cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
A side-by-side editorial comparison of dcurves and ddpcr — release velocity, themes, recent moves, and the top alternatives to consider.
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.
A decade-old droplet PCR analysis package woken up for one compatibility release
ddpcr reads droplet digital PCR data exported from Bio-Rad's QuantaSoft, classifies droplets and ships a Shiny interface over the analysis. It has been on CRAN since 2016 alongside an F1000Research paper. The last ten years of releases are almost entirely about keeping pace with QuantaSoft export formats and with churn in its own R dependencies.
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.
ddpcr reads droplet digital PCR data exported from Bio-Rad's QuantaSoft, classifies droplets and ships a Shiny interface over the analysis. It has been on CRAN since 2016 alongside an F1000Research paper. The last ten years of releases are almost entirely about keeping pace with QuantaSoft export formats and with churn in its own R dependencies.
This is a maintained-not-developed package, and the release history shows it plainly: a burst of real work through 2016 and 2017, then long silences broken by releases whose stated purpose is staying on CRAN. The 2026 release fits the same shape but does more than the 2023 pair did, adding support for a QuantaSoft variant and finally retiring dplyr code written against a tidy evaluation style that has been outdated for years.
Nothing in the entries points to new analysis capability; the pattern suggests the package surfaces again only when a QuantaSoft export change or a dependency deprecation forces it.
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 dcurves or ddpcr.
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
Standardised coefficients for models where standardising everything is wrong — but the feed only links out
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
See all dcurves alternatives → · See all ddpcr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. dcurves and ddpcr 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. dcurves and ddpcr 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 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.
Top ddpcr alternatives in Analytics are ranked by recent ship velocity. Browse the "ddpcr alternatives" section above for the current picks, or visit /alternatives/ddpcr for the full list with editorial commentary on each.