HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
Comparison · Infra & APIs
adjustedCurves vs ggdist
A side-by-side editorial comparison of adjustedCurves and ggdist — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
adjustedCurves vs ggdist: at a glance
What is adjustedCurves?
A survival curve package spending release after release correcting its own estimates
adjustedCurves computes confounder-adjusted survival and cumulative incidence curves across a range of estimators - IPTW, AIPTW, Aalen-Johansen, direct standardisation - with support for multiple imputation and bootstrapping. The recent releases are dominated by corrections to numbers the package already reported. Version 0.11.4 fixed cumulative incidence estimates under method="aalen_johansen" that were being read one time step early, which the maintainer notes could differ substantially when events are few, and added risk and event counts to the ggsurvplot conversion including correctly pooled values under multiple imputation.
What is ggdist?
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.
adjustedCurves vs ggdist: editorial side-by-side
adjustedCurves
0.0INFRA · APIS
A survival curve package spending release after release correcting its own estimates
◆ Current state
adjustedCurves computes confounder-adjusted survival and cumulative incidence curves across a range of estimators - IPTW, AIPTW, Aalen-Johansen, direct standardisation - with support for multiple imputation and bootstrapping. The recent releases are dominated by corrections to numbers the package already reported. Version 0.11.4 fixed cumulative incidence estimates under method="aalen_johansen" that were being read one time step early, which the maintainer notes could differ substantially when events are few, and added risk and event counts to the ggsurvplot conversion including correctly pooled values under multiple imputation.
◆ Where it's heading
Multiple imputation is the recurring fault line. The standard error pooling formula was implemented incorrectly until 0.11.2, then fixed again in 0.11.3 for the bootstrapping-plus-imputation combination, and 0.11.4 added the pooled risk table values that had previously been omitted entirely. A separate thread quietly removed capability: tmle and ostmle methods went in 0.10.0, and tmle support was pulled again in 0.11.1 after the concrete package left CRAN. Feature work does happen - risk tables, contrast arguments, the extend_to_last control on IPTW curves - but it is outweighed by correction.
◆ Prediction
Expect continued estimator-level corrections rather than new methods, and a possible return of tmle support if its upstream dependency returns to CRAN, since the removal was described as temporary.
ggdist
0.0INFRA · APIS
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
◆ Current state
ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.
◆ Where it's heading
Two threads run in parallel and keep converging. One is statistical: pluggable density estimators arrived first, then became the default, then gained weights and quantile histograms. The other is compositional: sub-geometries acquired their own guides, then their own scales, so a slab's thickness axis is now a first-class annotated dimension. Cadence has stretched from twice-yearly to roughly annual, with the recent work tightening existing surface rather than opening new.
◆ Prediction
Subguides gained subscales a release later, so the remaining asymmetry is in the sub-geometry system rather than the statistics; expect the next release to continue that fill-in work.
Alternatives to adjustedCurves and ggdist
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 adjustedCurves or ggdist.
bsvarSIGNs
Sign, zero and narrative restrictions brought into the bsvars ecosystem.
estimatr
Fast design-based estimators for experiments, coasting on CRAN patches.
ipaddress
IP address vectors for R that hit 1.0 and then went quiet.
ltertools
A column-key toolkit for stitching decades of ecological field data into one table.
finnts
Microsoft's automated forecasting framework, still mostly a one-maintainer effort.
See all adjustedCurves alternatives → · See all ggdist alternatives →
Recent activity from adjustedCurves and ggdist
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 6mo agoadjustedCurvesOff-by-one-step error corrected in cumulative incidence estimates
- 1y agoadjustedCurvesIPTW curves can now extend to the last observed time
- 1y agoggdistPer-geometry thickness subscales and settable defaults
- 2y agoadjustedCurvesDropped arguments and a wrong multiple-imputation pooling formula
- 2y agoadjustedCurvesRisk tables, contrast consolidation and faster bootstrapping
- 2y agoggdistBlurred dotplots draw Monte Carlo error; weights reach every estimator ⚡
- 2y agoggdistC++ dotplot binning and safer bandwidth fallbacks
- 3y agoggdistBounded density becomes the default; existing charts change ⚡
- 3y agoadjustedCurvestmle and ostmle methods dropped
- 3y agoggdistCategorical distributions, hex layouts, pluggable density estimators
- 3y agoadjustedCurvesDependency compatibility and installation documentation
- 4y agoggdistComputed variables shared across sub-geometries
Frequently asked questions
What is the difference between adjustedCurves and ggdist?
Both compete on the same themes — r-package — within Infra & APIs. adjustedCurves and ggdist 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 adjustedCurves better than ggdist?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. adjustedCurves and ggdist 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 adjustedCurves?
Top adjustedCurves alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "adjustedCurves alternatives" section above for the current picks, or visit /alternatives/adjustedcurves for the full list with editorial commentary on each.
What are the best alternatives to ggdist?
Top ggdist alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggdist alternatives" section above for the current picks, or visit /alternatives/ggdist for the full list with editorial commentary on each.