tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
feasts vs mlr3learners
A side-by-side editorial comparison of feasts and mlr3learners — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
feasts vs mlr3learners: at a glance
What is feasts?
feasts is splitting itself in two, moving every plot into ggtime
feasts provides feature extraction and statistics for tsibble time series. The last two releases are dominated by one decision: all of its gg_*() plotting functions are being moved out into a separate ggtime package. 0.4.2 announced the deprecation and 0.5.0 makes ggtime a dependency with soft-deprecation messages on every re-export.
What is mlr3learners?
mlr3learners spends its releases absorbing upstream churn
mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.
feasts vs mlr3learners: editorial side-by-side
feasts
0.0ANALYTICS
feasts is splitting itself in two, moving every plot into ggtime
◆ Current state
feasts provides feature extraction and statistics for tsibble time series. The last two releases are dominated by one decision: all of its gg_*() plotting functions are being moved out into a separate ggtime package. 0.4.2 announced the deprecation and 0.5.0 makes ggtime a dependency with soft-deprecation messages on every re-export.
◆ Where it's heading
The package is narrowing to its stated purpose — features and statistics — and shedding graphics entirely over a deliberately slow two-year window. Everything else in the recent history is ggplot2 compatibility work and narrow seasonal-plot bug fixes, which is consistent with a maintainer trimming surface area rather than growing it.
◆ Prediction
The next releases should be compatibility upkeep while the ggtime deprecation runs its course; the re-exports stay until the announced window closes.
mlr3learners
0.0ANALYTICS
mlr3learners spends its releases absorbing upstream churn
◆ Current state
mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.
◆ Where it's heading
The package's job is insulation, and the changelog shows what that costs — compatibility-only releases interleaved with small capability additions that expose more of each upstream model. The direction of travel is toward giving users access to the raw upstream objects rather than hiding them.
◆ Prediction
Expect the next release to track another upstream version bump, with incremental exposure of learner-specific fields continuing alongside.
Alternatives to feasts and mlr3learners
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 feasts or mlr3learners.
textrecipes
Text features finally stay sparse all the way to the model.
probably
The package that made calibration a step instead of an afterthought.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
See all feasts alternatives → · See all mlr3learners alternatives →
Recent activity from feasts and mlr3learners
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agomlr3learnerspredict_raw across all learners, plus probit and ranger internals
- 6mo agofeastsfeasts moves its plots to ggtime behind a 2-year deprecation ⚡
- 8mo agomlr3learnersxgboost 3.1.2.1 compatibility
- 9mo agomlr3learnersUncertainty estimation methods for ranger regression
- 10mo agomlr3learnersDevelopment snapshot: LDA test adjustment
- 11mo agofeastsggplot2 4.0.0 compatibility and the ggtime deprecation notice
- 1y agomlr3learnerskknn learners restored after returning to CRAN
- 1y agomlr3learnerskknn learners removed after CRAN archival
- 1y agofeastsgg_season() fix for sub-weekly daily data
- 1y agofeastsImpulse-response plots and Johansen cointegration tests
- 2y agofeastsPatch for ggplot2 3.5.0 breaking changes
- 3y agofeastsCRAN patch for S3 method consistency
Frequently asked questions
What is the difference between feasts and mlr3learners?
Both compete on the same themes — r-stats — within Analytics. feasts and mlr3learners 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 feasts better than mlr3learners?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. feasts and mlr3learners 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 feasts?
Top feasts alternatives in Analytics are ranked by recent ship velocity. Browse the "feasts alternatives" section above for the current picks, or visit /alternatives/feasts for the full list with editorial commentary on each.
What are the best alternatives to mlr3learners?
Top mlr3learners alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3learners alternatives" section above for the current picks, or visit /alternatives/mlr3learners for the full list with editorial commentary on each.