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modelbased vs rstanarm

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

modelbased vs rstanarm: at a glance

Featuremodelbasedrstanarm
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themeseasystats, marginal-effects, contrasts, mixed-modelsbayesian, stan, regression-models, dependency-migration
Last editorial update1h ago54m ago
WebsiteVisit →Visit →

What is modelbased?

modelbased is turning marginal effects into a full contrast grammar

modelbased computes marginal means, contrasts, and slopes from fitted models, and it ships every one to two months with a consistent shape: new comparison types, broader model support, and steady renaming toward clearer vocabulary. The recent arc runs from marginal effects inequality measures through inequality ratios to an omnibus global test and a post_process argument for multi-step comparisons. Argument names have been settled along the way, with trend becoming slope and an alias left behind.

Read the full modelbased trajectory →

What is rstanarm?

rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.

2.32.2 is entirely infrastructure and dependency work: formula machinery migrated from lme4 to reformulas, `r_eff` no longer computed for loo by default, the Stan R packages repo replaced by R-Universe, rstantools adopted to fix build and export errors, and CRAN NOTE cleanups — contributed largely by four first-time contributors. 2.32.1 and 2.26.1 follow the same pattern, tracking rstan syntax and adding `posterior::as_draws()` support. The last release with substantive modelling content is 2.21.1, which changed how default priors are determined and flipped `autoscale` to FALSE outside default priors.

Read the full rstanarm trajectory →

modelbased vs rstanarm: editorial side-by-side

M
modelbased
ANALYTICS
0.0

modelbased is turning marginal effects into a full contrast grammar

◆ Current state

modelbased computes marginal means, contrasts, and slopes from fitted models, and it ships every one to two months with a consistent shape: new comparison types, broader model support, and steady renaming toward clearer vocabulary. The recent arc runs from marginal effects inequality measures through inequality ratios to an omnibus global test and a post_process argument for multi-step comparisons. Argument names have been settled along the way, with trend becoming slope and an alias left behind.

◆ Where it's heading

The package is building a compositional vocabulary rather than a fixed menu — contrasts of average slopes, contrasts across two numeric predictors, inequality summaries across all outcome categories, and now user-supplied post-processing of comparisons. Support quietly widens underneath, covering nestedLogit, brms finite mixtures, and offsets under population and average estimation. Plotting gets attention in proportion to how often these results are presented rather than tabulated, including collapse_by_group() for showing averaged raw data under mixed-model fits.

◆ Prediction

With post_process and omnibus tests both landed, the likely next step is making these composed comparisons easier to report — formatting or plotting methods for the multi-step results rather than new comparison types.

R
rstanarm
ANALYTICS
0.0

rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.

◆ Current state

2.32.2 is entirely infrastructure and dependency work: formula machinery migrated from lme4 to reformulas, `r_eff` no longer computed for loo by default, the Stan R packages repo replaced by R-Universe, rstantools adopted to fix build and export errors, and CRAN NOTE cleanups — contributed largely by four first-time contributors. 2.32.1 and 2.26.1 follow the same pattern, tracking rstan syntax and adding `posterior::as_draws()` support. The last release with substantive modelling content is 2.21.1, which changed how default priors are determined and flipped `autoscale` to FALSE outside default priors.

◆ Where it's heading

The package has moved from feature development into ecosystem maintenance, and the contributor list shows why it survives: outside developers keep it compiling against a moving Stan, lme4 and CRAN. The `as_draws()` support and the reformulas migration both point the same way — rstanarm increasingly consumes shared infrastructure (posterior, reformulas, rstantools) instead of carrying its own.

◆ Prediction

Expect the next release to track another upstream change — rstan, reformulas or CRAN policy — rather than add model families. The pre-fit model surface looks settled.

Alternatives to modelbased and rstanarm

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 modelbased or rstanarm.

See all modelbased alternatives → · See all rstanarm alternatives →

Recent activity from modelbased and rstanarm

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

  1. 1mo agomodelbasedmodelbased 0.16.0 adds post-processing and omnibus contrast tests
  2. 3mo agomodelbasedmodelbased 0.15.0 contrasts average slopes across numeric predictors
  3. 5mo agomodelbasedmodelbased 0.14.0 renames trend to slope and adds collapse_by_group()
  4. 8mo agomodelbasedmodelbased 0.13.1 adds marginal group-level estimates and as.data.frame()
  5. 10mo agorstanarmrstanarm 2.32.2 migrates formula machinery to reformulas
  6. 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
  7. 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
  8. 2y agorstanarmrstanarm 2.32.1 fixes unit_vector error, enables LTO
  9. 2y agorstanarmrstanarm 2.26.1 adopts new rstan syntax and as_draws()
  10. 4y agorstanarmrstanarm 2.21.3 fixes loo() and adds stan_jm offsets
  11. 6y agorstanarmrstanarm 2.21.1 changes default prior behaviour

Frequently asked questions

What is the difference between modelbased and rstanarm?

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

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

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

What are the best alternatives to rstanarm?

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