mmpca
Back from CRAN removal under a new maintainer, with the compiled layer rebuilt.
A side-by-side editorial comparison of mice and robscale — release velocity, themes, recent moves, and the top alternatives to consider.
mice can finally predict, not just estimate, from multiply imputed data.
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
A robust-statistics package rewrote its estimators in SIMD C++ and went to CRAN in two weeks.
robscale computes robust scale and location estimators, and its pitch is speed: 21 to 26 times faster than stats::mad, 37 times faster than stats::IQR on small samples, with comparable margins over robustbase for Qn and Sn. The March 2026 releases took it from a GitHub project to a CRAN package carrying eleven estimators, all with confidence intervals.
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
Two things are happening. The imputation method catalogue keeps widening — lasso variants, multivariate PMM, categorical PMM via canonical correlation — while the pooling side is being extended past its original purpose, first to synthetic data, now to predictions on held-out sets. That second thread points at predictive modelling workflows rather than the inferential ones mice was built for. Meanwhile the maintainers keep finding consequential old bugs: the augment() ordered-factor defect in 3.18.0 had been silently degrading ordinal imputations for years.
predict_mi() is framed around evaluating predictive performance on test sets, and the ignore argument added in 3.12.0 already exists to hold out rows from the imputation model. Expect the next work to join those up into a fuller train/test story for imputed data, since the pieces are now in place but not yet connected.
robscale computes robust scale and location estimators, and its pitch is speed: 21 to 26 times faster than stats::mad, 37 times faster than stats::IQR on small samples, with comparable margins over robustbase for Qn and Sn. The March 2026 releases took it from a GitHub project to a CRAN package carrying eleven estimators, all with confidence intervals.
Three releases in a fortnight walk a clear line: expand the public API, submit to CRAN, then tune. The 0.5.4 work is where that tuning shows, and it is unusually specific about hardware, raising sorting-network thresholds after benchmarking and dropping the AVX-512 path entirely in favour of a shorter AVX2-first dispatch chain. The build-fix lists are long, which is what a package fighting compiler and TBB variation across CRAN's platforms looks like.
The dispatch hierarchy has been simplified once already; further releases most likely continue narrowing the SIMD surface and hardening the configure step rather than adding estimators. A new estimator would be the surprise.
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 mice or robscale.
Back from CRAN removal under a new maintainer, with the compiled layer rebuilt.
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 mice alternatives → · See all robscale alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. mice and robscale 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. mice and robscale 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 mice alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "mice alternatives" section above for the current picks, or visit /alternatives/mice for the full list with editorial commentary on each.
Top robscale alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "robscale alternatives" section above for the current picks, or visit /alternatives/robscale for the full list with editorial commentary on each.