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rstanarm vs tidypredict

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

rstanarm vs tidypredict: at a glance

Featurerstanarmtidypredict
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesbayesian, stan, regression-models, dependency-migrationtidypredict, sql-generation, gradient-boosting, in-database-scoring
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is rstanarm?

rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.

2.32.2 is entirely infrastructure and dependency work: formula machinery migrated from lme4 to reformulas, `r_eff` no longer computed for loo by default, the Stan R packages repo replaced by R-Universe, rstantools adopted to fix build and export errors, and CRAN NOTE cleanups — contributed largely by four first-time contributors. 2.32.1 and 2.26.1 follow the same pattern, tracking rstan syntax and adding `posterior::as_draws()` support. The last release with substantive modelling content is 2.21.1, which changed how default priors are determined and flipped `autoscale` to FALSE outside default priors.

Read the full rstanarm trajectory →

What is tidypredict?

tidypredict now translates the gradient-boosting libraries people actually deploy

tidypredict converts fitted R models into SQL and dplyr expressions so predictions can run inside a database instead of in R. The 1.1.0 release added rpart, CatBoost, and LightGBM, with full objective and tree-type coverage for the boosted models. That followed 1.0.0, which broke random-forest output into a single formula, added glmnet, and cut fit-translation time for xgboost, partykit, and ranger.

Read the full tidypredict trajectory →

rstanarm vs tidypredict: editorial side-by-side

R
rstanarm
ANALYTICS
0.0

rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.

◆ Current state

2.32.2 is entirely infrastructure and dependency work: formula machinery migrated from lme4 to reformulas, `r_eff` no longer computed for loo by default, the Stan R packages repo replaced by R-Universe, rstantools adopted to fix build and export errors, and CRAN NOTE cleanups — contributed largely by four first-time contributors. 2.32.1 and 2.26.1 follow the same pattern, tracking rstan syntax and adding `posterior::as_draws()` support. The last release with substantive modelling content is 2.21.1, which changed how default priors are determined and flipped `autoscale` to FALSE outside default priors.

◆ Where it's heading

The package has moved from feature development into ecosystem maintenance, and the contributor list shows why it survives: outside developers keep it compiling against a moving Stan, lme4 and CRAN. The `as_draws()` support and the reformulas migration both point the same way — rstanarm increasingly consumes shared infrastructure (posterior, reformulas, rstantools) instead of carrying its own.

◆ Prediction

Expect the next release to track another upstream change — rstan, reformulas or CRAN policy — rather than add model families. The pre-fit model surface looks settled.

T
tidypredict
ANALYTICS
0.0

tidypredict now translates the gradient-boosting libraries people actually deploy

◆ Current state

tidypredict converts fitted R models into SQL and dplyr expressions so predictions can run inside a database instead of in R. The 1.1.0 release added rpart, CatBoost, and LightGBM, with full objective and tree-type coverage for the boosted models. That followed 1.0.0, which broke random-forest output into a single formula, added glmnet, and cut fit-translation time for xgboost, partykit, and ranger.

◆ Where it's heading

The package's value scales directly with how many model types it can translate, and the recent work has concentrated on the tree ensembles that dominate tabular modelling in practice. Coverage now extends past what parsnip wraps, since raw CatBoost models are supported alongside parsnip and bonsai ones with an explicit escape hatch for categorical features. Performance work on the translation step suggests the models being converted have grown large enough for that to matter.

◆ Prediction

With the major boosting libraries covered, the remaining gap is what happens to preprocessing, so tighter integration with recipes or orbital for translating whole workflows is the natural next step.

Alternatives to rstanarm and tidypredict

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 rstanarm or tidypredict.

See all rstanarm alternatives → · See all tidypredict alternatives →

Recent activity from rstanarm and tidypredict

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

  1. 5mo agotidypredicttidypredict 1.1.0 adds CatBoost, LightGBM, and rpart support
  2. 8mo agotidypredicttidypredict 1.0.1 fixes base_score extraction for xgboost 3
  3. 8mo agotidypredicttidypredict 1.0.0 unifies random forest output and adds glmnet
  4. 10mo agorstanarmrstanarm 2.32.2 migrates formula machinery to reformulas
  5. 1y agotidypredicttidypredict 0.5.1 exports internals for the orbital package
  6. 2y agorstanarmrstanarm 2.32.1 fixes unit_vector error, enables LTO
  7. 2y agorstanarmrstanarm 2.26.1 adopts new rstan syntax and as_draws()
  8. 3y agotidypredicttidypredict 0.5 hands maintenance to a new maintainer
  9. 4y agotidypredicttidypredict 0.4.9 relicenses to MIT and fixes SQL generation
  10. 4y agorstanarmrstanarm 2.21.3 fixes loo() and adds stan_jm offsets
  11. 6y agorstanarmrstanarm 2.21.1 changes default prior behaviour

Frequently asked questions

What is the difference between rstanarm and tidypredict?

They serve adjacent needs but don't currently overlap on shipped themes. rstanarm and tidypredict 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 rstanarm better than tidypredict?

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

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

What are the best alternatives to tidypredict?

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