fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of b3gbi and UCell — release velocity, themes, recent moves, and the top alternatives to consider.
b3gbi pulled confidence intervals out of its indicator workflow and handed them to dubicube.
b3gbi computes biodiversity indicators from GBIF occurrence cubes for the B-Cubed project, and sits at 0.9.4 in a JOSS review run-up. The 0.9 release decoupled uncertainty from indicator calculation: confidence intervals are no longer produced inline but added afterward with add_ci(), backed by whole-cube bootstrapping from the sibling dubicube package. Everything since has been grid-parsing and compatibility repair around that split.
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.
b3gbi computes biodiversity indicators from GBIF occurrence cubes for the B-Cubed project, and sits at 0.9.4 in a JOSS review run-up. The 0.9 release decoupled uncertainty from indicator calculation: confidence intervals are no longer produced inline but added afterward with add_ci(), backed by whole-cube bootstrapping from the sibling dubicube package. Everything since has been grid-parsing and compatibility repair around that split.
Two forces are shaping releases. Internally, the uncertainty split produced an indicator-specific rule book — species-level indicators bootstrap the whole cube, raw counts resample within year, evenness gets a logit transform — and that rule book is where the statistical thinking now lives. Externally, GBIF's taxonomic backbone migration to the Catalogue of Life forced string taxon keys through process_cube() and the plotting paths, while recurring EEA and MGRS grid-code fixes mark coordinate parsing as the least settled area.
The 0.9.4 notes are entirely JOSS review items — contributors, examples, tracked datasets — so the next release is most likely a JOSS-accepted 1.0 rather than new indicator work.
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 b3gbi 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
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. b3gbi is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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. b3gbi is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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 b3gbi alternatives in Analytics are ranked by recent ship velocity. Browse the "b3gbi alternatives" section above for the current picks, or visit /alternatives/b3gbi 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.