fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of lstar and robma — release velocity, themes, recent moves, and the top alternatives to consider.
A single-cell data store commits to Zarr v3 and range-readable hosting across four language surfaces
lstar stores single-cell data behind one C++ core with Python, R and JS/WASM bindings, and ships a browser viewer that reads the store directly. Zarr v3 is now the default on-disk format across all four surfaces, with zstd compression and sharding that packs many chunks into fewer objects. Viewer stores are compressed per field and resolved at chunk granularity, so a hosted viewer fetches only what it displays. The tag stream carries both lstar and lstar-sc releases.
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
lstar stores single-cell data behind one C++ core with Python, R and JS/WASM bindings, and ships a browser viewer that reads the store directly. Zarr v3 is now the default on-disk format across all four surfaces, with zstd compression and sharding that packs many chunks into fewer objects. Viewer stores are compressed per field and resolved at chunk granularity, so a hosted viewer fetches only what it displays. The tag stream carries both lstar and lstar-sc releases.
The through-line is making a hosted store cheap to read. Sharding addresses the file-per-chunk explosion that makes many-chunk arrays awkward to host; per-field compression with chunk-granular resolution means colouring an embedding by one gene fetches one column rather than an array. The 0.2.x patches are the cost of maintaining four surfaces at once — a WASM heap crash that only browsers exercise, and a count-basis orientation defect where all three surfaces normalized in memory and none owned the on-disk layout.
The orientation bug's root cause — no surface owning the on-disk representation while all three normalized in memory — is the kind of gap that usually produces a validation or ownership change rather than another point fix.
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
The 3.x series solved the modeling problem and left an interface problem behind: a caller had to know which of six constructors matched their data type, and argument names differed across them. 4.0.0 resolves that by making the model family a set of arguments rather than a function name, and by standardizing input naming on metafor-style conventions. It shipped one day after BayesTools 0.3.0, the author's own upstream infrastructure package, whose new standardization and prior-transformation machinery this rewrite depends on.
A rewrite this wide usually needs a follow-up, so expect 4.0.x patches addressing migration gaps as users hit the removed constructors and renamed arguments.
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 lstar or robma.
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. lstar is currently shipping more aggressively (velocity 5.0 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. lstar is currently shipping more aggressively (velocity 5.0 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 lstar alternatives in Analytics are ranked by recent ship velocity. Browse the "lstar alternatives" section above for the current picks, or visit /alternatives/lstar for the full list with editorial commentary on each.
Top robma alternatives in Analytics are ranked by recent ship velocity. Browse the "robma alternatives" section above for the current picks, or visit /alternatives/robma for the full list with editorial commentary on each.