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parameters vs posterior

A side-by-side editorial comparison of parameters and posterior — release velocity, themes, recent moves, and the top alternatives to consider.

parameters vs posterior: at a glance

Featureparametersposterior
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themeseasystats, model-parameters, standardization, mixed-modelsbayesian, rvar, pareto-diagnostics, r-infrastructure
Last editorial update1h ago54m ago
WebsiteVisit →Visit →

What is parameters?

easystats' parameters package absorbs one more model class every few weeks

parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.

Read the full parameters trajectory →

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.

Read the full posterior trajectory →

parameters vs posterior: editorial side-by-side

P
parameters
ANALYTICS
0.0

easystats' parameters package absorbs one more model class every few weeks

◆ Current state

parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.

◆ Where it's heading

The package's job is to be the universal adapter for model output, so its roadmap is effectively set by what the R modelling ecosystem produces. Two threads are visible beyond coverage: getting standard errors right for awkward cases such as frailty terms and robust vcov matrices, and getting labels right when factors are converted on the fly or character variables appear in a formula. Interoperability inside easystats keeps tightening, with equivalence_test() gaining methods for modelbased objects.

◆ Prediction

Given the cadence, the next release will most likely add another model class alongside label and standard-error fixes rather than change how the package works.

P
posterior
ANALYTICS
0.0

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 parameters 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 parameters or posterior.

See all parameters alternatives → · See all posterior alternatives →

Recent activity from parameters and posterior

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agoparametersparameters 0.29.2 extends lavaan support and fixes label dropping
  2. 2mo agoposteriorposterior 1.7.1 released for JOSS paper
  3. 2mo agoparametersparameters 0.29.1 adds a cluster argument and fixes vcov handling
  4. 3mo agoparametersparameters 0.29.0 stops standardizing the intercept in post-hoc methods
  5. 3mo agoposteriorposterior 1.7.0 exports generalized-Pareto functions
  6. 8mo agoparametersparameters 0.28.3 adds Kenward-Roger and Satterthwaite for glmmTMB
  7. 10mo agoposteriorposterior 1.6.1 adds pit() for draws and rvars
  8. 11mo agoparametersparameters 0.28.2 updates tests for the latest fixest release
  9. 11mo agoparametersparameters 0.28.1 adds robust standard errors for glmmTMB
  10. 1y agoposteriorposterior 1.6.0 adds Pareto diagnostics and ESS-based thinning
  11. 2y agoposteriorposterior 1.5.0 adds nested-Rhat and rvar indexing
  12. 3y agoposteriorposterior 1.4.0 adds factor and ordered rvar subtypes

Frequently asked questions

What is the difference between parameters and posterior?

They serve adjacent needs but don't currently overlap on shipped themes. parameters 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 parameters better than posterior?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. parameters 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 parameters?

Top parameters alternatives in Analytics are ranked by recent ship velocity. Browse the "parameters alternatives" section above for the current picks, or visit /alternatives/parameters-r 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.