tidytext
Finished, widely taught, and shipping roxygen fixes.
A side-by-side editorial comparison of forecast and workflows — release velocity, themes, recent moves, and the top alternatives to consider.
After years of pure maintenance, forecast 9.0.0 reopens the package
forecast is the long-established R forecasting package that fable was meant to succeed. For several years its releases were RNG fixes, base-R compatibility and documentation. Then 9.0.0 arrived with a batch of new model constructors, wider prediction-interval support and a rewritten accuracy() built on S3 methods.
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.
forecast is the long-established R forecasting package that fable was meant to succeed. For several years its releases were RNG fixes, base-R compatibility and documentation. Then 9.0.0 arrived with a batch of new model constructors, wider prediction-interval support and a rewritten accuracy() built on S3 methods.
The major version reframes forecast around explicit *_model() constructors — mean, random walk, spline, theta, Croston — rather than the older function-per-method style, and the 9.0.x patches since have been performance and argument-handling cleanups on top. That is an active maintenance line, not a package winding down in favour of fable.
Expect continued 9.0.x patches consolidating the new constructors and their forecast methods, with the older interfaces kept working alongside them.
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 forecast 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 forecast alternatives → · See all workflows alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. forecast 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. forecast 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 forecast alternatives in Analytics are ranked by recent ship velocity. Browse the "forecast alternatives" section above for the current picks, or visit /alternatives/forecast-r 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.