tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
hardhat vs lime
A side-by-side editorial comparison of hardhat and lime — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-statsmachine-learning
hardhat vs lime: at a glance
What is hardhat?
hardhat keeps adding the contracts tidymodels needs next
hardhat is the infrastructure layer under tidymodels, defining the preprocessing and extraction contracts other packages implement. Its releases read as a list of new generics and vector classes: extract_postprocessor(), extract_fit_time(), extract_tailor(), and a quantile_pred() class for quantile-regression output. The newest release is narrow warning and missing-value handling in mold().
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.
hardhat vs lime: editorial side-by-side
hardhat
0.0ANALYTICS
hardhat keeps adding the contracts tidymodels needs next
◆ Current state
hardhat is the infrastructure layer under tidymodels, defining the preprocessing and extraction contracts other packages implement. Its releases read as a list of new generics and vector classes: extract_postprocessor(), extract_fit_time(), extract_tailor(), and a quantile_pred() class for quantile-regression output. The newest release is narrow warning and missing-value handling in mold().
◆ Where it's heading
Each addition here lands ahead of a user-facing feature elsewhere in tidymodels — the postprocessor and tailor generics precede the postprocessing workflow, quantile_pred() precedes quantile prediction in parsnip. The package's own surface stays deliberately small and its cadence follows what the rest of the stack is about to need.
◆ Prediction
Expect further extraction generics and prediction-type classes as tidymodels builds out postprocessing, with hardhat's own API remaining thin.
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 hardhat 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 hardhat 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.
See all hardhat alternatives → · See all lime alternatives →
Recent activity from hardhat and lime
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 4mo agohardhatmold() warning and quantile missing-value fixes
- 8mo agolimeCompatibility across all xgboost versions
- 11mo agohardhatextract_tailor() generic added
- 1y agohardhatquantile_pred() class for quantile regression output
- 2y agohardhatextract_postprocessor() and extract_fit_time() generics
- 2y agohardhatDocumentation topic renamed at CRAN's request
- 3y agohardhatMulti-outcome prediction helpers and one-hot factor encoding
- 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 hardhat and lime?
Both compete on the same themes — r-stats, machine-learning — within Analytics. hardhat 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 hardhat better than lime?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. hardhat 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 hardhat?
Top hardhat alternatives in Analytics are ranked by recent ship velocity. Browse the "hardhat alternatives" section above for the current picks, or visit /alternatives/hardhat 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.