← Back to home
Comparison · Analytics

fabletools vs RBesT

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

fabletools vs RBesT: at a glance

FeaturefabletoolsRBesT
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesforecasting, tidyverts, model-combination, reconciliationbayesian-statistics, clinical-trials, stan, r-language
Last editorial update1h ago39m ago
WebsiteVisit →Visit →

What is fabletools?

The tidyverts forecasting core rebuilt model combination on full residual covariance.

fabletools is the framework layer under fable and fpp3 — mables, fables, accuracy measures, reconciliation, and the model arithmetic that lets forecasters express ensembles as expressions. Version 0.8.0 reworked that arithmetic: combination now uses a joint N-way convolution accounting for the full residual covariance across components rather than composing pairwise, and every arithmetic operator collapses to a single model_combination with correctly implied weights, so nested expressions like ((m1 + m2)/2 + m3)/2 flatten automatically. In parallel, the package has been shedding graphics to {ggtime} on a deliberately slow deprecation clock.

Read the full fabletools trajectory →

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 →

fabletools vs RBesT: editorial side-by-side

F
fabletools
ANALYTICS
0.0

The tidyverts forecasting core rebuilt model combination on full residual covariance.

◆ Current state

fabletools is the framework layer under fable and fpp3 — mables, fables, accuracy measures, reconciliation, and the model arithmetic that lets forecasters express ensembles as expressions. Version 0.8.0 reworked that arithmetic: combination now uses a joint N-way convolution accounting for the full residual covariance across components rather than composing pairwise, and every arithmetic operator collapses to a single model_combination with correctly implied weights, so nested expressions like ((m1 + m2)/2 + m3)/2 flatten automatically. In parallel, the package has been shedding graphics to {ggtime} on a deliberately slow deprecation clock.

◆ Where it's heading

The framework is being narrowed and deepened at the same time. Narrowed, because plotting is moving out to a dedicated package over an announced two-year deprecation, leaving fabletools to modeling infrastructure. Deepened, because the recent statistical work targets correctness in places users could not easily inspect — combination weights, inverse-variance weighting computed on response rather than innovation residuals, reconciliation coherency matrices exposed via coherent_smat() and coherent_cmat(). Class hygiene follows the same instinct, with mdl_lst replacing lst_mdl and gaining augment(), glance(), and tidy() so global and reconciliation models report statistics like any other.

◆ Prediction

With combination and reconciliation infrastructure freshly reworked, the remaining announced work is the ggtime separation, so expect the graphics re-exports to keep degrading toward removal while modeling changes stay incremental.

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.

Alternatives to fabletools and RBesT

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 fabletools or RBesT.

See all fabletools alternatives → · See all RBesT alternatives →

Recent activity from fabletools and RBesT

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

  1. 1mo agofabletoolsModel combination rebuilt on joint N-way convolution
  2. 3mo agofabletoolsCoherency matrices exposed, mdl_lst gains tidier methods
  3. 5mo agoRBesTTwo-sided decisions across normal, binomial and Poisson outcomes
  4. 5mo agofabletoolsGraphics methods now require fabletools to be attached
  5. 6mo agofabletoolsTime series graphics migrating out to ggtime
  6. 8mo agofabletoolsggplot2 4.0.0 compatibility patch
  7. 8mo agofabletoolsIRF() generic and multivariate bootstrap sample paths
  8. 1y agoRBesTJSON read and write for mixture objects
  9. 1y agoRBesTess() fixed inside apply functions
  10. 1y agoRBesTESS for normal mixtures in the exponential family
  11. 1y agoRBesTTruncated mixture priors for brms, plus faster Stan models
  12. 2y agoRBesTStan array syntax update and CRAN system requirements

Frequently asked questions

What is the difference between fabletools and RBesT?

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

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

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

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.