tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
bayesplot vs probably
A side-by-side editorial comparison of bayesplot and probably — release velocity, themes, recent moves, and the top alternatives to consider.
bayesplot vs probably: 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 probably?
The package that made calibration a step instead of an afterthought.
probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.
bayesplot vs probably: 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.
probably
0.0ANALYTICS
The package that made calibration a step instead of an afterthought.
◆ Current state
probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.
◆ Where it's heading
The recent releases are about making these objects survive leaving the session. butcher and required_pkgs() methods are what a model needs to be pinned, containerised and served, and their arrival alongside workflows adding a tailor postprocessing stage and vetiver adding probably support points the same way: calibration is being moved out of analysis scripts and into the deployed pipeline. The cal_*_none() reference implementations are the tell that calibration is now something people tune rather than apply once.
◆ Prediction
Expect the calibration functions to be reachable directly from a tuned workflow's postprocessing stage rather than applied to predictions afterwards, following the tailor integration that workflows just shipped.
Alternatives to bayesplot and probably
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 probably.
textrecipes
Text features finally stay sparse all the way to the model.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
patchwork
patchwork stopped being a ggplot composer and became a page composer.
See all bayesplot alternatives → · See all probably alternatives →
Recent activity from bayesplot and probably
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 8mo agobayesplotmcmc_scatter gains shape; pre-ggplot2 v4 theme behavior restored
- 10mo agoprobablyConformal objects gain required_pkgs() and butcher methods
- 11mo agobayesplotQuantile dot plots and broader discrete-data support
- 1y agoprobablyggplot2 test updates and a clearer validation-set error
- 1y agobayesplotppc_loo_pit_ecdf() added; KM overlays gain truncation control
- 1y agoprobablyCalibration internals refactored; isotonic bootstrap bug fixed
- 1y agobayesplotggplot2 3.6 compatibility and a run of plot-data fixes
- 2y agoprobablyFix grouping sensitivity to variable type
- 2y agobayesplotPatch caps ppc_pit_ecdf evaluation points at 1000
- 2y agobayesplotbins/breaks across histograms; all LOO plots accept psis_object
- 3y agoprobablySplit conformal and conformal quantile regression added
- 3y agoprobablyCalibration and conformal inference arrive in tidymodels ⚡
Frequently asked questions
What is the difference between bayesplot and probably?
They serve adjacent needs but don't currently overlap on shipped themes. bayesplot and probably 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 probably?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bayesplot and probably 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 probably?
Top probably alternatives in Analytics are ranked by recent ship velocity. Browse the "probably alternatives" section above for the current picks, or visit /alternatives/probably for the full list with editorial commentary on each.