JointFPM
Recurrent-event modelling settles, with mean_no() promoted to stable.
A side-by-side editorial comparison of plssem and tidyplots — release velocity, themes, recent moves, and the top alternatives to consider.
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.
tidyplots keeps rebuilding its own foundations rather than layering around them.
tidyplots wraps ggplot2 in a pipe-driven API aimed at publication-ready scientific figures, trading grammar-of-graphics flexibility for a shorter path to a finished plot. It is at 0.4.0 after two years of frequent releases, and almost every one carries a breaking change — the most recent moved multi-panel layout off patchwork and onto ggplot2's own faceting. Statistical annotation, colour schemes and size control have each been reworked at least once.
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.
tidyplots wraps ggplot2 in a pipe-driven API aimed at publication-ready scientific figures, trading grammar-of-graphics flexibility for a shorter path to a finished plot. It is at 0.4.0 after two years of frequent releases, and almost every one carries a breaking change — the most recent moved multi-panel layout off patchwork and onto ggplot2's own faceting. Statistical annotation, colour schemes and size control have each been reworked at least once.
The package is converging on ggplot2 rather than abstracting away from it: split_plot() now uses facet_wrap and facet_grid, as_tidyplot() was hard-deprecated on the grounds that converting a ggplot was never a good idea, and releases are timed against upstream ggplot2 versions. The other constant is the statistics surface, which has grown from basic error bars to paired and selected comparisons. Breaking changes are announced plainly and frequently, consistent with a package using 0.x to fix its shape before committing.
The patchwork removal is described as something that will eventually break dependent code, so the near-term work is likely completing that migration and settling the split_plot() parameters introduced alongside it. A 1.0 would signal the breaking-change cadence is ending, and nothing here indicates that yet.
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 plssem or tidyplots.
Recurrent-event modelling settles, with mean_no() promoted to stable.
Nonparametric change point detection swaps p-values for importance scores.
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.
mice can finally predict, not just estimate, from multiply imputed data.
See all plssem alternatives → · See all tidyplots alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. plssem and tidyplots 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. plssem and tidyplots 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 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.
Top tidyplots alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tidyplots alternatives" section above for the current picks, or visit /alternatives/tidyplots for the full list with editorial commentary on each.