pr2database
The protist reference database keeps widening past the rRNA gene it was built on.
A side-by-side editorial comparison of ggfootball and mice — release velocity, themes, recent moves, and the top alternatives to consider.
A football-viz package just swapped scraping for an API and broke its own output to do it.
ggfootball is a small R package for plotting expected-goals and shot data, sourced from Understat. Four releases are visible. The 0.2.x line was argument tidying and dependency pruning; 0.3.0 replaced the data-acquisition layer wholesale, moving get_match_shots() from HTML parsing onto Understat's AJAX endpoints and changing the returned column names in the process.
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.
ggfootball is a small R package for plotting expected-goals and shot data, sourced from Understat. Four releases are visible. The 0.2.x line was argument tidying and dependency pruning; 0.3.0 replaced the data-acquisition layer wholesale, moving get_match_shots() from HTML parsing onto Understat's AJAX endpoints and changing the returned column names in the process.
The direction is away from scraped HTML and toward a thinner, more defensible package: four dependencies dropped in 0.3.0 on top of qdapRegex in 0.2.1, input validation added, error messages rewritten. Both breaking changes so far were accepted rather than deferred, which reads as a maintainer treating pre-1.0 as the window to get the shape right. The package is willing to break callers for structural reasons, not cosmetic ones.
With the scraper rebuilt and the dependency surface trimmed, the next releases are likely to stabilise the new column names and extend the plotting side, which has seen nothing since 0.2.0. A 1.0 would be the signal that the data structure is now considered fixed.
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 ggfootball 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.
See all ggfootball alternatives → · See all mice alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. ggfootball and mice 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. ggfootball and mice 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 ggfootball alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggfootball alternatives" section above for the current picks, or visit /alternatives/ggfootball 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.