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Comparison · Infra & APIs

adjustedCurves vs driveR

A side-by-side editorial comparison of adjustedCurves and driveR — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

adjustedCurves vs driveR: at a glance

FeatureadjustedCurvesdriveR
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themessurvival-analysis, causal-inference, r-package, biostatisticscancer-genomics, bioinformatics, r-package, driver-genes
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

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.

Read the full adjustedCurves trajectory →

What is driveR?

A cancer driver prioritization package that ships rarely and mostly to stay installable

driveR prioritizes cancer driver genes from somatic variant and copy number data, combining coding impact scores, noncoding impact, copy number alteration scores and hotspot annotations into a multi-task learning classification model. Version 0.5.0 added gene-level SCNA data frames as an accepted input to create_features_df(), with an example table shipped alongside, widening the entry point beyond the segment-level format. The same release moved org.Hs.eg.db and both hg19 and hg38 TxDb annotation packages from Imports to Suggests under new CRAN policy, with dependent functions now raising an error when they are absent rather than silently degrading.

Read the full driveR trajectory →

adjustedCurves vs driveR: editorial side-by-side

A
adjustedCurves
INFRA · APIS
0.0

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.

D
driveR
INFRA · APIS
0.0

A cancer driver prioritization package that ships rarely and mostly to stay installable

◆ Current state

driveR prioritizes cancer driver genes from somatic variant and copy number data, combining coding impact scores, noncoding impact, copy number alteration scores and hotspot annotations into a multi-task learning classification model. Version 0.5.0 added gene-level SCNA data frames as an accepted input to create_features_df(), with an example table shipped alongside, widening the entry point beyond the segment-level format. The same release moved org.Hs.eg.db and both hg19 and hg38 TxDb annotation packages from Imports to Suggests under new CRAN policy, with dependent functions now raising an error when they are absent rather than silently degrading.

◆ Where it's heading

Releases are infrequent and split cleanly between capability and correction. GRCh38 support arrived in 0.4.0 and cancer-type-specific thresholds were refreshed in 0.3.0, while the 0.2.x pair fixed scoring errors serious enough to require retraining: a column name mismatch meant the SCNA score was not being computed at all, and MCR table coordinates needed converting from hg18 to hg19. Both times the bundled classification model and thresholds were rebuilt as a consequence. Since 0.4.0 the changes have been input handling and packaging rather than method.

◆ Prediction

The move of the annotation databases to Suggests suggests a leaner install is the current priority; the entries give no indication of planned model or scoring changes.

Alternatives to adjustedCurves and driveR

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 driveR.

See all adjustedCurves alternatives → · See all driveR alternatives →

Recent activity from adjustedCurves and driveR

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 6mo agoadjustedCurvesOff-by-one-step error corrected in cumulative incidence estimates
  2. 7mo agodriveRGene-level copy number input accepted, annotation packages made optional
  3. 1y agoadjustedCurvesIPTW curves can now extend to the last observed time
  4. 2y agoadjustedCurvesDropped arguments and a wrong multiple-imputation pooling formula
  5. 2y agoadjustedCurvesRisk tables, contrast consolidation and faster bootstrapping
  6. 3y agodriveRCRAN documentation error fixed
  7. 3y agoadjustedCurvestmle and ostmle methods dropped
  8. 3y agoadjustedCurvesDependency compatibility and installation documentation
  9. 4y agodriveRGRCh38 genome build supported
  10. 4y agodriveRCancer-type-specific thresholds updated
  11. 5y agodriveRMCR coordinates converted to hg19 and the model retrained
  12. 5y agodriveRCopy number score was never being computed, model rebuilt

Frequently asked questions

What is the difference between adjustedCurves and driveR?

Both compete on the same themes — r-package — within Infra & APIs. adjustedCurves and driveR 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 driveR?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. adjustedCurves and driveR 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 driveR?

Top driveR alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "driveR alternatives" section above for the current picks, or visit /alternatives/driver for the full list with editorial commentary on each.