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Comparison · Infra & APIs

ggdist vs inti

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

Shared themes:r-package

ggdist vs inti: at a glance

Featureggdistinti
SectorInfra & APIsInfra & APIs
Velocity score0.02.5
Sparks · 30d00
Top themesdata-visualization, uncertainty, bayesian-statistics, ggplot2plant-science, pca, shiny, reproducible-reporting
Last editorial update22h ago1h ago
WebsiteVisit →Visit →

What is ggdist?

The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.

ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.

Read the full ggdist trajectory →

What is inti?

inti keeps compounding small statistics and publishing tools for plant-science labs.

inti bundles the tooling a plant-science lab uses end to end: the Yupana analysis app, the Tarpuy experiment planner, scihub and rticle document rendering, and helpers for ANOVA and heritability. The 0.7.x line has been dominated by one thread, a PCA sub-module in Yupana carrying variable contribution, dimension correlation, supplementary variables and per-dimension selection. Releases are cumulative: 0.7.0, 0.7.1 and 0.7.2 all restate the same PCA lines, with each tag adding a few items on top, and 0.7.0 itself is split across two tags published 35 seconds apart.

Read the full inti trajectory →

ggdist vs inti: editorial side-by-side

G
ggdist
INFRA · APIS
0.0

The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.

◆ Current state

ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.

◆ Where it's heading

Two threads run in parallel and keep converging. One is statistical: pluggable density estimators arrived first, then became the default, then gained weights and quantile histograms. The other is compositional: sub-geometries acquired their own guides, then their own scales, so a slab's thickness axis is now a first-class annotated dimension. Cadence has stretched from twice-yearly to roughly annual, with the recent work tightening existing surface rather than opening new.

◆ Prediction

Subguides gained subscales a release later, so the remaining asymmetry is in the sub-geometry system rather than the statistics; expect the next release to continue that fill-in work.

I
inti
INFRA · APIS
2.5

inti keeps compounding small statistics and publishing tools for plant-science labs.

◆ Current state

inti bundles the tooling a plant-science lab uses end to end: the Yupana analysis app, the Tarpuy experiment planner, scihub and rticle document rendering, and helpers for ANOVA and heritability. The 0.7.x line has been dominated by one thread, a PCA sub-module in Yupana carrying variable contribution, dimension correlation, supplementary variables and per-dimension selection. Releases are cumulative: 0.7.0, 0.7.1 and 0.7.2 all restate the same PCA lines, with each tag adding a few items on top, and 0.7.0 itself is split across two tags published 35 seconds apart.

◆ Where it's heading

Two axes are moving. Analysis is deepening inside Yupana, where PCA went from a single view to a sub-module with its own contribution and correlation outputs across three tags. Publishing is widening around rticle() and scihub(), which now handle Google Docs markdown, crossrefs and page numbers, continuing the gdocs2qmd work from the 0.6 line. Neither is a change of direction; the package accretes features where the maintainer's own research workflow needs them.

◆ Prediction

Expect the next tag to extend the PCA sub-module again and add another rticle() or scihub() rendering detail, on the two-to-six-week cadence the 0.7 line has held.

Alternatives to ggdist and inti

Other Infra & APIs 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 ggdist or inti.

See all ggdist alternatives → · See all inti alternatives →

Recent activity from ggdist and inti

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

  1. 22d agointiscihub() gains pages, rticle() improves crossrefs
  2. 1mo agointi0.7.1 restates the 0.7.0 PCA and Tarpuy notes
  3. 2mo agointiPCA sub-module adds contribution and dimension correlation
  4. 2mo agointiNew rticle() renders Google Docs markdown into articles
  5. 10mo agointiH2cal() takes factors as a formula; scihub() templates updated
  6. 11mo agointiSciHub RStudio addin arrives; gdocs2qmd table export fixed
  7. 1y agoggdistPer-geometry thickness subscales and settable defaults
  8. 2y agoggdistBlurred dotplots draw Monte Carlo error; weights reach every estimator
  9. 2y agoggdistC++ dotplot binning and safer bandwidth fallbacks
  10. 3y agoggdistBounded density becomes the default; existing charts change
  11. 3y agoggdistCategorical distributions, hex layouts, pluggable density estimators
  12. 4y agoggdistComputed variables shared across sub-geometries

Frequently asked questions

What is the difference between ggdist and inti?

Both compete on the same themes — r-package — within Infra & APIs. inti is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is ggdist better than inti?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. inti is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.

What are the best alternatives to ggdist?

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

What are the best alternatives to inti?

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