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easystats vs probably

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

easystats vs probably: at a glance

Featureeasystatsprobably
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr ecosystem, meta-package, statistical reporting, licensingcalibration, conformal-inference, tidymodels, uncertainty
Last editorial update2h ago46m ago
WebsiteVisit →Visit →

What is easystats?

The easystats meta-package is install tooling wrapped around a relicensed ecosystem.

easystats is the meta-package for the easystats ecosystem, which spans insight, parameters, performance and their siblings. It ships almost no statistics of its own; its releases add installation helpers, ecosystem introspection functions and vignettes. The consequential release in this window is 0.7.0, which moved the whole ecosystem to an MIT license.

Read the full easystats trajectory →

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 →

easystats vs probably: editorial side-by-side

E
easystats
ANALYTICS
0.0

The easystats meta-package is install tooling wrapped around a relicensed ecosystem.

◆ Current state

easystats is the meta-package for the easystats ecosystem, which spans insight, parameters, performance and their siblings. It ships almost no statistics of its own; its releases add installation helpers, ecosystem introspection functions and vignettes. The consequential release in this window is 0.7.0, which moved the whole ecosystem to an MIT license.

◆ Where it's heading

Work concentrates on making the ecosystem legible and installable as a unit: easystats_packages() to enumerate it, easystats_citations() to count its citations, pak and r-universe support to install it, and a complete-workflow vignette to show it in use. Underneath that, 0.7.0 settled the licensing and formalized the author list. The pattern is a project tending its own boundaries rather than adding capability.

◆ Prediction

The recent additions are all introspection and installation helpers, so the next release most likely adds another of those or refreshes component versions rather than changing what the ecosystem does.

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.

Alternatives to easystats 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 easystats or probably.

See all easystats alternatives → · See all probably alternatives →

Recent activity from easystats and probably

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 agoeasystatseasystats_citations() added; install_latest() gains a github source
  4. 1y agoprobablyCalibration internals refactored; isotonic bootstrap bug fixed
  5. 1y agoeasystatsComplete-workflow vignette added; install_suggested() fix
  6. 2y agoeasystatseasystats_packages() added; pak used for installs when available
  7. 2y agoeasystatsR version policy vignette added
  8. 2y agoeasystatsFix for development package version detection
  9. 2y agoprobablyFix grouping sensitivity to variable type
  10. 2y agoeasystatsEcosystem relicensed to MIT; two new authors added
  11. 3y agoprobablySplit conformal and conformal quantile regression added
  12. 3y agoprobablyCalibration and conformal inference arrive in tidymodels

Frequently asked questions

What is the difference between easystats and probably?

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

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

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