mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of midr and Rmonize — 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.
Collapsed a pile of parameters into one object and renamed every report column
Rmonize supports data harmonization: taking heterogeneous input datasets, applying processing rules against a DataSchema, and producing a harmonized dossier with assessment, summary and visual reports. Version 2.0.0 reshaped how that is driven — the evaluate, summarize and visualize functions now take the dossier alone rather than six or seven parallel arguments — and renamed every column in the assessment and summary outputs into plain language. The package is closely coupled to madshapR, whose changes the notes warn may require updates to existing user code.
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.
Rmonize supports data harmonization: taking heterogeneous input datasets, applying processing rules against a DataSchema, and producing a harmonized dossier with assessment, summary and visual reports. Version 2.0.0 reshaped how that is driven — the evaluate, summarize and visualize functions now take the dossier alone rather than six or seven parallel arguments — and renamed every column in the assessment and summary outputs into plain language. The package is closely coupled to madshapR, whose changes the notes warn may require updates to existing user code.
The arc runs from correctness toward interface. Version 1.0.1 was bug fixes found on real data, 1.1.0 added a debug parameter so harmonization could be tested with incomplete inputs, and 2.0.0 is a deliberate simplification that breaks existing code in exchange for a smaller surface. Renaming outputs from expressions like 'Categories::missing' and 'Nb. non-valid values' to 'Non-valid categories' and 'Number of non-valid values' points at reports being read by people who are not the person who wrote the harmonization rules.
Expect the superseded parameters and the renamed demo object to be removed outright rather than left superseded, and continued work on the visual reports, which carry the largest volume of referenced issues across all three versions.
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 Rmonize.
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.
See all midr alternatives → · See all Rmonize alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. midr and Rmonize 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 Rmonize 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 Rmonize alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "Rmonize alternatives" section above for the current picks, or visit /alternatives/rmonize for the full list with editorial commentary on each.