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Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
feasts vs mlr3cluster
A side-by-side editorial comparison of feasts and mlr3cluster — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
feasts vs mlr3cluster: 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 mlr3cluster?
mlr3cluster went from a handful of clusterers to covering the field
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
feasts vs mlr3cluster: 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.
mlr3cluster
0.0ANALYTICS
mlr3cluster went from a handful of clusterers to covering the field
◆ Current state
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
◆ Where it's heading
The package is at the tail end of a coverage push, and the emphasis has shifted from adding algorithms to making the ones it has behave correctly at prediction time — cutting trees at the current k, reclustering coresets, failing informatively on unsupported metric combinations. That is the normal sequence after a rapid expansion.
◆ Prediction
Expect further predict-path corrections and parameter-set alignment across the newly added learners before any more algorithms arrive.
Alternatives to feasts and mlr3cluster
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 mlr3cluster.
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 mlr3cluster alternatives →
Recent activity from feasts and mlr3cluster
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomlr3clusterHierarchical learners now honour k at prediction time
- 2mo agomlr3clusterNine new clustering learners in one release ⚡
- 5mo agomlr3clusterCLARA, k-prototypes and spectral clustering learners added
- 6mo agofeastsfeasts moves its plots to ggtime behind a 2-year deprecation ⚡
- 6mo agomlr3clusterTyped error classes and probabilistic EM assignments
- 8mo agomlr3clusterHDBSCAN gains cluster_selection_epsilon
- 11mo agofeastsggplot2 4.0.0 compatibility and the ggtime deprecation notice
- 1y agomlr3clusterMclust learner brought in line with paradox conventions
- 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 mlr3cluster?
Both compete on the same themes — r-stats — within Analytics. feasts and mlr3cluster 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 mlr3cluster?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. feasts and mlr3cluster 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 mlr3cluster?
Top mlr3cluster alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3cluster alternatives" section above for the current picks, or visit /alternatives/mlr3cluster for the full list with editorial commentary on each.