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Comparison · Analytics
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
| Feature | distributional | distributions3 |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 0.0 | 6.3 |
| Sparks · 30d | 0 | 1 |
| Top themes | r-package, probability-distributions, distribution-arithmetic, numerical-methods | r-package, probability-distributions, empirical-distributions, likelihood-inference |
| Last editorial update | 4d ago | 1h ago |
| Website | Visit → | 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.
What is distributions3?
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
distributional vs distributions3: 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.
distributions3
6.3ANALYTICS
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
◆ Current state
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
◆ Where it's heading
Growth used to arrive as new distribution families contributed from outside - the extreme-value set, Erlang, later the Poisson binomial. This release changes the axis: alongside two new distributions it adds an inference layer (score, hessian) and a forecast-evaluation one (crps() methods against scoringRules), which are capabilities about distributions rather than more of them. Dependency weight is being cut at the same time, with ggplot2 demoted to Suggests and glue replaced by base R sprintf().
◆ Prediction
With numeric fallbacks and the derivative generics in place, expect analytic score() and hessian() methods to be filled in across more of the distribution catalogue. The constructor-default change is the likeliest source of follow-up fixes, since calls like Poisson() now return a length-zero distribution where they previously errored.
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.
RStudio
RStudio ships through release branches, and the notes are commit messages
dbt Core
dbt Fusion's second beta is adapter work: ClickHouse gets materializations, indexes, and catalogs
Fulcrum
Fulcrum is betting its whole map stack on Esri, with a hard Google Maps cutoff on September 1.
Holistics
Holistics keeps fencing in the AI layer it spent the summer building.
Dovetail
Dovetail spent July opening doors to other tools and August making its own rooms easier to enter.
See all distributional alternatives → · See all distributions3 alternatives →
Recent activity from distributional and distributions3
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2h agodistributions3Empirical distributions, plus score and hessian generics ⚡
- 28d agodistributions3Maintenance moves to Achim Zeileis; moment calculations corrected
- 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
- 10mo agodistributions3ggplot2 compatibility for the plotting functions
- 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
- 1y agodistributions3Poisson binomial distribution, with a normal-approximation fallback
- 3y agodistributions3is_discrete and is_continuous generics, plus elementwise type-safety
- 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 6.3 vs 0.0), with 1 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 6.3 vs 0.0), with 1 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.