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distributions3 vs hstats

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

distributions3 vs hstats: at a glance

Featuredistributions3hstats
SectorAnalyticsAnalytics
Velocity score6.30.0
Sparks · 30d10
Top themesr-package, probability-distributions, empirical-distributions, likelihood-inferenceinteraction statistics, partial dependence, model explainability, r package
Last editorial update4h ago4d ago
WebsiteVisit →Visit →

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.

Read the full distributions3 trajectory →

What is hstats?

hstats settled into maintenance after its 1.0 restructuring, with model coverage the only thing still growing.

hstats computes Friedman's H-statistics, partial dependence, ICE curves and permutation importance for any model exposing a prediction function. The releases in view are consolidation: performance work on plain data.frames, ICE facetting for multioutput models, ranger survival support, and a ggplot 4.0 compatibility pass in 2025. The package moved to the ModelOriented organisation in 1.2.0.

Read the full hstats trajectory →

distributions3 vs hstats: editorial side-by-side

D6.3

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.

H
hstats
ANALYTICS
0.0

hstats settled into maintenance after its 1.0 restructuring, with model coverage the only thing still growing.

◆ Current state

hstats computes Friedman's H-statistics, partial dependence, ICE curves and permutation importance for any model exposing a prediction function. The releases in view are consolidation: performance work on plain data.frames, ICE facetting for multioutput models, ranger survival support, and a ggplot 4.0 compatibility pass in 2025. The package moved to the ModelOriented organisation in 1.2.0.

◆ Where it's heading

The structural work — the hstats_matrix object, quantile approximation, revised plotting — landed in 1.0.0 just outside this window, and nothing since has changed the package's shape. What continues is model-coverage plumbing: mlr3 classification modes, ranger survival behind a survival argument, and factor predictions added in 1.1.0 then removed again in 1.2.0. The most recent releases are compatibility-driven, tracking ggplot2 rather than the interaction statistics.

◆ Prediction

Expect the next release to be another dependency-compatibility pass or a new model backend working out of the box, rather than new interaction statistics.

Alternatives to distributions3 and hstats

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 distributions3 or hstats.

See all distributions3 alternatives → · See all hstats alternatives →

Recent activity from distributions3 and hstats

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

  1. 5h agodistributions3Empirical distributions, plus score and hessian generics
  2. 29d agodistributions3Maintenance moves to Achim Zeileis; moment calculations corrected
  3. 10mo agodistributions3ggplot2 compatibility for the plotting functions
  4. 10mo agohstatsggplot 4.0 compatibility and test coverage
  5. 1y agodistributions3Poisson binomial distribution, with a normal-approximation fallback
  6. 2y agohstatsranger survival models supported out of the box
  7. 2y agohstatsMoves to ModelOriented; factor predictions removed
  8. 2y agohstatsICE facets for multioutput models; mlr3 fixes
  9. 2y agohstatsFaster data.frame paths; NaN H-statistics fixed
  10. 2y agohstatsFactor predictions and line-style 2D partial dependence
  11. 3y agodistributions3is_discrete and is_continuous generics, plus elementwise type-safety
  12. 4y agodistributions3Extreme-value family, Erlang, and a plotting generic

Frequently asked questions

What is the difference between distributions3 and hstats?

They serve adjacent needs but don't currently overlap on shipped themes. 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 distributions3 better than hstats?

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 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.

What are the best alternatives to hstats?

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