fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of forrel and UCell — release velocity, themes, recent moves, and the top alternatives to consider.
forrel is getting faster at the simulations forensic kinship work actually spends its time on.
forrel handles forensic pedigree analysis: kinship likelihood ratios, profile simulation, relationship checking, and missing person calculations. Version 1.9.0 synced with pedtools 2.11.0's loop handling, which the release notes credit with enabling complex pedigrees that were previously intractable, and moved profileSim() to mirai for parallelism. It also added fEstimate() for inbreeding coefficients and parentChildLikelihood() as a fast path for the simplest case.
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.
forrel handles forensic pedigree analysis: kinship likelihood ratios, profile simulation, relationship checking, and missing person calculations. Version 1.9.0 synced with pedtools 2.11.0's loop handling, which the release notes credit with enabling complex pedigrees that were previously intractable, and moved profileSim() to mirai for parallelism. It also added fEstimate() for inbreeding coefficients and parentChildLikelihood() as a fast path for the simplest case.
Two long threads run through the window. One is making the common operations cheap: faster simulations through reorganized likelihood calculations, a dedicated parent-child path, dropped map attribute preservation, log-likelihoods to avoid underflow in kinshipLR(). The other is making relationship checking presentable, with checkPairwise() growing ggplot2 and plotly output, verbal relationship descriptions, and bootstrap p-values. Reference data is maintained alongside both, with the FORCE SNP panel completed and an X-chromosomal counterpart added.
With profileSim() on mirai and the loop handling synced, the next likely step is extending mirai parallelism to the other simulation-heavy functions such as exclusionPower() and the bootstrap in checkPairwise().
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 forrel 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 forrel 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. forrel and UCell 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. forrel and UCell 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 Analytics products to evaluate alongside.
Top forrel alternatives in Analytics are ranked by recent ship velocity. Browse the "forrel alternatives" section above for the current picks, or visit /alternatives/forrel 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.