rnaturalearth
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Comparison · Analytics
bayestestR vs posterior
A side-by-side editorial comparison of bayestestR and posterior — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:bayesian
bayestestR vs posterior: 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 posterior?
posterior keeps deepening two things: the rvar type and Pareto-based diagnostics.
The releases in this window advance on two fronts. The rvar random-variable type gained factor and ordered subtypes (1.4.0), rvar-indexed slicing and `rvar_ifelse()` (1.5.0), base `%*%` matrix multiplication and indexed variable names (1.6.0). Separately, Pareto diagnostics have grown from `pareto_smooth()` options and individual `pareto_khat()`-family functions (1.6.0) through `pit()` for draws and rvars (1.6.1) to exported generalized-Pareto functions and `pareto_pit` (1.7.0). 1.7.1 is a paperwork release for a JOSS submission.
bayestestR vs posterior: 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.
posterior
0.0ANALYTICS
posterior keeps deepening two things: the rvar type and Pareto-based diagnostics.
◆ Current state
The releases in this window advance on two fronts. The rvar random-variable type gained factor and ordered subtypes (1.4.0), rvar-indexed slicing and `rvar_ifelse()` (1.5.0), base `%*%` matrix multiplication and indexed variable names (1.6.0). Separately, Pareto diagnostics have grown from `pareto_smooth()` options and individual `pareto_khat()`-family functions (1.6.0) through `pit()` for draws and rvars (1.6.1) to exported generalized-Pareto functions and `pareto_pit` (1.7.0). 1.7.1 is a paperwork release for a JOSS submission.
◆ Where it's heading
posterior is positioning itself as shared infrastructure rather than an end-user package: 1.7.0 explicitly exports generalized-Pareto machinery 'for use in other packages', and the JOSS paper is a citation vehicle for the same audience. The rvar work points the same way — a random-variable type other Bayesian packages can build on. Cadence is steady but unhurried, roughly one feature release a year.
◆ Prediction
More diagnostic functions are likely to be exported for downstream reuse, following the pattern 1.7.0 established with the generalized-Pareto helpers.
Alternatives to bayestestR and posterior
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 posterior.
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 bayestestR alternatives → · See all posterior alternatives →
Recent activity from bayestestR and posterior
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agoposteriorposterior 1.7.1 released for JOSS paper
- 2mo agobayestestRmcse() gains a centrality argument
- 2mo agobayestestRCmdStanFit support and tail-ESS as the default diagnostic
- 3mo agoposteriorposterior 1.7.0 exports generalized-Pareto functions
- 10mo agoposteriorposterior 1.6.1 adds pit() for draws and rvars
- 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 agoposteriorposterior 1.6.0 adds Pareto diagnostics and ESS-based thinning
- 2y agoposteriorposterior 1.5.0 adds nested-Rhat and rvar indexing
- 3y agoposteriorposterior 1.4.0 adds factor and ordered rvar subtypes
Frequently asked questions
What is the difference between bayestestR and posterior?
Both compete on the same themes — bayesian — within Analytics. bayestestR and posterior 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 posterior?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bayestestR and posterior 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 posterior?
Top posterior alternatives in Analytics are ranked by recent ship velocity. Browse the "posterior alternatives" section above for the current picks, or visit /alternatives/posterior for the full list with editorial commentary on each.