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distributional vs RadialMR

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

Shared themes:r-package

distributional vs RadialMR: at a glance

FeaturedistributionalRadialMR
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, probability-distributions, distribution-arithmetic, numerical-methodsmendelian-randomization, radial-plots, correctness-audit, statistical-inference
Last editorial update49m 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 RadialMR?

RadialMR's 2026 release corrects degrees of freedom that had been wrong since documentation.

RadialMR implements radial-plot formulations of IVW and MR-Egger Mendelian randomization, with outlier detection and interactive plotting. The recent history is thin on features and increasingly focused on the arithmetic: 1.2.4 in July 2026 fixes the degrees of freedom returned by egger_radial() to the documented n-2, corrects the heterogeneity p-value that inherited the same error, and repairs a negated lower bound in the random-effects bootstrap standard error search interval in ivw_radial().

Read the full RadialMR trajectory →

distributional vs RadialMR: 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.

R
RadialMR
ANALYTICS
0.0

RadialMR's 2026 release corrects degrees of freedom that had been wrong since documentation.

◆ Current state

RadialMR implements radial-plot formulations of IVW and MR-Egger Mendelian randomization, with outlier detection and interactive plotting. The recent history is thin on features and increasingly focused on the arithmetic: 1.2.4 in July 2026 fixes the degrees of freedom returned by egger_radial() to the documented n-2, corrects the heterogeneity p-value that inherited the same error, and repairs a negated lower bound in the random-effects bootstrap standard error search interval in ivw_radial().

◆ Where it's heading

This is a package being read closely by its maintainer rather than extended. The 1.2.x line pairs statistical corrections with defensive hardening — an rmr_format class check so unformatted input fails with a clear message, and plotly_radial() dispatching on object class instead of counting list elements. Both are the kind of change made while auditing, not while building.

◆ Prediction

Expect the audit to continue into the remaining estimator internals and print methods rather than new radial variants; the entries show no feature work queued.

Alternatives to distributional and RadialMR

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 RadialMR.

See all distributional alternatives → · See all RadialMR alternatives →

Recent activity from distributional and RadialMR

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

  1. 1mo agoRadialMRegger_radial() degrees of freedom corrected to n-2
  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. 3mo agoRadialMRroxygen2 bumped; package-level helpfile added
  6. 4mo agoRadialMRUnspecified codebase optimizations
  7. 5mo agodistributionalDirichlet and Horseshoe distributions added
  8. 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
  9. 1y agoRadialMRggplot2 v4 warning removed from plot_radial()
  10. 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families

Frequently asked questions

What is the difference between distributional and RadialMR?

Both compete on the same themes — r-package — within Analytics. distributional and RadialMR 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.

Is distributional better than RadialMR?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributional and RadialMR 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.

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 RadialMR?

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