mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of midr and qol — 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 SAS-to-R comfort layer that has quietly grown into its own dialect.
qol is a one-maintainer R package aimed at analysts moving from SAS: SAS-shaped verbs (compute., if./else_if., retain_value, do_if blocks), format-driven tabulation through any_table()/summarise_plus(), and styled Excel output as the default destination. Releases land roughly monthly and each one is large. The recent line has shifted from adding verbs to letting conditions be written as parsed character strings, which is the closest the package gets to reproducing SAS syntax inside R.
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.
qol is a one-maintainer R package aimed at analysts moving from SAS: SAS-shaped verbs (compute., if./else_if., retain_value, do_if blocks), format-driven tabulation through any_table()/summarise_plus(), and styled Excel output as the default destination. Releases land roughly monthly and each one is large. The recent line has shifted from adding verbs to letting conditions be written as parsed character strings, which is the closest the package gets to reproducing SAS syntax inside R.
Three threads are visible across these releases. Syntax fidelity is the newest: ifelse_multi() introduced character-string conditions with SAS-style writing, and if./else_if. immediately picked the style up. Tabulation flexibility is the constant — any_table() gains per-variable statistic selection, nested variable combinations in brackets, vector order_by, compute support. The third is ecosystem plumbing the maintainer builds when a gap appears: file I/O in 1.3.0, a console message system, global style options, macro variables, and in 1.3.2 a code_statistics() script scanner. Renames to dodge data.table and dplyr masking recur often enough to be a pattern.
The maintainer flagged the new percentile behaviour as a first iteration that only works with few grouping variables, so a performance pass on it is the clearest outstanding item. Beyond that the character-condition syntax has reached three functions in two releases and looks likely to spread to the remaining filter-bearing verbs.
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 qol.
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. midr and qol 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 qol 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 qol alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "qol alternatives" section above for the current picks, or visit /alternatives/qol for the full list with editorial commentary on each.