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ggmagnify vs kernelshap

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

ggmagnify vs kernelshap: at a glance

Featureggmagnifykernelshap
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesggplot2, data-visualization, inset-plots, r-packageshap, model explainability, sampling algorithms, numerical correctness
Last editorial update49m ago9h ago
WebsiteVisit →Visit →

What is ggmagnify?

A single-purpose ggplot2 inset tool, refining the same three arguments.

ggmagnify draws magnified insets of a region of a ggplot, with projection lines connecting the inset to its source area. The visible releases are all small refinements to how that inset looks — corner radius, fill between projection lines — plus one fix for inset themes being overridden. There are only three entries, so the picture is necessarily partial.

Read the full ggmagnify trajectory →

What is kernelshap?

kernelshap makes permutation SHAP practical past eight features, then fixes the kernel weights it had wrong.

kernelshap computes model-agnostic SHAP values in R through Kernel SHAP, permutation SHAP and an exact additive explainer. Version 0.8.0 added a sampling permutation-SHAP algorithm with standard errors and early stopping, lifting the practical feature ceiling past what the exact method allows. Version 0.9.0 then corrected a bug in how kernel weights were computed — exact Kernel SHAP now agrees with exact permutation SHAP — and moved parallelism from foreach to doFuture.

Read the full kernelshap trajectory →

ggmagnify vs kernelshap: editorial side-by-side

G
ggmagnify
ANALYTICS
0.0

A single-purpose ggplot2 inset tool, refining the same three arguments.

◆ Current state

ggmagnify draws magnified insets of a region of a ggplot, with projection lines connecting the inset to its source area. The visible releases are all small refinements to how that inset looks — corner radius, fill between projection lines — plus one fix for inset themes being overridden. There are only three entries, so the picture is necessarily partial.

◆ Where it's heading

Work concentrates on the visual finish of the inset rather than on new capability, which is what a package with one job should look like. Two feature releases a week apart in early 2024 suggest a short burst of attention rather than sustained development, and the feed goes quiet after mid-2024.

◆ Prediction

Too few entries to call a direction with confidence; continued small styling arguments would be consistent with what is visible.

K
kernelshap
ANALYTICS
0.0

kernelshap makes permutation SHAP practical past eight features, then fixes the kernel weights it had wrong.

◆ Current state

kernelshap computes model-agnostic SHAP values in R through Kernel SHAP, permutation SHAP and an exact additive explainer. Version 0.8.0 added a sampling permutation-SHAP algorithm with standard errors and early stopping, lifting the practical feature ceiling past what the exact method allows. Version 0.9.0 then corrected a bug in how kernel weights were computed — exact Kernel SHAP now agrees with exact permutation SHAP — and moved parallelism from foreach to doFuture.

◆ Where it's heading

Two concerns drive this package: making exact methods reach further, and being demonstrably right. The first shows in the additive explainer, the optional background dataset and the sampling permutation algorithm; the second in unit tests written against Python's shap, credited fixes from outside contributors, and a willingness to ship a correctness fix that changes numbers people have already published. Speed work runs continuously underneath — direct solves replacing the Moore-Penrose pseudo-inverse, roughly 10% less memory.

◆ Prediction

The 0.6.0 and 0.7.0 notes each promised a stable 1.0.0 that has not arrived; with the weighting bug fixed and parallelism reworked, a 1.0 release is the most plausible next step.

Alternatives to ggmagnify and kernelshap

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 ggmagnify or kernelshap.

See all ggmagnify alternatives → · See all kernelshap alternatives →

Recent activity from ggmagnify and kernelshap

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

  1. 1y agokernelshapKernel weight bug fixed; parallelism moves to doFuture
  2. 1y agokernelshapSampling permutation SHAP with standard errors
  3. 1y agokernelshapBackground data now optional; ranger survival support
  4. 2y agokernelshapFactor-valued predictions dropped
  5. 2y agoggmagnifyFixes inset theme override on supplied plots
  6. 2y agokernelshapadditive_shap() explains additive models exactly
  7. 2y agoggmagnifyAdds fill between projection lines
  8. 2y agoggmagnifyAdds corner radius for target and inset
  9. 2y agokernelshapFaster on plain data.frames

Frequently asked questions

What is the difference between ggmagnify and kernelshap?

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

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggmagnify and kernelshap 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 ggmagnify?

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

What are the best alternatives to kernelshap?

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