rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
distributional vs rstatix
A side-by-side editorial comparison of distributional and rstatix — release velocity, themes, recent moves, and the top alternatives to consider.
distributional vs rstatix: 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 rstatix?
rstatix hit 1.0 by unrounding every p-value it has ever returned
rstatix is the pipe-friendly test wrapper behind most ggpubr annotation workflows — t-tests, Wilcoxon, ANOVA, post-hoc comparisons, effect sizes, all returning tidy data frames. After three quiet years of CRAN-compat patching, it shipped 1.0.0 and 1.1.0 three weeks apart in mid-2026. Both releases push in the same direction: interval estimates and full-precision output for numbers the package previously rounded or omitted.
distributional vs rstatix: 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.
rstatix
2.5ANALYTICS
rstatix hit 1.0 by unrounding every p-value it has ever returned
◆ Current state
rstatix is the pipe-friendly test wrapper behind most ggpubr annotation workflows — t-tests, Wilcoxon, ANOVA, post-hoc comparisons, effect sizes, all returning tidy data frames. After three quiet years of CRAN-compat patching, it shipped 1.0.0 and 1.1.0 three weeks apart in mid-2026. Both releases push in the same direction: interval estimates and full-precision output for numbers the package previously rounded or omitted.
◆ Where it's heading
The work is about matching what dedicated effect-size packages give you without taking on their dependencies. Confidence intervals for partial eta squared and for Cohen's d are both computed in base R from noncentral distributions and both check against effectsize; compact letter displays are computed in base R against multcompView. The pattern is deliberate — reproduce the reference implementation, add no imports. Alongside that, the package has started correcting statistical hygiene it got wrong for years, most visibly by no longer rounding p-values before adjusting them.
◆ Prediction
The analytic-interval machinery now exists for eta squared and Cohen's d; the untouched effect sizes in the package — eta squared for nonparametric tests, Cramer's V, rank-biserial correlation — are the obvious next targets for the same base-R noncentral treatment.
Alternatives to distributional and rstatix
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 rstatix.
audubon
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
affiner
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
r-ledger
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
See all distributional alternatives → · See all rstatix alternatives →
Recent activity from distributional and rstatix
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 22d agorstatixrstatix 1.1.0 gives Cohen's d a deterministic confidence interval
- 1mo agorstatixrstatix 1.0.0 stops rounding p-values before adjusting them ⚡
- 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
- 9mo agorstatixrstatix 0.7.3
- 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
- 3y agorstatixrstatix 0.7.2
- 3y agorstatixrstatix 0.7.1
- 5y agorstatixrstatix 0.7.0
Frequently asked questions
What is the difference between distributional and rstatix?
They serve adjacent needs but don't currently overlap on shipped themes. rstatix 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 rstatix?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rstatix 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 rstatix?
Top rstatix alternatives in Analytics are ranked by recent ship velocity. Browse the "rstatix alternatives" section above for the current picks, or visit /alternatives/rstatix for the full list with editorial commentary on each.