rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of midr and traumar — release velocity, themes, recent moves, and the top alternatives to consider.
A black-box interpreter reaches CRAN, then learns multi-class and survival responses
midr explains black-box models by fitting an interpretable surrogate through Maximum Interpretation Decomposition — main effects plus second-order interactions, with exact Shapley values for the surrogate. Two months after its first CRAN release it can take a matrix response, which covers multi-class classification and survival models, and hold collections of fitted interpretations in midlist and midrib objects for comparison.
Trauma registry statistics in R, tightening the numbers it reports against the literature.
traumar computes trauma-centre performance measures from registry data, including relative mortality metrics, SEQIC quality indicators and predicted survival probability. The recent releases are dominated by correctness rather than coverage: 1.2.5 changed how the denominator for SEQIC indicator 7 is derived so it reflects the definitive care population rather than a raw row count, and 1.2.3 realigned probability_of_survival() with published coefficients. Version 1.2.6 completes a deprecation, turning the old n_decimal argument into an error.
midr explains black-box models by fitting an interpretable surrogate through Maximum Interpretation Decomposition — main effects plus second-order interactions, with exact Shapley values for the surrogate. Two months after its first CRAN release it can take a matrix response, which covers multi-class classification and survival models, and hold collections of fitted interpretations in midlist and midrib objects for comparison.
The releases move outward along two axes at once: what can be interpreted, and how much of it fits in memory. Version 0.5.3 rebuilt the fitting path to avoid materialising large design matrices and added a save.memory option; 0.6.0 widened the response from a vector to a matrix and added parametric link functions. Class and argument names were shortened in the same release, so the package is still willing to break itself this early.
With multiple models now held in one object and visualisation methods for them, comparison across models is the surface most likely to fill out next — the collection classes exist but the notes describe manipulation and plotting rather than any comparison metric.
traumar computes trauma-centre performance measures from registry data, including relative mortality metrics, SEQIC quality indicators and predicted survival probability. The recent releases are dominated by correctness rather than coverage: 1.2.5 changed how the denominator for SEQIC indicator 7 is derived so it reflects the definitive care population rather than a raw row count, and 1.2.3 realigned probability_of_survival() with published coefficients. Version 1.2.6 completes a deprecation, turning the old n_decimal argument into an error.
This is a package whose outputs are reported numbers, and it is behaving accordingly: the notable changes in this window alter results rather than add features. A denominator computed as a row count instead of a filtered population, and a survival calculation not matching the coefficients it cites, are both the kind of defect that quietly propagates into published figures, and both were corrected here. Around that sit in-house validation helpers written deliberately to avoid a new dependency, and a steady tidy-up of tests and tooling.
Expect the validation family introduced in 1.2.4 to be applied more widely across the package's entry points, now that it exists and is deliberately kept internal. Further SEQIC indicators being reviewed against their definitions looks likely given indicator 7 needed it, though the entries name no specific one as next.
Other Infra & APIs 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 midr or traumar.
The recursive-computation engine under massProps grows the accessors its consumer needed
A mass-properties rollup spends a year on documentation and follows its sibling's API
Six months of releases and not one of them touched the scoring models
A cognitive-science sampling package ships once, then goes quiet for eighteen months
A Bayesian volatility sampler in its maintenance decade, paying for its own speed
Spatial thinning grows a result object, and the API breaks to make room for it
See all midr alternatives → · See all traumar alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. midr and traumar 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. midr and traumar 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 Infra & APIs products to evaluate alongside.
Top midr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "midr alternatives" section above for the current picks, or visit /alternatives/midr for the full list with editorial commentary on each.
Top traumar alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "traumar alternatives" section above for the current picks, or visit /alternatives/traumar for the full list with editorial commentary on each.