calibration a step instead of an afterthought.

probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.

Read the full probably trajectory →

lime vs probably: editorial side-by-side

L
lime
ANALYTICS
0.0

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.

P
probably
ANALYTICS
0.0

The package that made calibration a step instead of an afterthought.

◆ Current state

probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.

◆ Where it's heading

The recent releases are about making these objects survive leaving the session. butcher and required_pkgs() methods are what a model needs to be pinned, containerised and served, and their arrival alongside workflows adding a tailor postprocessing stage and vetiver adding probably support points the same way: calibration is being moved out of analysis scripts and into the deployed pipeline. The cal_*_none() reference implementations are the tell that calibration is now something people tune rather than apply once.

◆ Prediction

Expect the calibration functions to be reachable directly from a tuned workflow's postprocessing stage rather than applied to predictions afterwards, following the tailor integration that workflows just shipped.

Alternatives to lime and probably

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 lime or probably.

See all lime alternatives → · See all probably alternatives →

Recent activity from lime and probably

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

  1. 8mo agolimeCompatibility across all xgboost versions
  2. 10mo agoprobablyConformal objects gain required_pkgs() and butcher methods
  3. 1y agoprobablyggplot2 test updates and a clearer validation-set error
  4. 1y agoprobablyCalibration internals refactored; isotonic bootstrap bug fixed
  5. 2y agoprobablyFix grouping sensitivity to variable type
  6. 3y agoprobablySplit conformal and conformal quantile regression added
  7. 3y agoprobablyCalibration and conformal inference arrive in tidymodels
  8. 3y agolimeMaintainer handover to Emil Hvitfeldt
  9. 5y agolimeorder() fix and lighter dependencies
  10. 6y agolimeNamespace fix following glmnet changes
  11. 7y agolimeexplain() gains pass-through args and gower_pow tuning
  12. 8y agolimeh2o support, NA handling and date feature types

Frequently asked questions

What is the difference between lime and probably?

They serve adjacent needs but don't currently overlap on shipped themes. lime and probably 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 lime better than probably?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. lime and probably 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 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.

What are the best alternatives to probably?

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