mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
A side-by-side editorial comparison of embed and patchwork — release velocity, themes, recent moves, and the top alternatives to consider.
embed keeps adding encoding steps while shedding its deep-learning dependencies
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
patchwork stopped being a ggplot composer and became a page composer.
patchwork assembles plots into compositions with arithmetic operators, and the 1.x line has steadily hardened that grammar: guide and axis collection, free() to exempt a plot from alignment, inset_element() for overlays, and list-like behaviour so lapply() and length() work on a patchwork. Version 1.3.0 added native gt table support. The two releases since are a load-time warning fix and a compatibility pass for the next ggplot2 release.
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
Two quiet directions run through these releases. One is making the steps tunable rather than fixed, so they participate properly in tidymodels grids. The other is boundary maintenance: heavy dependencies pushed to Suggests, overlapping steps handed to the package that owns them. Recent releases are thin and fix-driven.
Expect further consolidation with textrecipes over which package owns which encoding step, and continued upkeep against xgboost and uwot releases rather than new step families.
patchwork assembles plots into compositions with arithmetic operators, and the 1.x line has steadily hardened that grammar: guide and axis collection, free() to exempt a plot from alignment, inset_element() for overlays, and list-like behaviour so lapply() and length() work on a patchwork. Version 1.3.0 added native gt table support. The two releases since are a load-time warning fix and a compatibility pass for the next ggplot2 release.
The centre of gravity is shifting from alignment mechanics to composition scope. Early releases were almost entirely bug fixes against grid and ggplot2 internals — strip placement, fixed aspect ratios, guide merging. Recent ones add object types and escape hatches instead. Between feature cycles the package is in maintenance defined by ggplot2's release calendar, which is what 1.3.1 is in its entirety.
Expect wrap_table() to grow beyond gt to other table objects, and expect the next substantive release to be triggered by a ggplot2 internals change rather than by a patchwork roadmap.
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 embed or patchwork.
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 embed alternatives → · See all patchwork alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. embed and patchwork 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. embed and patchwork 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 embed alternatives in Analytics are ranked by recent ship velocity. Browse the "embed alternatives" section above for the current picks, or visit /alternatives/embed for the full list with editorial commentary on each.
Top patchwork alternatives in Analytics are ranked by recent ship velocity. Browse the "patchwork alternatives" section above for the current picks, or visit /alternatives/patchwork for the full list with editorial commentary on each.