mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of melodi and midr — release velocity, themes, recent moves, and the top alternatives to consider.
INSEE's statistics API gets a French R client that keeps meeting its edge cases
Rmelodi is InseeFrLab's R client for the Melodi APIs, which serve French official statistics. It reached 1.0.0 in February 2026 with the technical call parameters moved out of function arguments and into options(), and a per-request row limit raised to 100,000 on the server side. Everything since has been dataset-specific: field names that vary between datasets, geography labels, performance.
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.
Rmelodi is InseeFrLab's R client for the Melodi APIs, which serve French official statistics. It reached 1.0.0 in February 2026 with the technical call parameters moved out of function arguments and into options(), and a per-request row limit raised to 100,000 on the server side. Everything since has been dataset-specific: field names that vary between datasets, geography labels, performance.
The work is convergence with an API that is still moving. Version 0.3.0 added label lookups so codes become readable; 1.0.0 centralised configuration; 1.0.1 and 1.0.2 each fix a place where a real dataset does not match the assumed shape — get_range_geo() needing an extra label field, then the consumer price index series naming its value column differently from every other dataset. Release notes are in French, which is consistent with the audience.
The 1.0.x pattern is one dataset-shape exception per release, which suggests the client is still discovering how much the Melodi datasets vary rather than converging on a general parser. Expect more of the same until the variation is handled generically.
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 melodi 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.
They serve adjacent needs but don't currently overlap on shipped themes. melodi 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. melodi 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 melodi alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "melodi alternatives" section above for the current picks, or visit /alternatives/melodi 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.