cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
A side-by-side editorial comparison of betaselectr and dcurves — release velocity, themes, recent moves, and the top alternatives to consider.
Standardised coefficients for models where standardising everything is wrong — but the feed only links out
betaselectr computes standardised coefficients selectively, for models where blanket standardisation misleads — interaction terms, categorical predictors and moderated effects, where standardising the product term or a dummy variable produces a number that does not mean what readers assume. It has been on CRAN since November 2024 across three releases. What those releases contain cannot be determined from this feed.
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.
betaselectr computes standardised coefficients selectively, for models where blanket standardisation misleads — interaction terms, categorical predictors and moderated effects, where standardising the product term or a dummy variable produces a number that does not mean what readers assume. It has been on CRAN since November 2024 across three releases. What those releases contain cannot be determined from this feed.
This changelog carries no release content. Every entry is a pointer to the CRAN page and to a changelog hosted on the package's own site, so the direction of development is not readable from what is published here. What the version numbers alone support is a package that reached CRAN in late 2024 and has issued two patch releases since, at roughly six-month intervals, without a minor version bump.
No prediction is supportable from these entries; the feed would need to carry actual release notes, or the package's own site would need to be read directly, before its direction could be called.
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.
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.
Nothing in these entries points to method or API work; expect the next release to be another compatibility or CRAN documentation patch.
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 betaselectr or dcurves.
A choice-based IRT model published once in 2019 and kept compiling ever since
The R engine behind MetaboAnalyst closes the gap from raw spectra to biological interpretation
Rebuilding SAS's formatting layer in R, one format specification at a time
Stream-network spatial models learning to run on data that no longer fits in memory
Bioconductor's installer, frozen at 1.30.x and tuned almost entirely through environment variables
The config-authoring half of hubverse, pinned to whatever the schema is doing this quarter
See all betaselectr alternatives → · See all dcurves alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. betaselectr 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. betaselectr 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.
Top betaselectr alternatives in Analytics are ranked by recent ship velocity. Browse the "betaselectr alternatives" section above for the current picks, or visit /alternatives/betaselectr for the full list with editorial commentary on each.
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.