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bpbounds vs kernelshap

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

bpbounds vs kernelshap: at a glance

Featurebpboundskernelshap
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themescausal inference, instrumental variables, r, partial identificationshap, model explainability, sampling algorithms, numerical correctness
Last editorial update1h ago2h ago
WebsiteVisit →Visit →

What is bpbounds?

bpbounds found the same swapped-cell bug twice and clamped its bounds back into range

bpbounds computes nonparametric Balke-Pearl bounds on the average causal effect from instrumental variable data, in the bivariate and trivariate cases. After years of pure packaging maintenance, the two 2026 releases are analytical corrections. Bounds on intervention probabilities are now clamped to [0, 1] so derived causal risk ratio bounds cannot fall outside their feasible range, and a cell-ordering error in the trivariate three-category instrument path has been repaired.

Read the full bpbounds 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 →

bpbounds vs kernelshap: editorial side-by-side

B
bpbounds
ANALYTICS
0.0

bpbounds found the same swapped-cell bug twice and clamped its bounds back into range

◆ Current state

bpbounds computes nonparametric Balke-Pearl bounds on the average causal effect from instrumental variable data, in the bivariate and trivariate cases. After years of pure packaging maintenance, the two 2026 releases are analytical corrections. Bounds on intervention probabilities are now clamped to [0, 1] so derived causal risk ratio bounds cannot fall outside their feasible range, and a cell-ordering error in the trivariate three-category instrument path has been repaired.

◆ Where it's heading

The direction is toward agreement with the reference Stata implementation and away from silently wrong output. The clamping change is described as matching the same fix in the Stata package, which suggests the two implementations are being reconciled rather than developed independently. The cell-ordering defect is the more instructive one: it was fixed in the calculation function in 0.1.7 and then again in the constraint matrix in 0.1.8, meaning the same x=0,y=1 / x=1,y=0 swap had been written in two places.

◆ Prediction

Since the recent fixes came from an external contributor's report and both touched the trivariate three-category path, the untested corners of that path are where further corrections would surface — but the release notes give no roadmap beyond parity with the Stata package.

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 bpbounds 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 bpbounds or kernelshap.

See all bpbounds alternatives → · See all kernelshap alternatives →

Recent activity from bpbounds and kernelshap

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

  1. 1mo agobpboundsbpbounds clamps probability bounds and fixes a constraint-matrix swap
  2. 2mo agobpboundsbpbounds fixes swapped cells in the trivariate calculation
  3. 1y agokernelshapKernel weight bug fixed; parallelism moves to doFuture
  4. 1y agokernelshapSampling permutation SHAP with standard errors
  5. 1y agokernelshapBackground data now optional; ranger survival support
  6. 2y agokernelshapFactor-valued predictions dropped
  7. 2y agobpboundsbpbounds 0.1.6
  8. 2y agokernelshapadditive_shap() explains additive models exactly
  9. 2y agokernelshapFaster on plain data.frames
  10. 3y agobpboundsbpbounds 0.1.5
  11. 6y agobpboundsVersion 0.1.4 on CRAN
  12. 7y agobpboundsVersion 0.1.3

Frequently asked questions

What is the difference between bpbounds and kernelshap?

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

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

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