pr2database
The protist reference database keeps widening past the rRNA gene it was built on.
A side-by-side editorial comparison of humind and mice — release velocity, themes, recent moves, and the top alternatives to consider.
The MSNI humanitarian needs framework as code, rewritten and re-broken every year
humind turns household survey data into Multi-Sector Needs Index severity scores across WASH, Protection, SNFI, Food Security, Education and Health. Its version line tracks the annual MSNI framework revision — 2024.x, 2025.x, 2026.x — with narrow correctness patches between rollouts. v2026.2.0 is the current rollout and the most structural one in the visible history: water-quantity scoring moved out into a new mandatory prerequisite, food-security severity now comes from a different matrix, and the impactR4PHU runtime dependency is gone.
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.
humind turns household survey data into Multi-Sector Needs Index severity scores across WASH, Protection, SNFI, Food Security, Education and Health. Its version line tracks the annual MSNI framework revision — 2024.x, 2025.x, 2026.x — with narrow correctness patches between rollouts. v2026.2.0 is the current rollout and the most structural one in the visible history: water-quantity scoring moved out into a new mandatory prerequisite, food-security severity now comes from a different matrix, and the impactR4PHU runtime dependency is gone.
Two things move together. The framework content is revised yearly — indicators added, weights corrected, instruments swapped — and the package keeps absorbing pipeline it used to delegate, most visibly by vendoring add_fcs(), add_hhs(), add_rcsi(), add_lcsi() and add_fcm_phase() locally rather than importing them. Each rollout is explicitly breaking and the release notes have grown per-function 'Action:' instructions, which reads as maintainers who expect every downstream dashboard to need rewiring on the same annual clock.
The 2025 line settled into narrow patches immediately after its rollout — 1.2, 1.3 and 1.4 fixed a separator argument, a schema rename and a shelter misclassification rather than adding indicators. Expect the 2026 line to do the same: correctness fixes against the new WASH, FCLCM and shelter-damage logic before any further framework change.
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.
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 humind or mice.
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.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. humind is currently shipping more aggressively (velocity 3.8 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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. humind is currently shipping more aggressively (velocity 3.8 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
Top humind alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "humind alternatives" section above for the current picks, or visit /alternatives/humind for the full list with editorial commentary on each.
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.