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Comparison · Infra & APIs
ddml vs ggh4x
A side-by-side editorial comparison of ddml and ggh4x — release velocity, themes, recent moves, and the top alternatives to consider.
ddml vs ggh4x: at a glance
What is ddml?
Double machine learning in R keeps adding estimands and the inference to go with them.
ddml implements double and debiased machine learning estimators, with a stacking layer so the nuisance functions can be fit by an ensemble rather than a single learner. The estimand list has grown from partially linear models to average treatment effects, treatment effects on the treated, and local average treatment effects, and 0.3.0 added one-way clustered inference. The most recent release is maintenance: xgboost syntax, glmnet binomial predictions, weights in the flexible partially linear IV estimator.
What is ggh4x?
Handed its axis and legend guides to ggplot2 and legendry, and kept the facets
ggh4x is a set of ggplot2 extensions — nested and manual facets, per-facet scales, forced panel sizes, and statistics such as stat_difference() and stat_rle(). Its guide functions, once a substantial part of the package, are deprecated: 0.3.0 redirected users to ggplot2's own guide arguments and to the legendry package, and 0.3.1 made the deprecated functions return plain equivalents. What remains as this package's own territory is facetting and panel layout.
ddml vs ggh4x: editorial side-by-side
ddml
0.0INFRA · APIS
Double machine learning in R keeps adding estimands and the inference to go with them.
◆ Current state
ddml implements double and debiased machine learning estimators, with a stacking layer so the nuisance functions can be fit by an ensemble rather than a single learner. The estimand list has grown from partially linear models to average treatment effects, treatment effects on the treated, and local average treatment effects, and 0.3.0 added one-way clustered inference. The most recent release is maintenance: xgboost syntax, glmnet binomial predictions, weights in the flexible partially linear IV estimator.
◆ Where it's heading
Two lines of work run in parallel. One extends what can be estimated, the other makes the estimates trustworthy under real data conditions, and the second is where the recent effort has gone: clustered standard errors, propensity score trimming, higher default fold counts, corrected ATE and LATE scores. Raising sample_folds and cv_folds to ten is a small change with a clear intent, trading compute for stability.
◆ Prediction
Clustered inference arrived one-way; two-way and multi-way clustering are the obvious continuation. The stacking layer is also accumulating edge-case handling, so expect more work on degenerate ensemble weights.
ggh4x
0.0INFRA · APIS
Handed its axis and legend guides to ggplot2 and legendry, and kept the facets
◆ Current state
ggh4x is a set of ggplot2 extensions — nested and manual facets, per-facet scales, forced panel sizes, and statistics such as stat_difference() and stat_rle(). Its guide functions, once a substantial part of the package, are deprecated: 0.3.0 redirected users to ggplot2's own guide arguments and to the legendry package, and 0.3.1 made the deprecated functions return plain equivalents. What remains as this package's own territory is facetting and panel layout.
◆ Where it's heading
The handover was signalled well in advance. Version 0.2.5 set the guides' lifecycle to 'questioning' and said outright they might migrate to a new package once ggplot2 overhauled its guide system, and 0.3.0 executed that eighteen months later. Much of the rest of the changelog is anticipatory compatibility work for upcoming ggplot2 releases, the standing cost of being an extension package. Releases carry playful codenames that say nothing about their contents.
◆ Prediction
The deprecated guide functions are scheduled for removal in the next non-hotfix release, so expect that next; continued work should concentrate on facets and panel sizing, the areas neither ggplot2 nor legendry has absorbed.
Alternatives to ddml and ggh4x
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 ddml or ggh4x.
Warp
Warp turned its quarter of software-factory essays into infrastructure you can buy.
Okta
Okta's developer blog is a Cross App Access campaign, now diluted by advocacy-team storytelling.
tealeaves
A leaf-temperature model that finished its job in 2020 and has stayed finished
Infisical
A credential platform assembled two or three pull requests at a time, never a headline
SigNoz
Search without knowing the field — SigNoz keeps lowering the cost of not knowing your schema
Recent activity from ddml and ggh4x
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 8mo agoddmlFixes for weighted FPLIV, binomial glmnet and empty stacking weights
- 1y agoggh4xDeprecated guides now return plain ggplot2 equivalents
- 1y agoggh4xGuide functions deprecated in favour of ggplot2 and legendry ⚡
- 1y agoddmlOne-way clustered inference and higher default fold counts
- 2y agoddmlPropensity score trimming added across the treatment effect estimators
- 2y agoddmlFixes permuted residuals returned by crossval
- 2y agoggh4xCRAN note fix and two facet bug fixes
- 2y agoddmlATT and LATE estimators join the supported estimands
- 2y agoggh4xAnticipatory changes for ggplot2 3.5.0
- 3y agoggh4xGuides marked 'questioning'; render_empty and panel-area sizing
- 3y agoggh4xSemi-discrete manual scales and facet whitespace handling
Frequently asked questions
What is the difference between ddml and ggh4x?
They serve adjacent needs but don't currently overlap on shipped themes. ddml and ggh4x 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 ddml better than ggh4x?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ddml and ggh4x 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 Infra & APIs products to evaluate alongside.
What are the best alternatives to ddml?
Top ddml alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ddml alternatives" section above for the current picks, or visit /alternatives/ddml for the full list with editorial commentary on each.
What are the best alternatives to ggh4x?
Top ggh4x alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggh4x alternatives" section above for the current picks, or visit /alternatives/ggh4x for the full list with editorial commentary on each.