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modelbased vs tidyposterior

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

Shared themes:r-package

modelbased vs tidyposterior: at a glance

Featuremodelbasedtidyposterior
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themeseasystats, marginal-effects, contrasts, mixed-modelstidymodels, bayesian-analysis, model-comparison, maintenance-mode
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

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.

Read the full modelbased trajectory →

What is tidyposterior?

A finished Bayesian model-comparison package in pure maintenance mode

tidyposterior compares model performance using Bayesian resampling analysis, and it reached its intended shape years ago. Every release since 1.0.0 has been maintenance: a broken test under R-devel, a maintainer email change, and most recently compatibility with an upcoming ggplot2 release plus the base-pipe transition. The substantive API decisions — autoplot() over ggplot() methods, tibble returns from contrast_models() — were settled in the 0.x series.

Read the full tidyposterior trajectory →

modelbased vs tidyposterior: editorial side-by-side

M
modelbased
ANALYTICS
0.0

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.

T
tidyposterior
ANALYTICS
0.0

A finished Bayesian model-comparison package in pure maintenance mode

◆ Current state

tidyposterior compares model performance using Bayesian resampling analysis, and it reached its intended shape years ago. Every release since 1.0.0 has been maintenance: a broken test under R-devel, a maintainer email change, and most recently compatibility with an upcoming ggplot2 release plus the base-pipe transition. The substantive API decisions — autoplot() over ggplot() methods, tibble returns from contrast_models() — were settled in the 0.x series.

◆ Where it's heading

The package tracks its dependencies rather than developing on its own line, and the dependencies do the moving: rstanarm API changes, dplyr 1.0.0, testthat 3e, ggplot2. Its integration surface widened once, when perf_mod() gained methods for tuning parameter objects from tune, finetune, and workflowsets, and has been stable since. This is what a completed package in an active ecosystem looks like.

◆ Prediction

Expect the next release to be triggered by an upstream change rather than by anything tidyposterior wants to do differently.

Alternatives to modelbased and tidyposterior

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 modelbased or tidyposterior.

See all modelbased alternatives → · See all tidyposterior alternatives →

Recent activity from modelbased and tidyposterior

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

  1. 1mo agomodelbasedmodelbased 0.16.0 adds post-processing and omnibus contrast tests
  2. 3mo agomodelbasedmodelbased 0.15.0 contrasts average slopes across numeric predictors
  3. 5mo agomodelbasedmodelbased 0.14.0 renames trend to slope and adds collapse_by_group()
  4. 8mo agomodelbasedmodelbased 0.13.1 adds marginal group-level estimates and as.data.frame()
  5. 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
  6. 1y agotidyposteriortidyposterior 1.0.1.9000 prepares for an upcoming ggplot2 release
  7. 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
  8. 2y agotidyposteriortidyposterior 1.0.1 fixes a test broken under R-devel
  9. 4y agotidyposteriortidyposterior 1.0.0 modernizes internals to pivot_longer and testthat 3e
  10. 5y agotidyposteriortidyposterior 0.1.0 adds perf_mod() methods for tune and workflowsets
  11. 6y agotidyposteriortidyposterior 0.0.3 returns tibbles and adds a formula override
  12. 7y agotidyposteriortidyposterior 0.0.2 removes example RData files for CRAN

Frequently asked questions

What is the difference between modelbased and tidyposterior?

Both compete on the same themes — r-package — within Analytics. modelbased and tidyposterior 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 modelbased better than tidyposterior?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. modelbased and tidyposterior 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 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.

What are the best alternatives to tidyposterior?

Top tidyposterior alternatives in Analytics are ranked by recent ship velocity. Browse the "tidyposterior alternatives" section above for the current picks, or visit /alternatives/tidyposterior for the full list with editorial commentary on each.