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see vs tune

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

see vs tune: at a glance

Featureseetune
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr, easystats, data-visualization, ggplot2hyperparameter-tuning, tidymodels, parallelism, postprocessing
Last editorial update2h ago53m ago
WebsiteVisit →Visit →

What is see?

see grows wherever easystats adds a diagnostic, one plot method at a time.

see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.

Read the full see trajectory →

What is tune?

tune extends tuning past the model itself to postprocessors, and adds a second parallel backend

tune runs hyperparameter search for tidymodels. Version 2.0.0 rewrote tune_grid() to make postprocessing tunable alongside preprocessing and the model, changed the .config naming scheme to match, and added mirai as a parallel backend next to future. Version 2.1.0 followed with quantile regression support and a replacement Gaussian process engine.

Read the full tune trajectory →

see vs tune: editorial side-by-side

S
see
ANALYTICS
0.0

see grows wherever easystats adds a diagnostic, one plot method at a time.

◆ Current state

see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.

◆ Where it's heading

Growth here is downstream-driven rather than self-directed: see expands to cover new diagnostics as easystats produces them. Running alongside that is a sustained investment in presentation control — theme arguments on plot methods, elements that scale with base_size — which suits users embedding these plots in documents rather than glancing at them interactively.

◆ Prediction

Expect new plot methods to keep arriving in step with performance and parameters releases, with continued theming work rather than any change in the package's scope.

T
tune
ANALYTICS
0.0

tune extends tuning past the model itself to postprocessors, and adds a second parallel backend

◆ Current state

tune runs hyperparameter search for tidymodels. Version 2.0.0 rewrote tune_grid() to make postprocessing tunable alongside preprocessing and the model, changed the .config naming scheme to match, and added mirai as a parallel backend next to future. Version 2.1.0 followed with quantile regression support and a replacement Gaussian process engine.

◆ Where it's heading

Two migrations run through this timeline. The tunable surface keeps widening - first censored regression as a mode, then postprocessors via tailor - so that a candidate is now a preprocessor, model and postprocessor triple rather than just a model. The parallel story has moved from foreach to future and now to mirai, each step deprecating the last. Neither is finished.

◆ Prediction

Expect the foreach path to be removed outright, and the postprocessing surface to grow as tailor gains more steps; the GauPro switch will likely need follow-up as its behavior differs from the old engine.

Alternatives to see and tune

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 see or tune.

See all see alternatives → · See all tune alternatives →

Recent activity from see and tune

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

  1. 1mo agoseesee 0.14.1 adds plots for prior checks and grouped means
  2. 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
  3. 3mo agotuneQuantile regression tuning; Bayesian search moves to GauPro
  4. 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
  5. 9mo agotuneFixes int_pctl() with future parallelism on last_fit()
  6. 11mo agoseesee 0.12.0 extends normality checks to psych factor models
  7. 11mo agotunePostprocessors become tunable; mirai joins future as a backend
  8. 11mo agotuneDevelopment snapshot re-enabling skipped tests
  9. 1y agoseesee 0.11.0 scales theme elements with base_size
  10. 1y agotuneWarns on foreach parallelism; space-filling grids by default
  11. 1y agoseesee 0.10.0 plots random-effect group levels for mixed models
  12. 2y agotuneFixes parallel tuning errors under multisession plans

Frequently asked questions

What is the difference between see and tune?

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

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

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

What are the best alternatives to tune?

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