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gridify vs kernelshap

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

Shared themes:r package

gridify vs kernelshap: at a glance

Featuregridifykernelshap
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr package, layout, tables, reportingshap, model explainability, sampling algorithms, numerical correctness
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is gridify?

gridify adds table pagination, the one visible feature in a single-release window.

gridify arranges plots and tables into layouts with headers and footers in R. Only one release is visible — 0.7.5 — carrying table pagination plus repository housekeeping in the form of an issue template and badges. The changelog is a pull-request list rather than written notes, so detail is thin.

Read the full gridify 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 →

gridify vs kernelshap: editorial side-by-side

G
gridify
ANALYTICS
0.0

gridify adds table pagination, the one visible feature in a single-release window.

◆ Current state

gridify arranges plots and tables into layouts with headers and footers in R. Only one release is visible — 0.7.5 — carrying table pagination plus repository housekeeping in the form of an issue template and badges. The changelog is a pull-request list rather than written notes, so detail is thin.

◆ Where it's heading

With one entry to read, direction can only be inferred from what it contains: pagination points the package toward long tables that do not fit a single output page, which is a reporting concern rather than a plotting one. Contributions come from a small named group. There is not enough history here to say whether that reporting emphasis is a trend.

◆ Prediction

Pagination usually pulls page-level controls behind it — repeating headers, row-count control — but the single entry available does not confirm any of that is planned.

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 gridify 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 gridify or kernelshap.

See all gridify alternatives → · See all kernelshap alternatives →

Recent activity from gridify and kernelshap

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

  1. 9mo agogridifyTable pagination added
  2. 1y agokernelshapKernel weight bug fixed; parallelism moves to doFuture
  3. 1y agokernelshapSampling permutation SHAP with standard errors
  4. 1y agokernelshapBackground data now optional; ranger survival support
  5. 2y agokernelshapFactor-valued predictions dropped
  6. 2y agokernelshapadditive_shap() explains additive models exactly
  7. 2y agokernelshapFaster on plain data.frames

Frequently asked questions

What is the difference between gridify and kernelshap?

Both compete on the same themes — r package — within Analytics. gridify 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 gridify better than kernelshap?

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

Top gridify alternatives in Analytics are ranked by recent ship velocity. Browse the "gridify alternatives" section above for the current picks, or visit /alternatives/gridify 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.