driveR
A cancer driver prioritization package that ships rarely and mostly to stay installable
Comparison · Infra & APIs
adjustedCurves vs simDAG
A side-by-side editorial comparison of adjustedCurves and simDAG — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:causal-inferencer-package
adjustedCurves vs simDAG: 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 simDAG?
simDAG grew a second simulation engine, then spent two releases surviving upstream breakage.
simDAG generates data from directed acyclic graphs, with a library of node types covering Gaussian, binomial, Poisson, negative binomial, zero-inflated, ordered regression, Cox, and Aalen models. The 1.0.0 milestone opened node_cox() to arbitrary baseline hazard functions, which lets continuous time-dependent hazards drive discrete-event simulations. The two most recent releases exist only to keep the package on CRAN through breakage in lme4 and simr.
adjustedCurves vs simDAG: 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.
simDAG
2.5INFRA · APIS
simDAG grew a second simulation engine, then spent two releases surviving upstream breakage.
◆ Current state
simDAG generates data from directed acyclic graphs, with a library of node types covering Gaussian, binomial, Poisson, negative binomial, zero-inflated, ordered regression, Cox, and Aalen models. The 1.0.0 milestone opened node_cox() to arbitrary baseline hazard functions, which lets continuous time-dependent hazards drive discrete-event simulations. The two most recent releases exist only to keep the package on CRAN through breakage in lme4 and simr.
◆ Where it's heading
The package has been widening what a simulation can represent rather than deepening any one node. Networks arrived in 0.4.0 so individuals could depend on each other, discrete-event simulation in continuous time arrived in 0.5.0 as an alternative to the discrete-time engine, and 1.0.0 connected the two by letting continuous hazards feed the event-driven path. Alongside that, node types keep accumulating for outcome families the framework could not previously generate.
◆ Prediction
Expect the node library to keep expanding into outcome types the discrete-event engine can now support, though the recent releases suggest upstream dependency churn will keep consuming release slots.
Alternatives to adjustedCurves and simDAG
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 simDAG.
metR
A meteorology ggplot2 extension where the netCDF reader became the main event
IsoriX
An isotope geolocation package still recovering from the r-spatial retirement
fdacluster
Functional data clustering grew from one algorithm into a comparable suite
profoc
A forecast combination package that spun its profiler out into its own project
mize
A numerical optimization toolkit that has been feature-complete and quiet since 2017
See all adjustedCurves alternatives → · See all simDAG alternatives →
Recent activity from adjustedCurves and simDAG
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 18d agosimDAGCRAN-retention patch for upstream lme4 breakage
- 3mo agosimDAGArbitrary baseline hazards connect node_cox() to discrete-event sims
- 4mo agosimDAGTest-only fix for an upstream simr update
- 5mo agosimDAGAdds node_polr() for ordinal outcomes and rsurv node support
- 6mo agoadjustedCurvesOff-by-one-step error corrected in cumulative incidence estimates
- 7mo agosimDAGAdds continuous-time discrete-event simulation ⚡
- 10mo agosimDAGAdds link functions to node types and fixes a broken seed default
- 1y agoadjustedCurvesIPTW curves can now extend to the last observed time
- 2y agoadjustedCurvesDropped arguments and a wrong multiple-imputation pooling formula
- 2y agoadjustedCurvesRisk tables, contrast consolidation and faster bootstrapping
- 3y agoadjustedCurvestmle and ostmle methods dropped
- 3y agoadjustedCurvesDependency compatibility and installation documentation
Frequently asked questions
What is the difference between adjustedCurves and simDAG?
Both compete on the same themes — causal-inference, r-package — within Infra & APIs. simDAG 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 adjustedCurves better than simDAG?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. simDAG 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 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 simDAG?
Top simDAG alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "simDAG alternatives" section above for the current picks, or visit /alternatives/simdag for the full list with editorial commentary on each.