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Comparison · Analytics
bayestools vs ggstats
A side-by-side editorial comparison of bayestools and ggstats — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
bayestools vs ggstats: at a glance
What is bayestools?
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
BayesTools provides the shared JAGS fitting, prior and summary infrastructure that the author's meta-analysis packages build on. The 0.2.x line filled in modeling primitives — prior_mixture() and mixed-posterior objects in 0.2.18, expression-valued priors and lme4-style uncorrelated random effects in 0.2.20, then a run of small diagnostic fixes for mixture and spike-and-slab priors. Version 0.3.0 in May 2026 adds automatic standardization of continuous predictors, default priors for unspecified factor and continuous terms, and functions to transform prior and posterior samples back to the original scale.
What is ggstats?
ggstats keeps widening what a coefficient or Likert plot can be
ggstats extends ggplot2 with statistical plotting: model coefficient plots, Likert and diverging bar charts, proportion geometries and the helpers that make them behave. Recent releases have added an experimental gglikert_side(), left and right total columns for gglikert(), and survey-object support across the Likert family. Development is steady and CRAN-paced, with releases every two to three months.
bayestools vs ggstats: editorial side-by-side
bayestools
0.0ANALYTICS
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
◆ Current state
BayesTools provides the shared JAGS fitting, prior and summary infrastructure that the author's meta-analysis packages build on. The 0.2.x line filled in modeling primitives — prior_mixture() and mixed-posterior objects in 0.2.18, expression-valued priors and lme4-style uncorrelated random effects in 0.2.20, then a run of small diagnostic fixes for mixture and spike-and-slab priors. Version 0.3.0 in May 2026 adds automatic standardization of continuous predictors, default priors for unspecified factor and continuous terms, and functions to transform prior and posterior samples back to the original scale.
◆ Where it's heading
This package's releases are best read against what depends on them. The 0.2.x fixes track features appearing in RoBMA one version later, and 0.3.0 landed a single day before RoBMA 4.0.0 — the standardization and sample-transformation functions are the substrate that rewrite needed. The direction of the work is toward sensible defaults: default priors by predictor type, automatic standardization for sampling stability, and transformation back to interpretable scale so the convenience does not cost the user their units.
◆ Prediction
Given how tightly its releases track downstream needs, the next version is most likely driven by gaps surfacing in RoBMA 4.0.x rather than by independent feature work.
ggstats
0.0ANALYTICS
ggstats keeps widening what a coefficient or Likert plot can be
◆ Current state
ggstats extends ggplot2 with statistical plotting: model coefficient plots, Likert and diverging bar charts, proportion geometries and the helpers that make them behave. Recent releases have added an experimental gglikert_side(), left and right total columns for gglikert(), and survey-object support across the Likert family. Development is steady and CRAN-paced, with releases every two to three months.
◆ Where it's heading
Two long-running threads. The coefficient side has been consolidating — ggcoef_multinom() and ggcoef_multicomponents() soft-deprecated in favour of a unified ggcoef_model() with group_by, plus new ggcoef_dodged() and ggcoef_faceted() variants. The Likert side keeps expanding outward instead, absorbing survey objects, total columns and side-by-side layouts. Underneath both is a steady tax of ggplot2 and vctrs compatibility work, including tracking the geom_errorbarh() deprecation in ggplot2 4.0.0.
◆ Prediction
Expect gglikert_side() to lose its experimental status once its interface settles, and the deprecated multinomial entry points to be removed in a future release now that ggcoef_model() covers their cases.
Alternatives to bayestools and ggstats
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 bayestools or ggstats.
gcube
gcube's recent releases are all packaging metadata, not simulation code
cubist
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ecodive
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
simStateSpace
State-space data simulation for R, filled in one function at a time
rollama
rollama turns a local-LLM wrapper into an instrument for reproducible annotation
See all bayestools alternatives → · See all ggstats alternatives →
Recent activity from bayestools and ggstats
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agobayestoolsAdds automatic predictor standardization and type-based default priors
- 5mo agoggstatsgglikert_side() and total columns for Likert plots
- 7mo agoggstatsLikert functions accept survey objects
- 8mo agobayestoolsBayesTools 0.2.23
- 8mo agobayestoolsBayesTools 0.2.22
- 11mo agoggstatsTable output for ggcoef_compare(); x-axis limits harmonised
- 11mo agobayestoolsBayesTools 0.2.21
- 1y agobayestoolsBayesTools 0.2.20
- 1y agoggstatsggstats 0.10.0
- 1y agobayestoolsBayesTools 0.2.19
- 1y agoggstatsCoefficient plots unified around ggcoef_model() with grouping
- 1y agoggstatsDiverging and Likert geoms redesigned; connector geoms added
Frequently asked questions
What is the difference between bayestools and ggstats?
Both compete on the same themes — r-package — within Analytics. bayestools and ggstats 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 bayestools better than ggstats?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bayestools and ggstats 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 bayestools?
Top bayestools alternatives in Analytics are ranked by recent ship velocity. Browse the "bayestools alternatives" section above for the current picks, or visit /alternatives/bayestools for the full list with editorial commentary on each.
What are the best alternatives to ggstats?
Top ggstats alternatives in Analytics are ranked by recent ship velocity. Browse the "ggstats alternatives" section above for the current picks, or visit /alternatives/ggstats for the full list with editorial commentary on each.