mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of dscore and midr — release velocity, themes, recent moves, and the top alternatives to consider.
The D-score reference implementation rebuilt its measurement foundation on seven countries.
dscore computes the D-score and DAZ, the GSED developmental measurement used in child-health research, and it is the reference implementation rather than one option among several. The package is at 2.1.0 after a dense 2025: the default key moved from three-country to seven-country validation data, the licence moved from AGPL to Apache 2.0, and the 2.1.0 line added per-country references, full BSID-III coverage and domain-level scoring. Breaking changes are routine here and always come with a documented fallback key or algorithm argument.
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.
dscore computes the D-score and DAZ, the GSED developmental measurement used in child-health research, and it is the reference implementation rather than one option among several. The package is at 2.1.0 after a dense 2025: the default key moved from three-country to seven-country validation data, the licence moved from AGPL to Apache 2.0, and the 2.1.0 line added per-country references, full BSID-III coverage and domain-level scoring. Breaking changes are routine here and always come with a documented fallback key or algorithm argument.
The arc runs from correcting the instrument to broadening who can use it. The 2020-2024 releases were item-table repair and error correction, including a scale-factor bug that altered published standard errors; from 1.11.0 onward the work is distribution — a permissive licence, more instruments mapped in, and references resolved per country rather than pooled. That combination points at national-survey and app-embedded use rather than research-only use.
The 2.0.0 notes state that groundwork was laid for extending D-scores to older children, and 2.0.0 still tells users to fall back to gsed2406 for instruments outside GSED SF and LF. Expect the next releases to close that gap by mapping more instruments into gsed2510, with the older-age extension the likeliest headline feature.
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.
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 dscore or midr.
mice can finally predict, not just estimate, from multiply imputed data.
A market-microstructure toolkit that keeps adding estimators as the papers land.
A vowel-analysis package trimming dependencies after an email address got it archived.
The R half of the EMU speech database system, fixing what was quietly broken.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. dscore and midr 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. dscore and midr 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 dscore alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "dscore alternatives" section above for the current picks, or visit /alternatives/dscore for the full list with editorial commentary on each.
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.