metatools
SDTM supplemental-qualifier merging got sturdier, then the package went quiet for two years.
Comparison · Analytics
ggsurvfit vs superspreading
A side-by-side editorial comparison of ggsurvfit and superspreading — release velocity, themes, recent moves, and the top alternatives to consider.
ggsurvfit vs superspreading: 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 superspreading?
superspreading now asks whether a pathogen will emerge at all, not just how unevenly it spreads.
superspreading quantifies individual-level variation in transmission — the offspring distributions and summary metrics behind the 20/80 rule — and calculates probabilities of epidemic, extinction and containment. With 0.4.0 it added probability_emergence(), estimating whether an introduced pathogen can evolve into sustained human-to-human transmission. The package moved from experimental to stable in the same release.
ggsurvfit vs superspreading: 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.
superspreading
0.0ANALYTICS
superspreading now asks whether a pathogen will emerge at all, not just how unevenly it spreads.
◆ Current state
superspreading quantifies individual-level variation in transmission — the offspring distributions and summary metrics behind the 20/80 rule — and calculates probabilities of epidemic, extinction and containment. With 0.4.0 it added probability_emergence(), estimating whether an introduced pathogen can evolve into sustained human-to-human transmission. The package moved from experimental to stable in the same release.
◆ Where it's heading
Scope has widened one published framework at a time. 0.2.0 added network-based reproduction numbers, 0.3.0 added the Lloyd-Smith formulation of proportion_transmission() and vendored a branching-process simulator to drop the {bpmodels} dependency, and 0.4.0 implemented and extended the Antia et al. emergence model. Each addition brings a vignette reproducing the source paper's figures, which is how this package treats a method as delivered.
◆ Prediction
The established pattern — implement a published framework, extend it, document it against the original figures — makes another literature-derived addition likelier than internal refactoring.
Alternatives to ggsurvfit and superspreading
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 superspreading.
marquee
marquee is filling in the typographic details — outlines, border types, real font metrics for underlines.
logrx
A clinical-script logger that stopped shipping after its 0.2 line, changelogs made of merged PRs.
lobstr
R's object inspector is losing its view of the internals as CRAN closes off the private C API.
GSODR
A weather-station data client that broke one return type to hand back distances instead of bare IDs.
giscoR
giscoR's 1.0 moved its dataset index into the cache, so new Eurostat releases arrive without a package update.
See all ggsurvfit alternatives → · See all superspreading alternatives →
Recent activity from ggsurvfit and superspreading
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 20d agoggsurvfitGray-test p-values matched to the plotted outcome by name
- 10mo agoggsurvfitggplot2 v4.0.0 compatibility and multi-state CI label fix
- 1y agosuperspreadingprobability_emergence() extends the package into pathogen emergence risk ⚡
- 1y agosuperspreadingLloyd-Smith transmission proportions; bpmodels dependency removed
- 2y agoggsurvfitNegative follow-up times and a cloglog transformation
- 2y agosuperspreadingNetwork reproduction numbers and joint individual/population control
- 2y agoggsurvfitAesthetic defaults become switchable and alignment is exported
- 2y agoggsurvfitConfidence limits corrected for monotonicity-reversing transforms
- 2y agosuperspreadingFirst release: offspring distributions and epidemic risk metrics
- 3y agoggsurvfitGlue syntax in risk tables and coxph model support
Frequently asked questions
What is the difference between ggsurvfit and superspreading?
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 superspreading?
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 superspreading?
Top superspreading alternatives in Analytics are ranked by recent ship velocity. Browse the "superspreading alternatives" section above for the current picks, or visit /alternatives/superspreading for the full list with editorial commentary on each.