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distributional vs distributions3

A side-by-side editorial comparison of distributional and distributions3 — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-packageprobability-distributions

distributional vs distributions3: at a glance

Featuredistributionaldistributions3
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesr-package, probability-distributions, distribution-arithmetic, numerical-methodsr-package, probability-distributions, statistical-modelling, maintainership
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

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.

Read the full distributional trajectory →

What is distributions3?

distributions3 changes hands to Achim Zeileis, and a moment calculation bug goes with it.

An R package giving probability distributions a consistent object interface — d/p/q/r functions, moments, and prodist() methods that extract a fitted distribution from a regression object. Releases are infrequent, roughly one a year, and the last one is largely administrative: Achim Zeileis takes over maintenance from Alex Hayes, with all URLs and documentation updated to match, alongside a fix to incorrect moment calculations reported by a user.

Read the full distributions3 trajectory →

distributional vs distributions3: editorial side-by-side

D0.0

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.

D2.5

distributions3 changes hands to Achim Zeileis, and a moment calculation bug goes with it.

◆ Current state

An R package giving probability distributions a consistent object interface — d/p/q/r functions, moments, and prodist() methods that extract a fitted distribution from a regression object. Releases are infrequent, roughly one a year, and the last one is largely administrative: Achim Zeileis takes over maintenance from Alex Hayes, with all URLs and documentation updated to match, alongside a fix to incorrect moment calculations reported by a user.

◆ Where it's heading

The package's growth has come in two modes. Early releases absorbed whole families of distributions from outside contributors — the extreme-value set, Erlang, later the Poisson binomial — while later ones tightened the interface itself with is_discrete() and is_continuous() generics and elementwise type-safety when applying a distribution vector to a numeric vector. The handover is the notable event in the current window: maintenance moves to the author of the surrounding statistical ecosystem this package already integrates with through prodist() and countreg, which suggests the interface work will continue over the distribution-collection work.

◆ Prediction

Expect closer alignment with Zeileis's own packages, with prodist() coverage widening to more model classes; the entries here do not indicate whether new distribution families remain on the agenda.

Alternatives to distributional and distributions3

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 distributions3.

See all distributional alternatives → · See all distributions3 alternatives →

Recent activity from distributional and distributions3

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 24d agodistributions3Maintenance moves to Achim Zeileis; moment calculations corrected
  2. 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
  3. 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators
  4. 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
  5. 5mo agodistributionalDirichlet and Horseshoe distributions added
  6. 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
  7. 10mo agodistributions3ggplot2 compatibility for the plotting functions
  8. 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
  9. 1y agodistributions3Poisson binomial distribution, with a normal-approximation fallback
  10. 3y agodistributions3is_discrete and is_continuous generics, plus elementwise type-safety
  11. 4y agodistributions3Extreme-value family, Erlang, and a plotting generic

Frequently asked questions

What is the difference between distributional and distributions3?

Both compete on the same themes — r-package, probability-distributions — within Analytics. distributions3 is currently shipping more aggressively (velocity 2.5 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 distributions3?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributions3 is currently shipping more aggressively (velocity 2.5 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 distributions3?

Top distributions3 alternatives in Analytics are ranked by recent ship velocity. Browse the "distributions3 alternatives" section above for the current picks, or visit /alternatives/distributions3-r for the full list with editorial commentary on each.