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RBesT vs tidymodels

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

RBesT vs tidymodels: at a glance

FeatureRBesTtidymodels
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesbayesian-statistics, clinical-trials, stan, r-languagetidymodels, meta-package, dependency-management, namespace-conflicts
Last editorial update1h ago46m ago
WebsiteVisit →Visit →

What is RBesT?

RBesT is teaching its Bayesian decision rules to answer two-sided questions.

RBesT builds meta-analytic-predictive priors — the machinery for borrowing historical control data into a new trial — and evaluates the operating characteristics of decisions made with them. The stable line has spent several releases on effective sample size: ESS for normal mixtures via a new `family` argument, boundary corrections when no responses or no non-responses are observed, and stabilised ELIR computations. The 1.9-0 release candidate extends the normal, binomial and Poisson outcome functions to two-sided decisions.

Read the full RBesT trajectory →

What is tidymodels?

The meta-package ships almost nothing, which is exactly what a version-pinning shim should do

The tidymodels package is a loader and version pin for the modeling framework's core set rather than a place where features live. Its entire changelog consists of updated dependency versions, adjustments to how tidymodels_prefer() resolves name conflicts against other packages, and the occasional addition of a package to the core set — workflowsets in 0.1.3, tailor in 1.4.0. The most recent releases moved the package's own code from the magrittr pipe to R's base pipe and patched a bug where some attached packages were omitted.

Read the full tidymodels trajectory →

RBesT vs tidymodels: editorial side-by-side

R
RBesT
ANALYTICS
0.0

RBesT is teaching its Bayesian decision rules to answer two-sided questions.

◆ Current state

RBesT builds meta-analytic-predictive priors — the machinery for borrowing historical control data into a new trial — and evaluates the operating characteristics of decisions made with them. The stable line has spent several releases on effective sample size: ESS for normal mixtures via a new `family` argument, boundary corrections when no responses or no non-responses are observed, and stabilised ELIR computations. The 1.9-0 release candidate extends the normal, binomial and Poisson outcome functions to two-sided decisions.

◆ Where it's heading

Two currents run through the changelog. One is ESS hardening — nearly every release since 1.7-4 fixes another edge case where the ELIR calculation aborted or returned something unstable, which is what happens when a quantity used to justify prior strength to regulators gets scrutinised. The other is Stan and brms integration debt: array syntax updates, a minimum Stan version bump, truncated prior generation for `mixstanvar`, deterministic EM. The RC's contributor list shows a second active maintainer, and the work is broader than any recent stable release.

◆ Prediction

The release candidate covers all three outcome families and has already absorbed a round of review comments, so the next step is most likely the 1.9-0 CRAN release itself rather than further feature work.

T
tidymodels
ANALYTICS
0.0

The meta-package ships almost nothing, which is exactly what a version-pinning shim should do

◆ Current state

The tidymodels package is a loader and version pin for the modeling framework's core set rather than a place where features live. Its entire changelog consists of updated dependency versions, adjustments to how tidymodels_prefer() resolves name conflicts against other packages, and the occasional addition of a package to the core set — workflowsets in 0.1.3, tailor in 1.4.0. The most recent releases moved the package's own code from the magrittr pipe to R's base pipe and patched a bug where some attached packages were omitted.

◆ Where it's heading

Release cadence tracks the ecosystem rather than any roadmap of its own: a version bump when member packages release, a tidymodels_prefer() rule when a new conflict appears — DALEX::explains() over dplyr::explains(), recipes::update() over other update() methods. Additions to the core set are the only structurally interesting events, and there have been two in seven releases. Everything else is plumbing that exists so a single library() call attaches a consistent set of versions.

◆ Prediction

The next release will most likely be another version-set update, with any new core package the only thing worth noting. Feature news for this framework will keep arriving in the member packages, not here.

Alternatives to RBesT and tidymodels

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 RBesT or tidymodels.

See all RBesT alternatives → · See all tidymodels alternatives →

Recent activity from RBesT and tidymodels

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

  1. 5mo agoRBesTTwo-sided decisions across normal, binomial and Poisson outcomes
  2. 11mo agotidymodelsFix for packages omitted from attachment
  3. 11mo agotidymodelstailor joins the core set; base pipe replaces magrittr
  4. 1y agoRBesTJSON read and write for mixture objects
  5. 1y agotidymodelsConflict preferences added for DALEX and recipes
  6. 1y agoRBesTess() fixed inside apply functions
  7. 1y agoRBesTESS for normal mixtures in the exponential family
  8. 1y agoRBesTTruncated mixture priors for brms, plus faster Stan models
  9. 2y agoRBesTStan array syntax update and CRAN system requirements
  10. 3y agotidymodelsConflict preferences and pinned versions refreshed
  11. 4y agotidymodelsVersion refresh and testthat 3e migration
  12. 4y agotidymodelsRotating startup messages and an analysis template

Frequently asked questions

What is the difference between RBesT and tidymodels?

They serve adjacent needs but don't currently overlap on shipped themes. RBesT and tidymodels 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 RBesT better than tidymodels?

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

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

What are the best alternatives to tidymodels?

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