tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
lime vs see
A side-by-side editorial comparison of lime and see — release velocity, themes, recent moves, and the top alternatives to consider.
lime vs see: at a glance
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.
What is see?
see grows wherever easystats adds a diagnostic, one plot method at a time.
see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.
lime vs see: editorial side-by-side
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.
see
0.0ANALYTICS
see grows wherever easystats adds a diagnostic, one plot method at a time.
◆ Current state
see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.
◆ Where it's heading
Growth here is downstream-driven rather than self-directed: see expands to cover new diagnostics as easystats produces them. Running alongside that is a sustained investment in presentation control — theme arguments on plot methods, elements that scale with base_size — which suits users embedding these plots in documents rather than glancing at them interactively.
◆ Prediction
Expect new plot methods to keep arriving in step with performance and parameters releases, with continued theming work rather than any change in the package's scope.
Alternatives to lime and see
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 see.
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 lime and see
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoseesee 0.14.1 adds plots for prior checks and grouped means
- 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
- 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
- 8mo agolimeCompatibility across all xgboost versions
- 11mo agoseesee 0.12.0 extends normality checks to psych factor models
- 1y agoseesee 0.11.0 scales theme elements with base_size
- 1y agoseesee 0.10.0 plots random-effect group levels for mixed models
- 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 lime and see?
They serve adjacent needs but don't currently overlap on shipped themes. lime and see 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 see?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. lime and see 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 see?
Top see alternatives in Analytics are ranked by recent ship velocity. Browse the "see alternatives" section above for the current picks, or visit /alternatives/see-r for the full list with editorial commentary on each.