mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of midr and statsExpressions — 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.
A statistics backend whose release history is mostly other people's weather
statsExpressions produces the tidy dataframes and plotmath expressions that ggstatsplot prints onto plots, and that position defines its changelog. Six of its ten most recent versions exist to absorb API changes in easystats, dplyr or purrr. Version 2.0.0 is the exception, cut to accompany ggstatsplot's own 1.0.0 and carrying pairwise Fisher's exact post hocs for contingency tables.
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.
statsExpressions produces the tidy dataframes and plotmath expressions that ggstatsplot prints onto plots, and that position defines its changelog. Six of its ten most recent versions exist to absorb API changes in easystats, dplyr or purrr. Version 2.0.0 is the exception, cut to accompany ggstatsplot's own 1.0.0 and carrying pairwise Fisher's exact post hocs for contingency tables.
This is a component settling into place beneath a larger package rather than a product with its own roadmap. New statistical content arrives rarely and narrowly — an exact-p toggle, one post-hoc function — while the recurring work is keeping expressions correct as the easystats stack shifts underneath. The one bug class it keeps returning to is rendering: p-values of exactly zero, decimal commas that plotmath parses as list separators.
Coupled this tightly, the next release is most likely another compatibility pass timed to an easystats or ggstatsplot version rather than new tests. Nothing in these entries signals an independent feature direction.
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 statsExpressions.
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 statsExpressions 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 statsExpressions 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 statsExpressions 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 statsExpressions alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "statsExpressions alternatives" section above for the current picks, or visit /alternatives/statsexpressions for the full list with editorial commentary on each.