← Back to home
Comparison · Analytics

gdverse vs ggstats

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

gdverse vs ggstats: at a glance

Featuregdverseggstats
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesspatial statistics, geographical detector, confidence intervals, reticulateggplot2, data-visualization, likert, regression-models
Last editorial update46m ago1h ago
WebsiteVisit →Visit →

What is gdverse?

gdverse is turning geographical detector methods into inference, not just point estimates.

A geographical detector toolkit for spatial stratified heterogeneity, shipping small numbered releases every few months. Recent work centres on statistical rigour: confidence intervals for the q-statistic (experimental in 1.3-2, made more robust in 1.6), reported significance for interaction detection, and a fix for stratification collision in that same interaction path. The rest is Python-interop maintenance — reticulate compatibility, parallel stability in cpd_disc, and dependency configuration.

Read the full gdverse trajectory →

What is ggstats?

ggstats keeps widening what a coefficient or Likert plot can be

ggstats extends ggplot2 with statistical plotting: model coefficient plots, Likert and diverging bar charts, proportion geometries and the helpers that make them behave. Recent releases have added an experimental gglikert_side(), left and right total columns for gglikert(), and survey-object support across the Likert family. Development is steady and CRAN-paced, with releases every two to three months.

Read the full ggstats trajectory →

gdverse vs ggstats: editorial side-by-side

G
gdverse
ANALYTICS
0.0

gdverse is turning geographical detector methods into inference, not just point estimates.

◆ Current state

A geographical detector toolkit for spatial stratified heterogeneity, shipping small numbered releases every few months. Recent work centres on statistical rigour: confidence intervals for the q-statistic (experimental in 1.3-2, made more robust in 1.6), reported significance for interaction detection, and a fix for stratification collision in that same interaction path. The rest is Python-interop maintenance — reticulate compatibility, parallel stability in cpd_disc, and dependency configuration.

◆ Where it's heading

The arc is from computing detector statistics to qualifying them. Confidence intervals, significance reporting and non-centrality parameter estimation are all about telling users how much to trust a q-value, which is the gap between a research script and a package other people cite. The Python-dependency work is the recurring tax on that: several releases exist mainly to keep reticulate-backed models passing checks.

◆ Prediction

Expect the experimental q-statistic confidence intervals to be promoted to a stable, documented interface across the detector family, since the last two releases have both worked on their robustness and reporting.

G
ggstats
ANALYTICS
0.0

ggstats keeps widening what a coefficient or Likert plot can be

◆ Current state

ggstats extends ggplot2 with statistical plotting: model coefficient plots, Likert and diverging bar charts, proportion geometries and the helpers that make them behave. Recent releases have added an experimental gglikert_side(), left and right total columns for gglikert(), and survey-object support across the Likert family. Development is steady and CRAN-paced, with releases every two to three months.

◆ Where it's heading

Two long-running threads. The coefficient side has been consolidating — ggcoef_multinom() and ggcoef_multicomponents() soft-deprecated in favour of a unified ggcoef_model() with group_by, plus new ggcoef_dodged() and ggcoef_faceted() variants. The Likert side keeps expanding outward instead, absorbing survey objects, total columns and side-by-side layouts. Underneath both is a steady tax of ggplot2 and vctrs compatibility work, including tracking the geom_errorbarh() deprecation in ggplot2 4.0.0.

◆ Prediction

Expect gglikert_side() to lose its experimental status once its interface settles, and the deprecated multinomial entry points to be removed in a future release now that ggcoef_model() covers their cases.

Alternatives to gdverse and ggstats

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 gdverse or ggstats.

See all gdverse alternatives → · See all ggstats alternatives →

Recent activity from gdverse and ggstats

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

  1. 5mo agoggstatsgglikert_side() and total columns for Likert plots
  2. 6mo agogdverseInteraction detection reports significance; stratification collision fixed
  3. 7mo agoggstatsLikert functions accept survey objects
  4. 10mo agogdversePython examples wrapped to stop CRAN check failures
  5. 10mo agogdversecpd_disc refactored for parallel stability and reticulate compatibility
  6. 11mo agoggstatsTable output for ggcoef_compare(); x-axis limits harmonised
  7. 1y agoggstatsggstats 0.10.0
  8. 1y agogdverseAdds package citation metadata
  9. 1y agoggstatsCoefficient plots unified around ggcoef_model() with grouping
  10. 1y agogdverseExperimental confidence intervals for the q statistic
  11. 1y agoggstatsDiverging and Likert geoms redesigned; connector geoms added
  12. 1y agogdversePlot method bug fixes across four detector models

Frequently asked questions

What is the difference between gdverse and ggstats?

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

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

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

What are the best alternatives to ggstats?

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