cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
A side-by-side editorial comparison of ddpcr and SSN2 — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Stream-network spatial models learning to run on data that no longer fits in memory
SSN2 fits spatial statistical models on stream networks, where covariance follows flow-connected distance along the network rather than straight-line distance. It is the maintained successor to the original SSN package, published through JOSS in 2024, and leans on spmodel for its underlying model machinery. Recent releases have concentrated on the constraint that binds this class of model hardest: the distance matrix.
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support for a QuantaSoft variant and finally retiring dplyr code written against a tidy evaluation style that has been outdated for years.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.
SSN2 fits spatial statistical models on stream networks, where covariance follows flow-connected distance along the network rather than straight-line distance. It is the maintained successor to the original SSN package, published through JOSS in 2024, and leans on spmodel for its underlying model machinery. Recent releases have concentrated on the constraint that binds this class of model hardest: the distance matrix.
The first year was about establishing credibility and interoperability — a JOSS review, geopackage import support, deprecation of the SSN-to-SSN2 bridge, marginal means through emmeans. The 2025 releases turn to scale, moving distance matrices onto disk via filematrix and routing estimation and prediction through the local approximation. The 0.4.0 default change is the visible consequence: the neighbourhood size rises from 100 to 200, buying accuracy now that the surrounding machinery can afford it.
With the large-data path established and its default just retuned, the next work most likely tightens that approximation further or extends it to the model classes the local argument does not yet cover.
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 ddpcr or SSN2.
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
Bioconductor's installer, frozen at 1.30.x and tuned almost entirely through environment variables
Decision curve analysis, settled since 2022 and now moving only when its neighbours do
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ddpcr and SSN2 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. ddpcr and SSN2 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 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.
Top SSN2 alternatives in Analytics are ranked by recent ship velocity. Browse the "SSN2 alternatives" section above for the current picks, or visit /alternatives/ssn2 for the full list with editorial commentary on each.