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rstanarm vs see

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

rstanarm vs see: at a glance

Featurerstanarmsee
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesbayesian, stan, regression-models, dependency-migrationr, easystats, data-visualization, ggplot2
Last editorial update1h ago8h ago
WebsiteVisit →Visit →

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 →

What is see?

see grows wherever easystats adds a diagnostic, one plot method at a time.

see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.

Read the full see trajectory →

rstanarm vs see: editorial side-by-side

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.

S
see
ANALYTICS
0.0

see grows wherever easystats adds a diagnostic, one plot method at a time.

◆ Current state

see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.

◆ Where it's heading

Growth here is downstream-driven rather than self-directed: see expands to cover new diagnostics as easystats produces them. Running alongside that is a sustained investment in presentation control — theme arguments on plot methods, elements that scale with base_size — which suits users embedding these plots in documents rather than glancing at them interactively.

◆ Prediction

Expect new plot methods to keep arriving in step with performance and parameters releases, with continued theming work rather than any change in the package's scope.

Alternatives to rstanarm and see

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 rstanarm or see.

See all rstanarm alternatives → · See all see alternatives →

Recent activity from rstanarm and see

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

  1. 1mo agoseesee 0.14.1 adds plots for prior checks and grouped means
  2. 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
  3. 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
  4. 10mo agorstanarmrstanarm 2.32.2 migrates formula machinery to reformulas
  5. 11mo agoseesee 0.12.0 extends normality checks to psych factor models
  6. 1y agoseesee 0.11.0 scales theme elements with base_size
  7. 1y agoseesee 0.10.0 plots random-effect group levels for mixed models
  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 rstanarm and see?

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

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

What are the best alternatives to see?

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