← Back to home
Comparison · Analytics

distributional vs mrbayes

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

Shared themes:r-package

distributional vs mrbayes: at a glance

Featuredistributionalmrbayes
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, probability-distributions, distribution-arithmetic, numerical-methodsmendelian-randomization, bayesian-inference, stan, jags
Last editorial update47m 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 mrbayes?

mrbayes spent 2026 auditing its own Bayesian MR estimators for coding errors.

mrbayes wraps Stan and JAGS implementations of Bayesian Mendelian randomization estimators — IVW, MR-Egger, radial Egger, and their multivariable forms. Most of the visible history is packaging work: dependency trimming, conditional examples so the package installs where JAGS will not compile, a maintainer handover. The 0.5.3 release in July 2026 breaks that pattern with a dense list of fixes inside the model code itself.

Read the full mrbayes trajectory →

distributional vs mrbayes: 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.

M
mrbayes
ANALYTICS
0.0

mrbayes spent 2026 auditing its own Bayesian MR estimators for coding errors.

◆ Current state

mrbayes wraps Stan and JAGS implementations of Bayesian Mendelian randomization estimators — IVW, MR-Egger, radial Egger, and their multivariable forms. Most of the visible history is packaging work: dependency trimming, conditional examples so the package installs where JAGS will not compile, a maintainer handover. The 0.5.3 release in July 2026 breaks that pattern with a dense list of fixes inside the model code itself.

◆ Where it's heading

The package has moved from packaging upkeep into a correctness-audit phase. 0.5.3 fixes a hardcoded three-exposure loop in MVMR-Egger reporting, a broken joint-prior branch, a sigma parameterization error in radial Egger, and several prior specifications — the profile of a maintainer reading their own model files closely rather than responding to bug reports. Platform work continues underneath: an R 4.3.0 floor inherited through a transitive dependency chain, and segfault fixes on macOS ARM.

◆ Prediction

Expect further audit-driven patches to the remaining rjags and Stan model files rather than new estimators; the fixes in 0.5.3 cluster in the Egger variants, which suggests that is where the reading is still in progress.

Alternatives to distributional and mrbayes

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 mrbayes.

See all distributional alternatives → · See all mrbayes alternatives →

Recent activity from distributional and mrbayes

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

  1. 1mo agomrbayesEstimator audit fixes MVMR-Egger loops and radial Egger sigma
  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. 5mo agodistributionalDirichlet and Horseshoe distributions added
  6. 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
  7. 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
  8. 1y agomrbayesMVMR rjags example gated on rjags being installed
  9. 1y agomrbayesExamples and tests skip when rstan or rjags is missing
  10. 1y agomrbayespkgdown site updated
  11. 1y agomrbayesHelper command added for installing JAGS
  12. 1y agomrbayesDependency surface trimmed; maintainer handover

Frequently asked questions

What is the difference between distributional and mrbayes?

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

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

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