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bayesplot vs parameters

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

bayesplot vs parameters: at a glance

Featurebayesplotparameters
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesbayesian workflow, stan, posterior predictive checks, ggplot2 compatibilityeasystats, model-parameters, standardization, mixed-models
Last editorial update4h ago1h ago
WebsiteVisit →Visit →

What is bayesplot?

bayesplot keeps widening its posterior-check catalogue while absorbing each ggplot2 break.

bayesplot supplies the plotting layer for Stan-adjacent Bayesian workflows: posterior predictive checks, MCMC diagnostics and LOO diagnostics. Releases through 2025 alternate between new plot families and keeping pace with ggplot2, which changed behavior twice in the visible window. Contributions increasingly arrive from outside the core Stan team.

Read the full bayesplot trajectory →

What is parameters?

easystats' parameters package absorbs one more model class every few weeks

parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.

Read the full parameters trajectory →

bayesplot vs parameters: editorial side-by-side

B
bayesplot
ANALYTICS
0.0

bayesplot keeps widening its posterior-check catalogue while absorbing each ggplot2 break.

◆ Current state

bayesplot supplies the plotting layer for Stan-adjacent Bayesian workflows: posterior predictive checks, MCMC diagnostics and LOO diagnostics. Releases through 2025 alternate between new plot families and keeping pace with ggplot2, which changed behavior twice in the visible window. Contributions increasingly arrive from outside the core Stan team.

◆ Where it's heading

Two forces drive the release line: expanding what can be checked visually, and absorbing upstream ggplot2 churn. The 1.13-1.14 pair shows the first, adding LOO-PIT ECDF plots, quantile dot plots and discrete-data handling across the stat family, while 1.12 and 1.15 are largely spent on ggplot2 3.6 and 4.0 compatibility. The recurring new-contributor lists suggest maintenance load is being spread rather than concentrated.

◆ Prediction

Discrete-data support has rolled out plot family by plot family across three releases; the next release most likely continues that sweep and finishes the ggplot2 v4 adaptation.

P
parameters
ANALYTICS
0.0

easystats' parameters package absorbs one more model class every few weeks

◆ Current state

parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.

◆ Where it's heading

The package's job is to be the universal adapter for model output, so its roadmap is effectively set by what the R modelling ecosystem produces. Two threads are visible beyond coverage: getting standard errors right for awkward cases such as frailty terms and robust vcov matrices, and getting labels right when factors are converted on the fly or character variables appear in a formula. Interoperability inside easystats keeps tightening, with equivalence_test() gaining methods for modelbased objects.

◆ Prediction

Given the cadence, the next release will most likely add another model class alongside label and standard-error fixes rather than change how the package works.

Alternatives to bayesplot and parameters

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 bayesplot or parameters.

See all bayesplot alternatives → · See all parameters alternatives →

Recent activity from bayesplot and parameters

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

  1. 1mo agoparametersparameters 0.29.2 extends lavaan support and fixes label dropping
  2. 2mo agoparametersparameters 0.29.1 adds a cluster argument and fixes vcov handling
  3. 3mo agoparametersparameters 0.29.0 stops standardizing the intercept in post-hoc methods
  4. 8mo agobayesplotmcmc_scatter gains shape; pre-ggplot2 v4 theme behavior restored
  5. 8mo agoparametersparameters 0.28.3 adds Kenward-Roger and Satterthwaite for glmmTMB
  6. 11mo agoparametersparameters 0.28.2 updates tests for the latest fixest release
  7. 11mo agobayesplotQuantile dot plots and broader discrete-data support
  8. 11mo agoparametersparameters 0.28.1 adds robust standard errors for glmmTMB
  9. 1y agobayesplotppc_loo_pit_ecdf() added; KM overlays gain truncation control
  10. 1y agobayesplotggplot2 3.6 compatibility and a run of plot-data fixes
  11. 2y agobayesplotPatch caps ppc_pit_ecdf evaluation points at 1000
  12. 2y agobayesplotbins/breaks across histograms; all LOO plots accept psis_object

Frequently asked questions

What is the difference between bayesplot and parameters?

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

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

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

What are the best alternatives to parameters?

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