tidytext
Finished, widely taught, and shipping roxygen fixes.
A side-by-side editorial comparison of censored and workflows — release velocity, themes, recent moves, and the top alternatives to consider.
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.
The tidymodels pipeline grew a third stage, and it happens after the model runs.
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
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.
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.
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.
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
The object is filling out into a complete pipeline description rather than a preprocessing-plus-model pair. Postprocessing is the structural addition — calibration and threshold selection were previously done by hand after prediction, outside anything tidymodels could tune or record — and the fact that it arrived integrated with tunable() and tune_args() rather than as a standalone step is the point. The rest of the arc is the steady tidymodels habit of converting silent guesses into errors.
Expect tailor postprocessors to spread through tune and workflowsets next, since the parameter and tuning generics were wired up first, and expect sparse support to extend to more engines after lightgbm.
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 workflows.
Finished, widely taught, and shipping roxygen fixes.
Text features finally stay sparse all the way to the model.
The package that made calibration a step instead of an afterthought.
workflowsets keeps widening what counts as a model worth comparing.
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
patchwork stopped being a ggplot composer and became a page composer.
See all censored alternatives → · See all workflows alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — tidymodels — within Analytics. censored and workflows 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. censored and workflows 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.
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.
Top workflows alternatives in Analytics are ranked by recent ship velocity. Browse the "workflows alternatives" section above for the current picks, or visit /alternatives/workflows-r for the full list with editorial commentary on each.