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Comparison · Analytics
distributional vs vellum
A side-by-side editorial comparison of distributional and vellum — release velocity, themes, recent moves, and the top alternatives to consider.
distributional vs vellum: at a glance
What is distributional?
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
What is vellum?
vellum's bugs are now found by using it, not testing it — the downstream grammar is doing the QA.
The rendering engine shipped nine releases in the two weeks around the end of July, six of them on a single day. Almost every entry is a correctness fix in a capability that worked when drawn and failed when measured, or worked in isolation and failed in composition. The release notes are unusually forensic: each one states the mechanism, the observable symptom, and why the fix mirrors the draw path rather than reimplementing it.
distributional vs vellum: editorial side-by-side
distributional
0.0ANALYTICS
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
◆ Current state
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
◆ Where it's heading
The generic-computation thread is the one that matters and it has been building steadily: a Monte Carlo default method for cdf(), has_symmetry() to let algorithms specialise, hdr() moving to exact results for symmetric distributions and 4096 quantiles elsewhere, open-versus-closed support intervals. Version 0.8.0 is where that thread arrives somewhere — arithmetic on arbitrary distributions, with closed forms used when they exist and numerical convolution when they do not. The package is positioning itself as a computational layer rather than a catalogue, which is consistent with how weird and the forecasting packages consume it.
◆ Prediction
Expect the numerical machinery behind dist_convolved() to be reused for other operators, and more generics like has_symmetry() that let downstream algorithms take exact paths when a distribution supports them.
vellum
5.0ANALYTICS
vellum's bugs are now found by using it, not testing it — the downstream grammar is doing the QA.
◆ Current state
The rendering engine shipped nine releases in the two weeks around the end of July, six of them on a single day. Almost every entry is a correctness fix in a capability that worked when drawn and failed when measured, or worked in isolation and failed in composition. The release notes are unusually forensic: each one states the mechanism, the observable symptom, and why the fix mirrors the draw path rather than reimplementing it.
◆ Where it's heading
The pivotal detail is stated outright in 0.6.3 — the first bug in the series found by using the engine from vellumplot rather than testing it in isolation. Every release since names the downstream as the source: the contrast rule's false positives, the lint rules that fired on all five sample plots, the keyed roundrect batch. A rendering engine with a real grammar built on top of it is now getting the integration coverage that unit tests structurally cannot provide, and the fixes are converging on one theme: the measurement path and the draw path must not drift.
◆ Prediction
Expect the release rate to fall as the vellumplot integration surface is exhausted, with remaining work concentrated in the lint rule set now that it is meant to gate builds rather than just inform.
Alternatives to distributional and vellum
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 distributional or vellum.
epiworldR
epiworldR is a thin R shell whose releases track the C++ simulator underneath it
watina
A groundwater database client that has started doing the domain analysis too
inbospatial
A thin R wrapper over Flemish geospatial services, adding one standard at a time
Fluent Bit
Fluent Bit keeps two lines alive while the 5.x branch quietly opens 5.1.
OpenCTI
OpenCTI is rebuilding its connector layer into a marketplace and wiring the platform to XTM Hub
See all distributional alternatives → · See all vellum alternatives →
Recent activity from distributional and vellum
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 11d agovellumLinter grows to 20 rules and stops firing on every plot
- 13d agovellumAnimated SVGs no longer blink once and vanish or play in reverse
- 13d agovellumPick table now reports device pixels instead of two coordinate systems
- 14d agovellumContrast rule stops flagging every plot; gridlines become PDF artifacts
- 14d agovellumgrobwidth and grobheight now measure wrapped text, not the unwrapped line
- 15d agovellumKeyed roundrect becomes a real batch after downstream integration exposes it
- 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
- 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators ⚡
- 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
- 5mo agodistributionalDirichlet and Horseshoe distributions added
- 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
- 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
Frequently asked questions
What is the difference between distributional and vellum?
They serve adjacent needs but don't currently overlap on shipped themes. vellum 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.
Is distributional better than vellum?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. vellum 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.
What are the best alternatives to distributional?
Top distributional alternatives in Analytics are ranked by recent ship velocity. Browse the "distributional alternatives" section above for the current picks, or visit /alternatives/distributional-r for the full list with editorial commentary on each.
What are the best alternatives to vellum?
Top vellum alternatives in Analytics are ranked by recent ship velocity. Browse the "vellum alternatives" section above for the current picks, or visit /alternatives/vellum-r for the full list with editorial commentary on each.