← Back to home
Comparison · Analytics

mlr3proba vs scales

A side-by-side editorial comparison of mlr3proba and scales — release velocity, themes, recent moves, and the top alternatives to consider.

mlr3proba vs scales: at a glance

Featuremlr3probascales
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesmlr3, survival-analysis, probabilistic-learning, dependency-maintenancer, ggplot2, data-visualization, axis-labels
Last editorial update1h ago7h ago
WebsiteVisit →Visit →

What is mlr3proba?

mlr3proba is shedding weight as its survival work moves into sibling packages

mlr3proba provides probabilistic supervised learning for mlr3 — survival analysis, density estimation, and the measures that go with them. Recent releases are almost entirely upkeep: a distr6 fork to work around an upstream problem, an ooplah fix, a predict-type correction, and registration in mlr_reflections$loaded_packages. The one deletion is telling, with LearnerDensPenalized removed after pendensity left CRAN.

Read the full mlr3proba 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 →

mlr3proba vs scales: editorial side-by-side

M
mlr3proba
ANALYTICS
0.0

mlr3proba is shedding weight as its survival work moves into sibling packages

◆ Current state

mlr3proba provides probabilistic supervised learning for mlr3 — survival analysis, density estimation, and the measures that go with them. Recent releases are almost entirely upkeep: a distr6 fork to work around an upstream problem, an ooplah fix, a predict-type correction, and registration in mlr_reflections$loaded_packages. The one deletion is telling, with LearnerDensPenalized removed after pendensity left CRAN.

◆ Where it's heading

The package is being pared back rather than extended. Its README now points at survdistr and mlr3cmprsk as matured alternatives for parts of what it covers, which reads as scope being handed off, while the Cox proportional-hazards autoplot arrived from mlr3viz in the other direction. Several fixes exist to route around dependencies that broke or disappeared, which is the recurring cost of building on a long chain of specialized CRAN packages.

◆ Prediction

The dependency churn suggests more consolidation — further reliance on survdistr and mlr3cmprsk, and more learners retired when the package underneath them goes unmaintained.

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 mlr3proba 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 mlr3proba or scales.

See all mlr3proba alternatives → · See all scales alternatives →

Recent activity from mlr3proba and scales

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

  1. 2mo agomlr3probamlr3proba 0.8.10 registers itself in the mlr3 loaded-packages registry
  2. 4mo agomlr3probamlr3proba 0.8.9 drops LearnerDensPenalized after pendensity left CRAN
  3. 5mo agomlr3probamlr3proba 0.8.8
  4. 5mo agomlr3probamlr3proba 0.8.7
  5. 9mo agomlr3probamlr3proba 0.8.5
  6. 10mo agomlr3probamlr3proba 0.8.4 takes over the Cox proportional-hazards autoplot
  7. 1y agoscalesscales 1.4.0 opens range training to custom classes
  8. 2y agoscalesscales 1.3.0 makes timespans first-class on axes
  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 mlr3proba and scales?

They serve adjacent needs but don't currently overlap on shipped themes. mlr3proba 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 mlr3proba better than scales?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3proba 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 mlr3proba?

Top mlr3proba alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3proba alternatives" section above for the current picks, or visit /alternatives/mlr3proba 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.