ggdist
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
Comparison · Infra & APIs
adjustedCurves vs cvms
A side-by-side editorial comparison of adjustedCurves and cvms — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
adjustedCurves vs cvms: 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 cvms?
A cross-validation package whose real development has moved to its plotting function
cvms runs repeated cross-validation over model formulas and reports comparable metrics. The 2.0.0 release was a breaking correctness fix: every function accepting fold_cols mismatched training and testing data when fold indices were non-sequential, did not start at 1, or were strings, because the iteration index was compared against the raw fold value rather than its factor level index. 2.0.1 restored coefficient extraction for nnet::multinom and mixed models by supplying an environment containing the training data, and followed lme4's move of findbars() into the reformulas package.
adjustedCurves vs cvms: 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.
cvms
0.0INFRA · APIS
A cross-validation package whose real development has moved to its plotting function
◆ Current state
cvms runs repeated cross-validation over model formulas and reports comparable metrics. The 2.0.0 release was a breaking correctness fix: every function accepting fold_cols mismatched training and testing data when fold indices were non-sequential, did not start at 1, or were strings, because the iteration index was compared against the raw fold value rather than its factor level index. 2.0.1 restored coefficient extraction for nnet::multinom and mixed models by supplying an environment containing the training data, and followed lme4's move of findbars() into the reformulas package.
◆ Where it's heading
Two threads run in parallel and only one is about cross-validation. The plotting function plot_confusion_matrix() has absorbed most feature work since 1.5.0 - custom gradient palettes, intensity limits, per-tile settings, dynamic font colors keyed to value thresholds, and arguments that accept functions rather than constants - to the point where a companion web application exists for using it without code. The cross-validation core, by contrast, sees maintenance: upstream compatibility fixes for pROC, ggnewscale and ggplot2, and the fold-matching correction that finally forced a major version.
◆ Prediction
Expect continued option growth in the confusion matrix plotting surface, since that is where nearly every release since 1.5.0 has spent its changes, with core cross-validation changes arriving only as upstream packages force them.
Alternatives to adjustedCurves and cvms
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finnts
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BayLum
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daedalus
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See all adjustedCurves alternatives → · See all cvms alternatives →
Recent activity from adjustedCurves and cvms
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agocvmsCoefficient extraction restored for multinom and mixed models
- 6mo agoadjustedCurvesOff-by-one-step error corrected in cumulative incidence estimates
- 9mo agocvmsBreaking fix for mismatched folds with non-sequential fold IDs
- 11mo agocvmsTest compatibility with pROC 1.19 and a deprecation warning fix
- 1y agoadjustedCurvesIPTW curves can now extend to the last observed time
- 1y agocvmsConfusion matrix fonts and colors can now be computed from the values
- 1y agocvmsTile intensity by row or column percentages
- 1y agocvmsMultinom coefficient extraction fix after a parameters update
- 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 cvms?
Both compete on the same themes — r-package — within Infra & APIs. adjustedCurves and cvms 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 cvms?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. adjustedCurves and cvms 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 cvms?
Top cvms alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "cvms alternatives" section above for the current picks, or visit /alternatives/cvms for the full list with editorial commentary on each.