pr2database
The protist reference database keeps widening past the rRNA gene it was built on.
A side-by-side editorial comparison of mice and serocalculator — 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 seroincidence engine grows up: whole API renamed, then clustered survey designs
serocalculator turns cross-sectional antibody measurements into infection-rate estimates, and it spent its last two releases making itself safe to depend on. Version 1.4.0 renamed nearly every user-facing function into a consistent est_seroincidence()/sr_params vocabulary; 1.4.1 shipped the migration crosswalk that admits how much that broke. The newest capability is cluster-robust variance estimation for household- and school-based surveys.
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.
serocalculator turns cross-sectional antibody measurements into infection-rate estimates, and it spent its last two releases making itself safe to depend on. Version 1.4.0 renamed nearly every user-facing function into a consistent est_seroincidence()/sr_params vocabulary; 1.4.1 shipped the migration crosswalk that admits how much that broke. The newest capability is cluster-robust variance estimation for household- and school-based surveys.
The arc runs from method to instrument. Early releases added example data and plotting; recent ones fix the API surface, satisfy CRAN's offline-failure policy, and extend the estimator to sampling designs field epidemiology actually uses — multi-level clustering, stratification, and the two combined. Each release also carries visible refactoring discipline (one function per file, linting, per-PR website previews) that reads like a package preparing for contributors it does not have yet.
With cluster_var and stratum_var now threaded through both est_seroincidence() and est_seroincidence_by(), survey weights are the remaining piece of a complex-survey design the sandwich estimator does not cover. The entries do not name it, so read that as direction rather than a promise.
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 serocalculator.
The protist reference database keeps widening past the rRNA gene it was built on.
Composable aligned layouts, rebuilt on S7 while ggplot2 4.0 lands underneath.
Conservation planning absorbs the literature's target-setting rules as code.
Joint species distribution models in Gibbs-sampled C++, quiet since 2023.
An ecosystem model starts tracking carbon isotopes and land-use change.
Ten years in, US mapping splits its data out and finally adds Puerto Rico.
See all mice alternatives → · See all serocalculator 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 serocalculator 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 serocalculator 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 serocalculator alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "serocalculator alternatives" section above for the current picks, or visit /alternatives/serocalculator for the full list with editorial commentary on each.