easystats
The easystats meta-package is install tooling wrapped around a relicensed ecosystem.
Comparison · Analytics
bayestestR vs tune
A side-by-side editorial comparison of bayestestR and tune — release velocity, themes, recent moves, and the top alternatives to consider.
bayestestR vs tune: at a glance
What is bayestestR?
Bayesian diagnostics get stricter defaults while the Stan backend list widens
bayestestR is the diagnostics and hypothesis-testing layer of the easystats stack, and its recent releases have concentrated on two things: reporting the right uncertainty numbers by default, and accepting posterior draws from more sources. The 0.18.x line added CmdStanFit support alongside the existing rstanarm/brms paths and switched effective-sample-size reporting to tail-ESS. Bug-fix releases in between are mostly CRAN-check maintenance.
What is tune?
tune extends tuning past the model itself to postprocessors, and adds a second parallel backend
tune runs hyperparameter search for tidymodels. Version 2.0.0 rewrote tune_grid() to make postprocessing tunable alongside preprocessing and the model, changed the .config naming scheme to match, and added mirai as a parallel backend next to future. Version 2.1.0 followed with quantile regression support and a replacement Gaussian process engine.
bayestestR vs tune: editorial side-by-side
bayestestR
0.0ANALYTICS
Bayesian diagnostics get stricter defaults while the Stan backend list widens
◆ Current state
bayestestR is the diagnostics and hypothesis-testing layer of the easystats stack, and its recent releases have concentrated on two things: reporting the right uncertainty numbers by default, and accepting posterior draws from more sources. The 0.18.x line added CmdStanFit support alongside the existing rstanarm/brms paths and switched effective-sample-size reporting to tail-ESS. Bug-fix releases in between are mostly CRAN-check maintenance.
◆ Where it's heading
The package is converging on a single posture: work with raw MCMC draws from anywhere, and report the diagnostic that actually governs the interval being shown. Successive releases have swapped defaults rather than added surface area, and the efficiency work in 0.16.x aimed squarely at large brms and rstanarm fits. Output formatting is drifting toward the shared easystats display() and tinytable path.
◆ Prediction
Expect continued backend coverage on the Stan side and further alignment of print/display behavior with insight and the rest of easystats; the entries do not show a push into new inference methods.
tune
0.0ANALYTICS
tune extends tuning past the model itself to postprocessors, and adds a second parallel backend
◆ Current state
tune runs hyperparameter search for tidymodels. Version 2.0.0 rewrote tune_grid() to make postprocessing tunable alongside preprocessing and the model, changed the .config naming scheme to match, and added mirai as a parallel backend next to future. Version 2.1.0 followed with quantile regression support and a replacement Gaussian process engine.
◆ Where it's heading
Two migrations run through this timeline. The tunable surface keeps widening - first censored regression as a mode, then postprocessors via tailor - so that a candidate is now a preprocessor, model and postprocessor triple rather than just a model. The parallel story has moved from foreach to future and now to mirai, each step deprecating the last. Neither is finished.
◆ Prediction
Expect the foreach path to be removed outright, and the postprocessing surface to grow as tailor gains more steps; the GauPro switch will likely need follow-up as its behavior differs from the old engine.
Alternatives to bayestestR and tune
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 bayestestR or tune.
discrim
discrim settled into a thin engine shim after handing its model definitions to parsnip.
dbparser
dbparser shed its database and CSV writers to become just a DrugBank parser.
desirability2
desirability2 is making multi-metric model selection a first-class tidymodels step.
crosstalk
crosstalk is frozen infrastructure: four releases in five years, mostly CRAN upkeep.
cmdstanr
cmdstanr keeps adding fast approximations beside full HMC, and fighting Windows toolchains.
See all bayestestR alternatives → · See all tune alternatives →
Recent activity from bayestestR and tune
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agobayestestRmcse() gains a centrality argument
- 2mo agobayestestRCmdStanFit support and tail-ESS as the default diagnostic
- 3mo agotuneQuantile regression tuning; Bayesian search moves to GauPro
- 9mo agotuneFixes int_pctl() with future parallelism on last_fit()
- 11mo agotunePostprocessors become tunable; mirai joins future as a backend ⚡
- 11mo agotuneDevelopment snapshot re-enabling skipped tests
- 11mo agobayestestRrope() gains complement probabilities; display() methods added
- 1y agobayestestRdescribe_posterior() efficiency and multinomial handling
- 1y agobayestestReffects argument changes behavior for large brms/rstanarm fits
- 1y agobayestestRTail ESS returned from effective_sample() and its callers
- 1y agotuneWarns on foreach parallelism; space-filling grids by default
- 2y agotuneFixes parallel tuning errors under multisession plans
Frequently asked questions
What is the difference between bayestestR and tune?
They serve adjacent needs but don't currently overlap on shipped themes. bayestestR and tune 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 bayestestR better than tune?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bayestestR and tune 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 bayestestR?
Top bayestestR alternatives in Analytics are ranked by recent ship velocity. Browse the "bayestestR alternatives" section above for the current picks, or visit /alternatives/bayestestr for the full list with editorial commentary on each.
What are the best alternatives to tune?
Top tune alternatives in Analytics are ranked by recent ship velocity. Browse the "tune alternatives" section above for the current picks, or visit /alternatives/tune for the full list with editorial commentary on each.