rjdqa
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
A side-by-side editorial comparison of kernelshap and NWCTrends — 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.
The salmon status-review trend package, maintained one federal review cycle at a time
NWCTrends fits multivariate state-space trend models to Pacific salmon population data and generates the tables and figures used in NOAA Northwest Fisheries Science Center viability and status reviews. Its release history maps onto those review cycles rather than a development calendar: v1.0 carries the 2015 review code, v1.25 the 2020 review, v1.30 the changes since. The 2026 v1.31 is internal restructuring and a dependency swap.
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.
NWCTrends fits multivariate state-space trend models to Pacific salmon population data and generates the tables and figures used in NOAA Northwest Fisheries Science Center viability and status reviews. Its release history maps onto those review cycles rather than a development calendar: v1.0 carries the 2015 review code, v1.25 the 2020 review, v1.30 the changes since. The 2026 v1.31 is internal restructuring and a dependency swap.
Development is driven by reproducibility of a specific government reporting product, so most work goes into making the report generation configurable and the fitting assumptions explicit rather than into new modelling. The 2020 cycle removed hard-coded per-population hacks and made the fitting window an explicit argument; the 2023 cycle moved plot styling into package options and clarified how missing data and zeros are handled in the published tables.
The cadence suggests the next substantive release arrives with the next status review rather than before it, most likely continuing the move of report parameters out of function signatures and into structured configuration.
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 NWCTrends.
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
SimInf 10.0 turns an epidemic simulator into a tool that fits models to real time series
A young package porting Stata's egen row-wise helpers to the tidyverse, one function per release
A statistician's personal toolbox, growing one plotting utility at a time
R/qtl is in pure custodial mode: every recent release answers a compiler, not a user
See all kernelshap alternatives → · See all NWCTrends 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 NWCTrends 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 NWCTrends 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 NWCTrends alternatives in Analytics are ranked by recent ship velocity. Browse the "NWCTrends alternatives" section above for the current picks, or visit /alternatives/nwctrends-r for the full list with editorial commentary on each.