mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
patchwork vs probably
A side-by-side editorial comparison of patchwork and probably — release velocity, themes, recent moves, and the top alternatives to consider.
patchwork vs probably: at a glance
What is patchwork?
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.
What is probably?
The package that made calibration a step instead of an afterthought.
probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.
patchwork vs probably: editorial side-by-side
patchwork
0.0ANALYTICS
patchwork stopped being a ggplot composer and became a page composer.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
probably
0.0ANALYTICS
The package that made calibration a step instead of an afterthought.
◆ Current state
probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.
◆ Where it's heading
The recent releases are about making these objects survive leaving the session. butcher and required_pkgs() methods are what a model needs to be pinned, containerised and served, and their arrival alongside workflows adding a tailor postprocessing stage and vetiver adding probably support points the same way: calibration is being moved out of analysis scripts and into the deployed pipeline. The cal_*_none() reference implementations are the tell that calibration is now something people tune rather than apply once.
◆ Prediction
Expect the calibration functions to be reachable directly from a tuned workflow's postprocessing stage rather than applied to predictions afterwards, following the tailor integration that workflows just shipped.
Alternatives to patchwork and probably
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 patchwork or probably.
mlr3viz
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
mlr3tuning
mlr3tuning is rebuilding its async machinery under a stable public surface
timetk
timetk swallowed anomalize whole, then went quiet for two years
modelbased
modelbased is turning marginal effects into a full contrast grammar
parameters
easystats' parameters package absorbs one more model class every few weeks
See all patchwork alternatives → · See all probably alternatives →
Recent activity from patchwork and probably
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 10mo agoprobablyConformal objects gain required_pkgs() and butcher methods
- 11mo agopatchworkFix spurious load-time warnings
- 1y agoprobablyggplot2 test updates and a clearer validation-set error
- 1y agopatchworkCompatibility pass for the next ggplot2 release
- 1y agoprobablyCalibration internals refactored; isotonic bootstrap bug fixed
- 1y agopatchworkgt tables become first-class patchwork objects ⚡
- 2y agoprobablyFix grouping sensitivity to variable type
- 2y agopatchworkAxis collection and free() arrive
- 3y agopatchworkPatchworks behave like lists; NULL becomes a no-op
- 3y agoprobablySplit conformal and conformal quantile regression added
- 3y agoprobablyCalibration and conformal inference arrive in tidymodels ⚡
- 3y agopatchworkClearer error when plotting space is too small
Frequently asked questions
What is the difference between patchwork and probably?
They serve adjacent needs but don't currently overlap on shipped themes. patchwork and probably 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 patchwork better than probably?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. patchwork and probably 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 patchwork?
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.
What are the best alternatives to probably?
Top probably alternatives in Analytics are ranked by recent ship velocity. Browse the "probably alternatives" section above for the current picks, or visit /alternatives/probably for the full list with editorial commentary on each.