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ggeffects vs rstanarm

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

ggeffects vs rstanarm: at a glance

Featureggeffectsrstanarm
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesmarginal-effects, r-stats, statistics, breaking-changesbayesian, stan, regression-models, dependency-migration
Last editorial update4h ago1h ago
WebsiteVisit →Visit →

What is ggeffects?

ggeffects hands its contrast engine to modelbased and keeps the interface

ggeffects computes and plots marginal effects for a long tail of R model classes. Its recent line has two threads: steadily broadening model support and argument surface, and repeatedly absorbing breaking changes from the packages it computes on top of. In 2.2.0 it stopped absorbing them and delegated test_predictions() and johnson_neyman() to modelbased instead.

Read the full ggeffects 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 →

ggeffects vs rstanarm: editorial side-by-side

G
ggeffects
ANALYTICS
0.0

ggeffects hands its contrast engine to modelbased and keeps the interface

◆ Current state

ggeffects computes and plots marginal effects for a long tail of R model classes. Its recent line has two threads: steadily broadening model support and argument surface, and repeatedly absorbing breaking changes from the packages it computes on top of. In 2.2.0 it stopped absorbing them and delegated test_predictions() and johnson_neyman() to modelbased instead.

◆ Where it's heading

The package is settling into a front-end role — a consistent predict_response() interface over other people's estimation engines — rather than owning the computation itself. The 2.x releases also show a pattern of removing deprecated arguments and clarifying mixed-model semantics, so the interface is being tightened as the backend is outsourced.

◆ Prediction

Expect the features lost in the modelbased handover to return as that package's contrast and slope estimation matures, rather than being reimplemented locally.

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 ggeffects 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 ggeffects or rstanarm.

See all ggeffects alternatives → · See all rstanarm alternatives →

Recent activity from ggeffects and rstanarm

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

  1. 10mo agorstanarmrstanarm 2.32.2 migrates formula machinery to reformulas
  2. 1y agoggeffectsggeffects delegates contrasts and slopes to modelbased
  3. 1y agoggeffectsFive focal terms and formula-based contrast tests
  4. 1y agoggeffectsMixed-model predictions split type from interval
  5. 1y agoggeffectsBias correction for back-transformed mixed-model predictions
  6. 1y agoggeffectsSupport for WeightIt model classes
  7. 2y agoggeffectsglmgee support and vcov controls for ggemmeans()
  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 ggeffects and rstanarm?

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

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

Top ggeffects alternatives in Analytics are ranked by recent ship velocity. Browse the "ggeffects alternatives" section above for the current picks, or visit /alternatives/ggeffects 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.