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epikit vs kernelshap

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

epikit vs kernelshap: at a glance

Featureepikitkernelshap
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesepidemiology, field-data, date-handling, r-packageshap, model explainability, sampling algorithms, numerical correctness
Last editorial update1h ago8h ago
WebsiteVisit →Visit →

What is epikit?

epikit narrows to field-epidemiology helpers, handing proportions to a sibling package

epikit is a set of small helpers for applied epidemiology in R — age categorisation, date reconstruction from partial records, and related field-data chores, developed in the R4Epis orbit. Version 0.2.0 moved the proportion functions out to epitabulate, improved how find_date_cause(), find_start_date() and find_end_date() handle dates falling outside the period, and added a floor argument to age_categories() so the lowest band reads as under one rather than zero to zero.

Read the full epikit 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 →

epikit vs kernelshap: editorial side-by-side

E
epikit
ANALYTICS
0.0

epikit narrows to field-epidemiology helpers, handing proportions to a sibling package

◆ Current state

epikit is a set of small helpers for applied epidemiology in R — age categorisation, date reconstruction from partial records, and related field-data chores, developed in the R4Epis orbit. Version 0.2.0 moved the proportion functions out to epitabulate, improved how find_date_cause(), find_start_date() and find_end_date() handle dates falling outside the period, and added a floor argument to age_categories() so the lowest band reads as under one rather than zero to zero.

◆ Where it's heading

The package is being scoped down rather than built out. The 0.1.3 restructuring and the 0.2.0 handover of proportions to epitabulate are the same move made twice: push functionality into the package where it belongs and keep epikit to the toolkit that field epidemiologists reach for directly. The rest of the history is dependency compatibility work against dplyr and tibble.

◆ Prediction

With proportions gone and dependencies trimmed, the remaining functions cluster tightly around dates and age bands, so further refinement of the date-reconstruction helpers is more likely than new capability areas.

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 epikit 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 epikit or kernelshap.

See all epikit alternatives → · See all kernelshap alternatives →

Recent activity from epikit and kernelshap

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

  1. 9mo agoepikitProportion functions moved to epitabulate; date helpers warn correctly
  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
  8. 3y agoepikitFunctions rearranged across sibling packages
  9. 5y agoepikitRaise dplyr and tibble minimums; move CI to GitHub Actions
  10. 5y agoepikitCompatibility release for dplyr 1.0.0
  11. 6y agoepikitFirst CRAN release

Frequently asked questions

What is the difference between epikit and kernelshap?

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

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

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