rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
collapse vs kernelshap
A side-by-side editorial comparison of collapse and kernelshap — release velocity, themes, recent moves, and the top alternatives to consider.
collapse vs kernelshap: at a glance
What is collapse?
collapse got a JSS paper and a 7x fmean speedup in the same release.
collapse provides fast grouped statistical computing and data transformation for R, built on a C backend with its own grouping, hashing and aggregation primitives. The 2.1.x line is a maintenance and optimization series: SIMD multiple-accumulator work delivering roughly 2x on fsum() and 7x on fmean() for systems without OpenMP, a custom internal unlist() with better attribute preservation, and a steady stream of correctness fixes in collap(), pivot() and roworderv().
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.
collapse vs kernelshap: editorial side-by-side
collapse
0.0ANALYTICS
collapse got a JSS paper and a 7x fmean speedup in the same release.
◆ Current state
collapse provides fast grouped statistical computing and data transformation for R, built on a C backend with its own grouping, hashing and aggregation primitives. The 2.1.x line is a maintenance and optimization series: SIMD multiple-accumulator work delivering roughly 2x on fsum() and 7x on fmean() for systems without OpenMP, a custom internal unlist() with better attribute preservation, and a steady stream of correctness fixes in collap(), pivot() and roworderv().
◆ Where it's heading
The package is consolidating institutionally as much as technically. The repository moved to the fastverse organization with multiple people granted access, the Journal of Statistical Software paper landed as the primary citation, and documentation now includes an AI-generated interactive layer. Technically the focus is the hashing and grouping core — the decision to treat -0 and 0 as equal across funique(), group(), fmatch(), fmode() and their derivatives was made in sync with an equivalent change in Rcpp, and accepted a measured 3% cost to get it. The last release with breaking changes sits outside this six-entry window.
◆ Prediction
Expect further targeted performance work on the grouped statistical functions and continued small correctness fixes; the governance move to fastverse suggests contribution volume rather than direction is what the maintainer is managing.
kernelshap
0.0ANALYTICS
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 collapse 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 collapse or kernelshap.
audubon
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
affiner
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
r-ledger
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
See all collapse alternatives → · See all kernelshap alternatives →
Recent activity from collapse and kernelshap
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agocollapseSIMD accumulators give fmean a 7x speedup without OpenMP
- 7mo agocollapseNegative zero now hashes equal to zero across the package
- 8mo agocollapsecollap() no longer double-aggregates external weights
- 9mo agocollapseCustom unlist() preserves attributes
- 0y agocollapseAssorted bug fixes
- 1y agokernelshapKernel weight bug fixed; parallelism moves to doFuture
- 1y agokernelshapSampling permutation SHAP with standard errors ⚡
- 1y agocollapsena_insert gains by-reference mode; gsplit and pivot speed up
- 1y agokernelshapBackground data now optional; ranger survival support
- 2y agokernelshapFactor-valued predictions dropped
- 2y agokernelshapadditive_shap() explains additive models exactly
- 2y agokernelshapFaster on plain data.frames
Frequently asked questions
What is the difference between collapse and kernelshap?
They serve adjacent needs but don't currently overlap on shipped themes. collapse 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 collapse better than kernelshap?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. collapse 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 collapse?
Top collapse alternatives in Analytics are ranked by recent ship velocity. Browse the "collapse alternatives" section above for the current picks, or visit /alternatives/collapse-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.