rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
bayesplot vs modelbased
A side-by-side editorial comparison of bayesplot and modelbased — release velocity, themes, recent moves, and the top alternatives to consider.
bayesplot vs modelbased: at a glance
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.
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.
bayesplot vs modelbased: editorial side-by-side
bayesplot
0.0ANALYTICS
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.
modelbased
0.0ANALYTICS
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.
Alternatives to bayesplot and modelbased
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 modelbased.
writexl
writexl spent nine years refusing to do formatting, then shipped all of it in 2.0.0.
rgbif
rgbif is steadily pushing users off paged searching and onto real downloads.
rstanarm
rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.
taxa
taxa started a ground-up rewrite in 2021 and has published almost nothing since.
rotl
rotl's whole release history is keeping name matching honest against a moving taxonomy.
See all bayesplot alternatives → · See all modelbased alternatives →
Recent activity from bayesplot and modelbased
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomodelbasedmodelbased 0.16.0 adds post-processing and omnibus contrast tests
- 3mo agomodelbasedmodelbased 0.15.0 contrasts average slopes across numeric predictors
- 5mo agomodelbasedmodelbased 0.14.0 renames trend to slope and adds collapse_by_group()
- 8mo agobayesplotmcmc_scatter gains shape; pre-ggplot2 v4 theme behavior restored
- 8mo agomodelbasedmodelbased 0.13.1 adds marginal group-level estimates and as.data.frame()
- 11mo agobayesplotQuantile dot plots and broader discrete-data support
- 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
- 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
- 1y agobayesplotppc_loo_pit_ecdf() added; KM overlays gain truncation control
- 1y agobayesplotggplot2 3.6 compatibility and a run of plot-data fixes
- 2y agobayesplotPatch caps ppc_pit_ecdf evaluation points at 1000
- 2y agobayesplotbins/breaks across histograms; all LOO plots accept psis_object
Frequently asked questions
What is the difference between bayesplot and modelbased?
They serve adjacent needs but don't currently overlap on shipped themes. bayesplot and modelbased 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 modelbased?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bayesplot and modelbased 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 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.