fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of rmediation and UCell — release velocity, themes, recent moves, and the top alternatives to consider.
RMediation shipped a three-normal CDF, then found it was silently wrong.
RMediation is a long-standing CRAN package for confidence intervals on mediated effects, now built on an S7 class hierarchy. Over eight weeks it added ProductNormal3 for serial indirect effects of the form a1*a2*b, folded the engine into the existing pprodnormal naming family, and then replaced that engine outright after finding it returned wrong probabilities without warning. The dev branch is at 1.7.0; CRAN still serves 1.6.1.
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
UCell scores gene signatures in single-cell data using a rank-based metric that is robust to dataset composition. Its release history reads as a sequence of ecosystem accommodations: Bioconductor submission in 2.0, SmoothKNN() for k-nearest-neighbor smoothing of scores in 2.2, smoothing applied directly to expression slots in 2.4, Seurat v5 assay compatibility in 2.6, multi-layer Seurat v5 objects in 2.8, and a missing_genes parameter in 2.14 that lets callers impute or skip signature genes absent from the data. Version 2.16 tracks Bioconductor 3.23 and points at a new publication and a Python implementation, pyUCell.
RMediation is a long-standing CRAN package for confidence intervals on mediated effects, now built on an S7 class hierarchy. Over eight weeks it added ProductNormal3 for serial indirect effects of the form a1*a2*b, folded the engine into the existing pprodnormal naming family, and then replaced that engine outright after finding it returned wrong probabilities without warning. The dev branch is at 1.7.0; CRAN still serves 1.6.1.
The package is moving from a hand-rolled numerical layer to one that checks itself: the new default integrator escalates its node count until successive rules agree, warns when it hits the cap instead of returning a number, and exposes a diagnostics argument for the convergence estimate. The correctness fix went to dev ahead of the CRAN window rather than being held for it, which suggests wrong-answer bugs are treated as release-blocking regardless of cadence. Serial mediation is where the new surface area is concentrated.
1.7.0 exists specifically to land before CRAN's 2026-08-21 update window, so the next move is a CRAN submission promoting it to main; whether hcubature survives past that as a cross-check option is the open question.
UCell scores gene signatures in single-cell data using a rank-based metric that is robust to dataset composition. Its release history reads as a sequence of ecosystem accommodations: Bioconductor submission in 2.0, SmoothKNN() for k-nearest-neighbor smoothing of scores in 2.2, smoothing applied directly to expression slots in 2.4, Seurat v5 assay compatibility in 2.6, multi-layer Seurat v5 objects in 2.8, and a missing_genes parameter in 2.14 that lets callers impute or skip signature genes absent from the data. Version 2.16 tracks Bioconductor 3.23 and points at a new publication and a Python implementation, pyUCell.
Two threads run through this. The scoring algorithm itself has barely changed — the rank-based core is stable, and 2.14's reformatting to gene indices rather than string matching is a speed change, not a method change. What does change constantly is object-format compatibility, which is the tax of living between Seurat and SingleCellExperiment. The pyUCell reference in 2.16 is the first sign of the method reaching beyond R, though these notes say nothing about its scope.
The cadence is locked to Bioconductor's twice-yearly release train, so the next version will most likely accompany Bioconductor 3.24 with whatever Seurat or SingleCellExperiment changes it brings.
Other Analytics 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 rmediation or UCell.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all rmediation alternatives → · See all UCell alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. rmediation is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 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. rmediation is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top rmediation alternatives in Analytics are ranked by recent ship velocity. Browse the "rmediation alternatives" section above for the current picks, or visit /alternatives/rmediation for the full list with editorial commentary on each.
Top UCell alternatives in Analytics are ranked by recent ship velocity. Browse the "UCell alternatives" section above for the current picks, or visit /alternatives/ucell for the full list with editorial commentary on each.