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Comparison · Analytics
ggstats vs spEDM
A side-by-side editorial comparison of ggstats and spEDM — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
ggstats vs spEDM: at a glance
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.
What is spEDM?
Spatial causal discovery in R, one exposed method per release
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
ggstats vs spEDM: editorial side-by-side
ggstats
0.0ANALYTICS
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.
spEDM
0.0ANALYTICS
Spatial causal discovery in R, one exposed method per release
◆ Current state
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
◆ Where it's heading
The cadence is steady and predictable: each release surfaces one more EDM method as an R-level API with a vignette, then spends the rest of its notes on parameter-handling consistency across the generics. Breaking changes are frequent and deliberate — argument renames, parameter reordering, NA-handling defaults — which reads as a package still settling its interface while the method surface expands. Shared changes appear in tEDM within days, so interface churn lands on both packages at once.
◆ Prediction
Expect the next release to expose another causality variant at the R level with an accompanying vignette, and to continue renaming or reordering parameters toward consistency across the spatial and temporal packages.
Alternatives to ggstats and spEDM
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 ggstats or spEDM.
fastml
fastml added survival modelling and leakage-proof resampling, moving past classification and regression.
abclass
abclass built out angle-based classifiers in 2022, then went quiet except for CRAN upkeep.
churon
churon is spending its entire release history getting a Rust ONNX binding through CRAN.
firatheme
firatheme woke up after four years and started fixing what ggplot2 changed underneath it.
bagyo
bagyo reached CRAN as a Philippine tropical cyclone dataset, with its tags stamped out of order.
See all ggstats alternatives → · See all spEDM alternatives →
Recent activity from ggstats and spEDM
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 4mo agospEDMData slicing for large-scale pattern causality, plus API breaks
- 5mo agoggstatsgglikert_side() and total columns for Likert plots
- 6mo agospEDMspEDM 1.11
- 6mo agospEDMSpatially convergent partial cross mapping reaches the R API
- 7mo agoggstatsLikert functions accept survey objects
- 8mo agospEDMRaster cross mapping with anisotropic embedding
- 11mo agoggstatsTable output for ggcoef_compare(); x-axis limits harmonised
- 11mo agospEDMConfigurable distance metrics and multithreaded distance computation
- 1y agoggstatsggstats 0.10.0
- 1y agospEDMSpatial logistic map exposed at the R level
- 1y agoggstatsCoefficient plots unified around ggcoef_model() with grouping
- 1y agoggstatsDiverging and Likert geoms redesigned; connector geoms added
Frequently asked questions
What is the difference between ggstats and spEDM?
Both compete on the same themes — r-package — within Analytics. ggstats and spEDM 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 ggstats better than spEDM?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggstats and spEDM 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 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.
What are the best alternatives to spEDM?
Top spEDM alternatives in Analytics are ranked by recent ship velocity. Browse the "spEDM alternatives" section above for the current picks, or visit /alternatives/spedm for the full list with editorial commentary on each.