OHPL
A 2017 chemometrics method frozen in place, visited only when CRAN changes its documentation rules.
A side-by-side editorial comparison of kernelshap and tidypolars — release velocity, themes, recent moves, and the top alternatives to consider.
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.
tidypolars is grinding toward complete dplyr coverage, one supported function at a time
tidypolars lets you write dplyr and tidyr syntax against Polars DataFrames and LazyFrames. Its releases follow a fixed shape: raise the required polars version, add a handful of newly supported R functions and arguments, fix places where behaviour diverges from dplyr. Recent additions run from %notin% and as.integer() to .before/.after in mutate() and time zone handling in datetime parsing. Cadence is roughly every six to ten weeks and has not varied.
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.
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.
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.
tidypolars lets you write dplyr and tidyr syntax against Polars DataFrames and LazyFrames. Its releases follow a fixed shape: raise the required polars version, add a handful of newly supported R functions and arguments, fix places where behaviour diverges from dplyr. Recent additions run from %notin% and as.integer() to .before/.after in mutate() and time zone handling in datetime parsing. Cadence is roughly every six to ten weeks and has not varied.
Coverage is the whole strategy, and the target has been widening from dplyr into tidyr — unnest_longer_polars(), separate_longer_delim_polars() and separate_longer_position_polars() bring list-column and string-splitting verbs that have no Polars-idiomatic equivalent in the tidyverse dialect. The other consistent thread is fidelity: distinct() dropping unselected columns, summarize() dropping the last group, relocate() honouring tidy-select helpers, NULL in mutate() behaving as dplyr does. Each of these is a small breaking change made to match the reference rather than to differ from it.
The pattern of tracking the polars floor upward every release and following tidyverse changes closely — .by in fill() arrived when tidyr 1.3.2 shipped it — suggests the next releases continue mirroring new dplyr and tidyr arguments rather than adding a distinct capability.
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 kernelshap or tidypolars.
A 2017 chemometrics method frozen in place, visited only when CRAN changes its documentation rules.
A fast dplyr stand-in that keeps finding new places to skip work entirely.
Belgium's invasive-species indicator toolkit is in steady refinement, one plotting edge case at a time.
The ICES stock assessment client took upload away in 2024 and spent two years giving it back.
A discrete global grid generator grew cell traversal and became a usable spatial index.
Community ecology's standard toolkit is retiring the functions a generation of scripts was built on.
See all kernelshap alternatives → · See all tidypolars alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. kernelshap and tidypolars 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. kernelshap and tidypolars 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.
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.
Top tidypolars alternatives in Analytics are ranked by recent ship velocity. Browse the "tidypolars alternatives" section above for the current picks, or visit /alternatives/tidypolars for the full list with editorial commentary on each.