mmpca
Back from CRAN removal under a new maintainer, with the compiled layer rebuilt.
A side-by-side editorial comparison of mice and plssem — 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.
plssem took PLS-SEM into multilevel data, then spent two releases making the estimates trustworthy.
plssem is a young R implementation of partial least squares structural equation modelling, three CRAN releases old and shipping monthly. Its distinguishing work is the MC-PLS family — consistent PLS estimators the maintainer extended to mixed-effects designs in June — and the releases since have been about getting standard errors, admissibility and fit measures onto the same footing as the point estimates.
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.
plssem is a young R implementation of partial least squares structural equation modelling, three CRAN releases old and shipping monthly. Its distinguishing work is the MC-PLS family — consistent PLS estimators the maintainer extended to mixed-effects designs in June — and the releases since have been about getting standard errors, admissibility and fit measures onto the same footing as the point estimates.
The pattern is capability first, inference second. Multilevel MC-PLSc and MC-OrdPLSc arrived in 0.1.2 together with Monte-Carlo delta-method standard errors and a Polyak-Juditsky extrapolation step; 0.1.3 then extended delta-method errors to redundant parameters and thresholds, optimized their computation, added a loglikelihood-based fit measure and generated dynamic bounds to keep MC-PLS solutions admissible. Admissibility recurs throughout — penalized inadmissible solutions in 0.1.1, variance lower bounds and negative residual variance handling in 0.1.3, and an option to drop inadmissible bootstraps rather than silently include them. The release notes are pull-request lists, so the reasoning behind each change stays in the repository.
The MIMIC mode and GLS estimator both landed in the most recent release without the standard-error and fit-measure work that followed earlier additions, so extending inference to cover them is the natural next step. Bootstrap defaults moving to 500 replications suggests runtime is a live constraint and further optimization is likely.
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 plssem.
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.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. mice and plssem 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 plssem 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 plssem alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "plssem alternatives" section above for the current picks, or visit /alternatives/plssem for the full list with editorial commentary on each.