mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of anticlust and midr — release velocity, themes, recent moves, and the top alternatives to consider.
Anticlustering keeps absorbing constraints — must-link, cannot-link, blocks, missing data
anticlust partitions a set of items into groups that are as similar to each other as possible — assembling matched stimulus sets, balanced experimental conditions, comparable teaching groups. The current surface is one function, anticlustering(), with a growing list of things it will honour: must-link and cannot-link constraints, categorical variables as factors, NAs, blocking by a categorical level, and a choice among exact solvers and heuristics.
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.
anticlust partitions a set of items into groups that are as similar to each other as possible — assembling matched stimulus sets, balanced experimental conditions, comparable teaching groups. The current surface is one function, anticlustering(), with a growing list of things it will honour: must-link and cannot-link constraints, categorical variables as factors, NAs, blocking by a categorical level, and a choice among exact solvers and heuristics.
Two years of releases have gone almost entirely into constraint handling and input tolerance rather than new objectives. The methods list grew by absorbing outside work — the three-phase search of Yang et al. contributed by an external author, a 2PML heuristic for must-link problems, a Gurobi backend for the exact formulations. Meanwhile categories_to_binary() has been rewritten twice, which is where the recent bugs have come from.
The constraint types now interact combinatorially — blocks, must-link, cannot-link, missing values and each objective — and the last two releases were both regressions in the encoding layer underneath them. Consolidating that layer is the more likely next move than another solver.
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 anticlust 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.
See all anticlust alternatives → · See all midr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. anticlust 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. anticlust 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 anticlust alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "anticlust alternatives" section above for the current picks, or visit /alternatives/anticlust 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.