JointFPM
Recurrent-event modelling settles, with mean_no() promoted to stable.
A side-by-side editorial comparison of mice and surveytidy — 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.
surveytidy taught every dplyr verb to operate on a whole collection of surveys at once.
surveytidy is the tidyverse-facing half of a two-package survey stack, wrapping survey design objects from surveycore so filter, mutate, select and the rest work on them while carrying variable labels, value labels and a transformation log alongside the data. The May release extended that verb surface to survey_collection, the abstraction surveycore uses to hold several surveys as one object, so a pipeline written once dispatches across every member.
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.
surveytidy is the tidyverse-facing half of a two-package survey stack, wrapping survey design objects from surveycore so filter, mutate, select and the rest work on them while carrying variable labels, value labels and a transformation log alongside the data. The May release extended that verb surface to survey_collection, the abstraction surveycore uses to hold several surveys as one object, so a pipeline written once dispatches across every member.
The package is being built in two layers that arrive in order: vector-level transformations first, structural dispatch second. The make_* family in 0.4.0 handles the recoding that survey work actually consists of — labelled to factor, multi-level to dichotomous, scale reversal, valence flipping — with value labels propagating automatically. Collection support then applies data-masking, tidyselect, grouping, slicing and collapsing verbs per survey, with joins explicitly refused and a typed message reporting which surveys were skipped. Metadata fidelity is the recurring bug source: labels surviving across(), stale labels left behind by recoding, the transformation log keeping up with what the verbs did.
Joins are the one verb family that errors on collections, with users directed to join before constructing the collection, so that restriction is the clearest outstanding gap. The re-export of surveycore's collection constructors suggests the package is positioning itself as the single import users need, which points toward more re-exports as surveycore's stable API settles.
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 surveytidy.
Recurrent-event modelling settles, with mean_no() promoted to stable.
Nonparametric change point detection swaps p-values for importance scores.
A Prism-styled ggplot2 theme in maintenance, now surviving ggplot2 4.0.
Wavelet trend estimation tightens the defaults it shipped with.
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.
See all mice alternatives → · See all surveytidy 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 surveytidy 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 surveytidy 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 surveytidy alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "surveytidy alternatives" section above for the current picks, or visit /alternatives/surveytidy for the full list with editorial commentary on each.