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censored vs tune

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

Shared themes:tidymodels

censored vs tune: at a glance

Featurecensoredtune
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themessurvival-analysis, tidymodels, parsnip, engineshyperparameter-tuning, tidymodels, parallelism, postprocessing
Last editorial update2h ago1h ago
WebsiteVisit →Visit →

What is censored?

censored keeps survival models aligned with parsnip's shifting prediction contracts

censored supplies survival-analysis engines to parsnip, and its release history is dominated by staying in step with the rest of tidymodels rather than shipping independent features. The 0.2.0 and 0.3.0 releases added engines and prediction types; everything since has been contract alignment — quantile prediction format, hardhat test adaptation, flexsurv preparation.

Read the full censored trajectory →

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.

Read the full tune trajectory →

censored vs tune: editorial side-by-side

C
censored
ANALYTICS
0.0

censored keeps survival models aligned with parsnip's shifting prediction contracts

◆ Current state

censored supplies survival-analysis engines to parsnip, and its release history is dominated by staying in step with the rest of tidymodels rather than shipping independent features. The 0.2.0 and 0.3.0 releases added engines and prediction types; everything since has been contract alignment — quantile prediction format, hardhat test adaptation, flexsurv preparation.

◆ Where it's heading

The package is settling into a downstream role where parsnip and hardhat set the interface and censored implements it for censored regression. Breaking changes arrive from upstream, not from new ideas here. The substantive engine work — aorsf, flexsurvspline, glmnet multi_predict — is behind it, and recent cycles are thin.

◆ Prediction

Expect the next releases to track further parsnip prediction-type changes rather than add engines; the entries do not show new survival methods in progress.

T
tune
ANALYTICS
0.0

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 censored 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 censored or tune.

See all censored alternatives → · See all tune alternatives →

Recent activity from censored and tune

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

  1. 3mo agotuneQuantile regression tuning; Bayesian search moves to GauPro
  2. 4mo agocensoredcensored 0.3.4
  3. 9mo agotuneFixes int_pctl() with future parallelism on last_fit()
  4. 11mo agotunePostprocessors become tunable; mirai joins future as a backend
  5. 11mo agotuneDevelopment snapshot re-enabling skipped tests
  6. 1y agotuneWarns on foreach parallelism; space-filling grids by default
  7. 1y agocensoredQuantile prediction format follows new parsnip requirements
  8. 2y agocensoredSurvival probabilities at infinite evaluation times now computed
  9. 2y agotuneFixes parallel tuning errors under multisession plans
  10. 2y agocensoredcensored 0.3.1
  11. 2y agocensoredmulti_predict() for all glmnet prediction types; aorsf predicts time
  12. 3y agocensoredeval_time replaces time; matrix fitting for censored regression

Frequently asked questions

What is the difference between censored and tune?

Both compete on the same themes — tidymodels — within Analytics. censored 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 censored better than tune?

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

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