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probably vs scales

A side-by-side editorial comparison of probably and scales — release velocity, themes, recent moves, and the top alternatives to consider.

probably vs scales: at a glance

Featureprobablyscales
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themescalibration, conformal-inference, tidymodels, uncertaintyr, ggplot2, data-visualization, axis-labels
Last editorial update51m ago5h ago
WebsiteVisit →Visit →

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.

Read the full probably trajectory →

What is scales?

scales keeps widening what ggplot2 can put on an axis.

scales supplies the breaks, labels and transformations behind ggplot2's axes and legends. Unlike much of the tidyverse infrastructure around it, it still ships genuine feature work each release: native timespan handling in 1.3.0, then custom range-training classes and label_glue() in 1.4.0.

Read the full scales trajectory →

probably vs scales: editorial side-by-side

P
probably
ANALYTICS
0.0

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.

S
scales
ANALYTICS
0.0

scales keeps widening what ggplot2 can put on an axis.

◆ Current state

scales supplies the breaks, labels and transformations behind ggplot2's axes and legends. Unlike much of the tidyverse infrastructure around it, it still ships genuine feature work each release: native timespan handling in 1.3.0, then custom range-training classes and label_glue() in 1.4.0.

◆ Where it's heading

The arc runs toward extensibility and type coverage. First came built-in support for awkward types like difftime and hms; 1.4.0 inverts that by letting any third-party class participate in range training simply by implementing range() or levels(). Labelling is getting more expressive rather than merely more numerous.

◆ Prediction

Expect continued type-support and labelling work, with extension points that let downstream packages plug in their own classes instead of scales enumerating every one.

Alternatives to probably and scales

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 probably or scales.

See all probably alternatives → · See all scales alternatives →

Recent activity from probably and scales

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 10mo agoprobablyConformal objects gain required_pkgs() and butcher methods
  2. 1y agoprobablyggplot2 test updates and a clearer validation-set error
  3. 1y agoprobablyCalibration internals refactored; isotonic bootstrap bug fixed
  4. 1y agoscalesscales 1.4.0 opens range training to custom classes
  5. 2y agoprobablyFix grouping sensitivity to variable type
  6. 2y agoscalesscales 1.3.0 makes timespans first-class on axes
  7. 3y agoprobablySplit conformal and conformal quantile regression added
  8. 3y agoprobablyCalibration and conformal inference arrive in tidymodels
  9. 3y agoscalesscales 1.2.1 re-documents to fix .Rd HTML issues
  10. 4y agoscalesscales 1.2.0 fixes currency sign order and adds scale_cut
  11. 6y agoscalesscales 1.1.1 fixes palette inversion and adds oob_keep()
  12. 6y agoscalesscales 1.1.0 reorganises breaks and labels into a naming scheme

Frequently asked questions

What is the difference between probably and scales?

They serve adjacent needs but don't currently overlap on shipped themes. probably and scales 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 probably better than scales?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. probably and scales 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 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.

What are the best alternatives to scales?

Top scales alternatives in Analytics are ranked by recent ship velocity. Browse the "scales alternatives" section above for the current picks, or visit /alternatives/scales-r for the full list with editorial commentary on each.