mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
A side-by-side editorial comparison of bonsai and textrecipes — release velocity, themes, recent moves, and the top alternatives to consider.
bonsai keeps widening tidymodels' boosted-tree engine bench, catboost most recently
bonsai exists to attach non-core tree engines to parsnip's boost_tree() and rand_forest(), and the release history reads as a steady accumulation of them: partykit, aorsf, lightgbm, and now catboost. The 0.4.x line is spent making catboost behave like a full tidymodels citizen rather than adding anything new.
Text features finally stay sparse all the way to the model.
textrecipes supplies the recipes steps for turning text into model-ready columns: tokenizing, hashing, term frequency, TF-IDF, and word embeddings. Version 1.1.0 added a sparse argument to step_dummy_hash(), step_texthash(), step_tf() and step_tfidf() so they emit sparse vectors. The releases before it are a long consistency pass — keep_original_cols on every step that creates columns, informative errors on name collisions, tunable arguments documented, integer rather than double output where integers are what is meant.
bonsai exists to attach non-core tree engines to parsnip's boost_tree() and rand_forest(), and the release history reads as a steady accumulation of them: partykit, aorsf, lightgbm, and now catboost. The 0.4.x line is spent making catboost behave like a full tidymodels citizen rather than adding anything new.
Each engine follows the same arc — land it, then close the gaps that keep it from tuning cleanly (parameter naming, multi_predict, threading, case weights). Recent work is squarely in that second phase for catboost, with dials supplying the matching parameter objects on its own release schedule. Bug-fix density is high relative to new surface.
Expect the catboost integration to keep filling in tuning and GPU-related arguments before any further engine is added; the entries give no signal about which engine would come next.
textrecipes supplies the recipes steps for turning text into model-ready columns: tokenizing, hashing, term frequency, TF-IDF, and word embeddings. Version 1.1.0 added a sparse argument to step_dummy_hash(), step_texthash(), step_tf() and step_tfidf() so they emit sparse vectors. The releases before it are a long consistency pass — keep_original_cols on every step that creates columns, informative errors on name collisions, tunable arguments documented, integer rather than double output where integers are what is meant.
Two forces drive this package. One is memory: text produces wide, mostly-zero matrices, and the sparse work is the direct answer, landing in the same period that workflows learned to fit and predict on dgCMatrix input. The other is upstream churn — the tweets tokenizer was deprecated because tokenizers deprecated it, the politeness feature disappeared when textfeatures left Suggests. The package's own agenda is consistency; its release timing belongs to its dependencies.
Expect the sparse argument to spread to the remaining column-producing steps, since only four of them have it, and expect more steps to be reworked as recipes' own sparse-data support matures.
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 bonsai or textrecipes.
mlr3proba is shedding weight as its survival work moves into sibling packages
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
mlr3tuning is rebuilding its async machinery under a stable public surface
timetk swallowed anomalize whole, then went quiet for two years
modelbased is turning marginal effects into a full contrast grammar
easystats' parameters package absorbs one more model class every few weeks
See all bonsai alternatives → · See all textrecipes alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — tidymodels — within Analytics. bonsai and textrecipes 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. bonsai and textrecipes 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 bonsai alternatives in Analytics are ranked by recent ship velocity. Browse the "bonsai alternatives" section above for the current picks, or visit /alternatives/bonsai-r for the full list with editorial commentary on each.
Top textrecipes alternatives in Analytics are ranked by recent ship velocity. Browse the "textrecipes alternatives" section above for the current picks, or visit /alternatives/textrecipes for the full list with editorial commentary on each.