broom.helpers
After eleven quiet months, the tidier under gtsummary is back to absorbing model classes.
A side-by-side editorial comparison of haze and vecvec — release velocity, themes, recent moves, and the top alternatives to consider.
Mesh interpolation drops its custom fork dependency and sheds weight.
haze does per-vertex smoothing and interpolation on triangular meshes, aimed at mapping neuroimaging surface data between subjects, with k-d tree lookup and interpolation in C++. After three years dormant it has shipped twice in a month: a modernization pass to get through R CMD check, and now the removal of its dependency on a custom Rvcg build. It has never been on CRAN because of package size.
A lazy vector container keeps closing the gaps where it quietly materialised anyway.
vecvec provides an R class that holds multiple vectors as one logical vector without copying them together, for cases where concatenating would be wasteful. The 1.0.0 rewrite moved the class off vctrs onto S7 while keeping user-facing code working, and added matrix and array behaviour. Since then the work has been about whether the abstraction actually saves anything: 1.3.0 makes duplicated(), equality proxies, casting and array formatting compute slot-wise instead of materialising, and adds vecvec_mapply() to apply a function across several vecvecs at once.
haze does per-vertex smoothing and interpolation on triangular meshes, aimed at mapping neuroimaging surface data between subjects, with k-d tree lookup and interpolation in C++. After three years dormant it has shipped twice in a month: a modernization pass to get through R CMD check, and now the removal of its dependency on a custom Rvcg build. It has never been on CRAN because of package size.
Both recent releases point at the same obstacle. Needing a patched Rvcg meant users could not install from any normal source, and package size is the stated reason CRAN was ruled out at the first release; this release removes the first barrier and starts on the second by deleting unused data files. Nothing has been added to the interpolation surface since 2022.
The direction of travel suggests a CRAN attempt once the size problem is solved, though the package has not said so and the earlier note put it ten times over the limit.
vecvec provides an R class that holds multiple vectors as one logical vector without copying them together, for cases where concatenating would be wasteful. The 1.0.0 rewrite moved the class off vctrs onto S7 while keeping user-facing code working, and added matrix and array behaviour. Since then the work has been about whether the abstraction actually saves anything: 1.3.0 makes duplicated(), equality proxies, casting and array formatting compute slot-wise instead of materialising, and adds vecvec_mapply() to apply a function across several vecvecs at once.
The arc runs from proving the idea to making it cheap, and 1.3.0 is the widest pass yet at the second half. Early releases established constructors and vctrs dispatch; 1.0.0 rebuilt the internals on S7; the three releases since have worked through the operations that were quietly defeating the point — printing, duplicate detection, casting, equality — and made each compute on storage slots rather than elements. ALTREP detection has moved from parsing .Internal(inspect()) output to a C-level check, the same work on a firmer footing. The other visible thread is a widening apply surface: vec_apply() per vector, now vecvec_mapply() across several.
With the main vctrs operations converted to slot-wise computation, the remaining materialisation points are the natural next target; the entries name no specific one, and the internal structure reserved at 1.0.0 still leaves room for the faster special-case representations flagged then.
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 haze or vecvec.
After eleven quiet months, the tidier under gtsummary is back to absorbing model classes.
OpenHouse starts adding per-column defaults while still closing silent-failure holes.
Julia's distribution library keeps filing down the edges where sampling meets array types
Power BI's monthly grind: authoring defaults, DAX documentation, and mobile finally catching up.
ggquiver is awake again, fixing arrow scaling that quietly misread irregular data.
A mesh renderer grows a scene graph and an exit route into glTF.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. haze is currently shipping more aggressively (velocity 5.0 vs 2.5), 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. haze is currently shipping more aggressively (velocity 5.0 vs 2.5), 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 haze alternatives in Analytics are ranked by recent ship velocity. Browse the "haze alternatives" section above for the current picks, or visit /alternatives/haze for the full list with editorial commentary on each.
Top vecvec alternatives in Analytics are ranked by recent ship velocity. Browse the "vecvec alternatives" section above for the current picks, or visit /alternatives/vecvec for the full list with editorial commentary on each.