metatools
SDTM supplemental-qualifier merging got sturdier, then the package went quiet for two years.
Comparison · Analytics
ggsurvfit vs mlr3extralearners
A side-by-side editorial comparison of ggsurvfit and mlr3extralearners — release velocity, themes, recent moves, and the top alternatives to consider.
ggsurvfit vs mlr3extralearners: at a glance
What is ggsurvfit?
ggsurvfit is in correctness-and-compatibility mode, not feature mode.
The package draws survival and cumulative-incidence curves on a ggplot2 grammar, with risk tables, p-values and quantile annotations. Recent releases are entirely fixes and upstream tracking: ggplot2 v4.0.0 compatibility in 2025, and a 2026 patch correcting a Gray-test p-value that could be reported for the wrong competing event.
What is mlr3extralearners?
The mlr3 learner catalogue is growing fast and pruning hyperparameters just as deliberately.
mlr3extralearners is the overflow catalogue for mlr3 learners that do not ship in the core packages — currently spanning H2O, Botorch, fastai, glmnet, survival and competing-risks models. The last two feature releases added roughly thirty learners between them. 1.6.0 then went the other way, cutting hyperparameters that were never correctly forwarded.
ggsurvfit vs mlr3extralearners: editorial side-by-side
ggsurvfit
2.5ANALYTICS
ggsurvfit is in correctness-and-compatibility mode, not feature mode.
◆ Current state
The package draws survival and cumulative-incidence curves on a ggplot2 grammar, with risk tables, p-values and quantile annotations. Recent releases are entirely fixes and upstream tracking: ggplot2 v4.0.0 compatibility in 2025, and a 2026 patch correcting a Gray-test p-value that could be reported for the wrong competing event.
◆ Where it's heading
The feature surface settled around 1.0.0, when risk-table alignment was exported and colour and linetype defaults became configurable. Since then the work is keeping pace with survival, ggplot2 and tidycmprsk changes, and closing cases where the plotted curve and the annotation disagreed — the p-value matched by position rather than name, confidence limits swapped for multi-state models, quantiles read off a plateau.
◆ Prediction
Expect the next release to track upstream survival or ggplot2 changes rather than add plotting features; the CDISC censoring convention adopted in Surv_CNSR() suggests further alignment with clinical data standards is the likelier direction.
mlr3extralearners
0.0ANALYTICS
The mlr3 learner catalogue is growing fast and pruning hyperparameters just as deliberately.
◆ Current state
mlr3extralearners is the overflow catalogue for mlr3 learners that do not ship in the core packages — currently spanning H2O, Botorch, fastai, glmnet, survival and competing-risks models. The last two feature releases added roughly thirty learners between them. 1.6.0 then went the other way, cutting hyperparameters that were never correctly forwarded.
◆ Where it's heading
Two forces are visible. The catalogue expands in bursts — 1.4.0 and 1.5.0 each added large batches, including a full H2O family and Bayesian regression models — while the maintenance releases in between are dominated by skipping tests on platforms where Python-backed learners crash. 1.6.0 marks a shift toward correctness of the existing surface: priority_lasso parameter sets reduced to what actually passes through, and Cox-inapplicable glmnet parameters removed.
◆ Prediction
The Python-backed learners are the recurring source of platform instability, so expect continued pinning and test-skipping there alongside the next batch of additions.
Alternatives to ggsurvfit and mlr3extralearners
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 ggsurvfit or mlr3extralearners.
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logrx
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lobstr
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giscoR
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See all ggsurvfit alternatives → · See all mlr3extralearners alternatives →
Recent activity from ggsurvfit and mlr3extralearners
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 20d agoggsurvfitGray-test p-values matched to the plotted outcome by name
- 1mo agomlr3extralearnersHyperparameter sets pruned where arguments were never forwarded
- 3mo agomlr3extralearnersDependency version updates
- 4mo agomlr3extralearnersPlatform-specific test skips
- 4mo agomlr3extralearnersSixteen new learners, including a full H2O family
- 6mo agomlr3extralearnersTwenty new learners and a survival learner rename
- 10mo agoggsurvfitggplot2 v4.0.0 compatibility and multi-state CI label fix
- 2y agoggsurvfitNegative follow-up times and a cloglog transformation
- 2y agoggsurvfitAesthetic defaults become switchable and alignment is exported
- 2y agoggsurvfitConfidence limits corrected for monotonicity-reversing transforms
- 3y agoggsurvfitGlue syntax in risk tables and coxph model support
Frequently asked questions
What is the difference between ggsurvfit and mlr3extralearners?
They serve adjacent needs but don't currently overlap on shipped themes. ggsurvfit 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 ggsurvfit better than mlr3extralearners?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggsurvfit 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 Analytics products to evaluate alongside.
What are the best alternatives to ggsurvfit?
Top ggsurvfit alternatives in Analytics are ranked by recent ship velocity. Browse the "ggsurvfit alternatives" section above for the current picks, or visit /alternatives/ggsurvfit for the full list with editorial commentary on each.
What are the best alternatives to mlr3extralearners?
Top mlr3extralearners alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3extralearners alternatives" section above for the current picks, or visit /alternatives/mlr3extralearners for the full list with editorial commentary on each.