cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
Comparison · Analytics
BORG vs dcurves
A side-by-side editorial comparison of BORG and dcurves — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
BORG vs dcurves: at a glance
What is BORG?
A cross-validation guard that refuses to run random CV on dependent data unless you insist
BORG detects spatial, temporal and clustered dependence in a modelling dataset and generates a cross-validation scheme that respects it — spatial blocks, temporal blocks, group folds — rather than letting random splits leak information between train and test. Its distinguishing choice is enforcement: when it finds dependence, random CV is blocked outright and needs an explicit allow_random=TRUE to proceed. The package also wraps the standard rsample and caret entry points so the guard applies inside existing workflows.
What is dcurves?
Decision curve analysis, settled since 2022 and now moving only when its neighbours do
dcurves implements decision curve analysis — evaluating a prediction model or diagnostic test by net benefit across the range of thresholds a clinician might plausibly use, rather than by a single discrimination statistic. Its API stabilised in 2022 around dca() and test_consequences(). The two releases since exist because gtsummary and CRAN documentation rules changed, not because the method did.
BORG vs dcurves: editorial side-by-side
BORG
0.0ANALYTICS
A cross-validation guard that refuses to run random CV on dependent data unless you insist
◆ Current state
BORG detects spatial, temporal and clustered dependence in a modelling dataset and generates a cross-validation scheme that respects it — spatial blocks, temporal blocks, group folds — rather than letting random splits leak information between train and test. Its distinguishing choice is enforcement: when it finds dependence, random CV is blocked outright and needs an explicit allow_random=TRUE to proceed. The package also wraps the standard rsample and caret entry points so the guard applies inside existing workflows.
◆ Where it's heading
The entire visible history is a single day, and the sequence within it is coherent rather than churn: enforcement first, then the evidence layer, then framework integration, then idiomatic R polish. The evidence work matters to the pitch — borg_compare_cv() runs random against blocked CV so users see the inflation on their own data instead of taking the warning on faith, and the methods-text and certificate exports are aimed squarely at getting this into published papers. By the final release the interface has been rebuilt on standard S3 plot and summary methods.
◆ Prediction
The wrappers so far cover rsample and caret; tidymodels and mlr3 are the obvious remaining entry points if the guard is to reach the workflows it hasn't yet intercepted.
dcurves
0.0ANALYTICS
Decision curve analysis, settled since 2022 and now moving only when its neighbours do
◆ Current state
dcurves implements decision curve analysis — evaluating a prediction model or diagnostic test by net benefit across the range of thresholds a clinician might plausibly use, rather than by a single discrimination statistic. Its API stabilised in 2022 around dca() and test_consequences(). The two releases since exist because gtsummary and CRAN documentation rules changed, not because the method did.
◆ Where it's heading
The package reached its intended scope quickly and then stopped. Its 2022 releases did the substantive work: adding threshold-level diagnostic accuracy, tightening argument validation, and taking one breaking change to make net-interventions-avoided plots show the treat-all and treat-none reference lines by default. Since then it has moved only as a dependent of the wider tidy-modelling documentation ecosystem it plugs into.
◆ Prediction
Nothing in these entries points to method or API work; expect the next release to be another compatibility or CRAN documentation patch.
Alternatives to BORG and dcurves
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 BORG or dcurves.
MetaboAnalystR
The R engine behind MetaboAnalyst closes the gap from raw spectra to biological interpretation
fmtr
Rebuilding SAS's formatting layer in R, one format specification at a time
betaselectr
Standardised coefficients for models where standardising everything is wrong — but the feed only links out
SSN2
Stream-network spatial models learning to run on data that no longer fits in memory
BiocManager
Bioconductor's installer, frozen at 1.30.x and tuned almost entirely through environment variables
See all BORG alternatives → · See all dcurves alternatives →
Recent activity from BORG and dcurves
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 7mo agoBORGInterface rebuilt on standard S3 plot and summary methods
- 7mo agoBORGGuarded wrappers for rsample and caret splitting functions
- 7mo agoBORGEmpirical inflation comparison and publication-ready reporting
- 7mo agoBORGRandom CV blocked by default when dependence is detected ⚡
- 7mo agoBORGVersion bump to 0.1.1
- 9mo agodcurvesHTML5 documentation rebuild for CRAN
- 2y agodcurvesbroom.helpers added to Suggests for gtsummary 2.0
- 3y agodcurvesNet interventions plots now show treat-all and treat-none by default
- 4y agodcurvestest_consequences() reports accuracy across thresholds
Frequently asked questions
What is the difference between BORG and dcurves?
Both compete on the same themes — r-package — within Analytics. BORG and dcurves 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 BORG better than dcurves?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. BORG and dcurves 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 Analytics products to evaluate alongside.
What are the best alternatives to BORG?
Top BORG alternatives in Analytics are ranked by recent ship velocity. Browse the "BORG alternatives" section above for the current picks, or visit /alternatives/borg for the full list with editorial commentary on each.
What are the best alternatives to dcurves?
Top dcurves alternatives in Analytics are ranked by recent ship velocity. Browse the "dcurves alternatives" section above for the current picks, or visit /alternatives/dcurves for the full list with editorial commentary on each.