ggprism
A Prism-styled ggplot2 theme in maintenance, now surviving ggplot2 4.0.
A side-by-side editorial comparison of mice and PEIMAN2 — 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.
PEIMAN2 cut its annotation database loose from its release cycle without breaking CRAN.
PEIMAN2 does enrichment analysis over post-translational modifications, testing whether a protein list is enriched for particular PTMs against UniProt-derived annotations, with translation functions bridging to mass spectrometry workflows. Its answers are only as current as its bundled database, and until June that database could only be refreshed by releasing a new package version. Version 1.1.0 changes that.
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.
PEIMAN2 does enrichment analysis over post-translational modifications, testing whether a protein list is enriched for particular PTMs against UniProt-derived annotations, with translation functions bridging to mass spectrometry workflows. Its answers are only as current as its bundled database, and until June that database could only be refreshed by releasing a new package version. Version 1.1.0 changes that.
The package has been moving from a fixed snapshot toward versioned, user-selectable data. Earlier releases updated the bundled database in place — 1.0.0 shipped the March 2025 version and said little else — which meant the annotation vintage was whatever the package version implied. Now update_peiman_database() downloads and caches external database files and UniProt PTM lists, enrichment workflows take a database_version argument, and the mass-spec translators take a ptmlist_version, so an analysis can pin a dated database rather than a package release. The CRAN-safe default is preserved deliberately: loading, examples and checks still use the bundled internal data and need no network.
Version pinning is now expressible but the release notes do not describe how a chosen version is recorded in output, so surfacing the active database version in results is the natural companion. The database and the UniProt PTM list are versioned separately, which leaves room for a combined manifest.
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 PEIMAN2.
A Prism-styled ggplot2 theme in maintenance, now surviving ggplot2 4.0.
Wavelet trend estimation tightens the defaults it shipped with.
Back from CRAN removal under a new maintainer, with the compiled layer rebuilt.
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.
See all mice alternatives → · See all PEIMAN2 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 PEIMAN2 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 PEIMAN2 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 PEIMAN2 alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "PEIMAN2 alternatives" section above for the current picks, or visit /alternatives/peiman2 for the full list with editorial commentary on each.