rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
A side-by-side editorial comparison of distributional and gtsummary — release velocity, themes, recent moves, and the top alternatives to consider.
| Feature | distributional | gtsummary |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 0.0 | 0.0 |
| Sparks · 30d | 0 | 0 |
| Top themes | r-package, probability-distributions, distribution-arithmetic, numerical-methods | clinical-tables, analysis-results-data, regression-summaries, reproducible-reporting |
| Last editorial update | 49m ago | 1h ago |
| Website | Visit → | Visit → |
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.
gtsummary is quietly rebuilding itself around analysis results data, one table verb at a time.
gtsummary builds publication-ready summary, regression and survival tables for clinical and epidemiological work. Across this window it has grown in two directions at once: table composition primitives — splitting tables by rows and columns, stacking with labeled IDs, nested strata stacks, flexible merge columns — and a steadily deepening ARD layer, where tbl_ard_* functions, gather_ard() and the hierarchical table family expose the underlying analysis results data as a first-class object.
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.
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.
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.
gtsummary builds publication-ready summary, regression and survival tables for clinical and epidemiological work. Across this window it has grown in two directions at once: table composition primitives — splitting tables by rows and columns, stacking with labeled IDs, nested strata stacks, flexible merge columns — and a steadily deepening ARD layer, where tbl_ard_* functions, gather_ard() and the hierarchical table family expose the underlying analysis results data as a first-class object.
The ARD work is the through-line. Table IDs exist so gather_ard() can return a named list; hierarchical tables gained per-level sorting and targeted filtering; ARD inputs are pre-processed so sorting applies to non-standard shapes. The package is becoming a structured-results engine that happens to render tables, rather than a renderer alone. Alongside that, 2.2.0 restored data pre-processing that 2.0 had removed after the reduced functionality hurt users — a maintainer willing to reverse a major-version decision.
Expect the hierarchical and ARD functions, introduced as a preview without a full deprecation cycle, to keep stabilizing toward a settled API rather than new table types appearing.
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 gtsummary.
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
See all distributional alternatives → · See all gtsummary alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. distributional and gtsummary are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributional and gtsummary are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
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.
Top gtsummary alternatives in Analytics are ranked by recent ship velocity. Browse the "gtsummary alternatives" section above for the current picks, or visit /alternatives/gtsummary-r for the full list with editorial commentary on each.