broom.helpers
After eleven quiet months, the tidier under gtsummary is back to absorbing model classes.
A side-by-side editorial comparison of Distributions.jl and vecvec — release velocity, themes, recent moves, and the top alternatives to consider.
Julia's distribution library keeps filing down the edges where sampling meets array types
Distributions.jl ships small, frequent releases against a large and settled API surface. The newest release accepts any AbstractVecOrMat when sampling from an MvNormal, closing a reported case where rand! ran much slower for an AbstractMatrix than for a plain Matrix. Around it sits the usual mix of per-distribution correctness fixes, fitting support such as sufficient statistics and MLE for Chi and Chisq, and dependency pruning.
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.
Distributions.jl ships small, frequent releases against a large and settled API surface. The newest release accepts any AbstractVecOrMat when sampling from an MvNormal, closing a reported case where rand! ran much slower for an AbstractMatrix than for a plain Matrix. Around it sits the usual mix of per-distribution correctness fixes, fitting support such as sufficient statistics and MLE for Chi and Chisq, and dependency pruning.
The arc is consolidation rather than expansion, and this release is a clean example: the fix is not a new distribution but a signature loosened so the library behaves the same whatever array type callers hand it. Together with earlier sparsity tracing through constructors and looser MvNormal type aliases, the direction is a package that composes predictably with the rest of the Julia numerical stack instead of one that grows new surface.
Expect the same cadence of per-distribution fixes and fitting-method additions, with further signature loosening where concrete array types are still assumed. Nothing in these entries signals a major version or API break.
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 Distributions.jl 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.
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.
Mesh interpolation drops its custom fork dependency and sheds weight.
A mesh renderer grows a scene graph and an exit route into glTF.
See all Distributions.jl alternatives → · See all vecvec alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Distributions.jl 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. Distributions.jl 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 Distributions.jl alternatives in Analytics are ranked by recent ship velocity. Browse the "Distributions.jl alternatives" section above for the current picks, or visit /alternatives/distributions-jl 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.