nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
A side-by-side editorial comparison of detectseparation and discretefdr — release velocity, themes, recent moves, and the top alternatives to consider.
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
detectseparation identifies separation and infinite estimates in binomial-response GLMs — the condition where maximum likelihood estimates diverge and standard software reports large coefficients with enormous standard errors instead of an error. Version 0.3 was the structural turn: detect_infinite_estimates() became the general method covering log, logit, probit and cauchit links, with detect_separation() demoted to a wrapper around it. Version 0.4 in April 2026 adds the ability to distinguish complete from quasi-complete separation via separation_type.
The discrete-data FDR package is being pared into one piece of a larger multiple-testing suite.
DiscreteFDR implements false discovery rate procedures adapted for discrete test statistics, where the standard continuous-case corrections are conservative. It now covers a discrete Benjamini-Yekutieli procedure alongside the Benjamini-Hochberg variants it started with, including adaptive versions. Its datasets and test-result classes have been moved out into companion packages, so it increasingly does one job and defers the rest.
detectseparation identifies separation and infinite estimates in binomial-response GLMs — the condition where maximum likelihood estimates diverge and standard software reports large coefficients with enormous standard errors instead of an error. Version 0.3 was the structural turn: detect_infinite_estimates() became the general method covering log, logit, probit and cauchit links, with detect_separation() demoted to a wrapper around it. Version 0.4 in April 2026 adds the ability to distinguish complete from quasi-complete separation via separation_type.
The package has been generalizing steadily — first past its own framing, since separation is one case of infinite estimates rather than the whole problem, and now toward finer classification of what it detects. The distinction 0.4 adds is practically useful because complete and quasi-complete separation call for different responses. Release intervals are long, roughly two to four years, which fits a diagnostic tool whose underlying theory is settled.
With link coverage broad and separation now classified by type, further work is more likely to refine reporting than to extend detection to new model families.
DiscreteFDR implements false discovery rate procedures adapted for discrete test statistics, where the standard continuous-case corrections are conservative. It now covers a discrete Benjamini-Yekutieli procedure alongside the Benjamini-Hochberg variants it started with, including adaptive versions. Its datasets and test-result classes have been moved out into companion packages, so it increasingly does one job and defers the rest.
The direction is decomposition into a suite. The amnesia dataset went to DiscreteDatasets, summary output now interoperates with the DiscreteTestResults class from DiscreteTests, and match.pvals() stopped being exported — each release trims something that belongs elsewhere. What remains gets methodological additions at a slow, deliberate cadence, with performance work on the step-up procedures that dominate cost when the number of tests is large. Recent activity is maintenance: replacing deprecated calls the package still made of its own siblings. This is a mature statistical package whose release notes are short because the methods underneath them are settled.
Expect further alignment with the companion packages rather than new procedures, since the last substantive release was already about interoperating with DiscreteTests classes and the most recent one about clearing deprecations.
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 detectseparation or discretefdr.
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
See all detectseparation alternatives → · See all discretefdr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. detectseparation and discretefdr 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. detectseparation and discretefdr 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 detectseparation alternatives in Analytics are ranked by recent ship velocity. Browse the "detectseparation alternatives" section above for the current picks, or visit /alternatives/detectseparation for the full list with editorial commentary on each.
Top discretefdr alternatives in Analytics are ranked by recent ship velocity. Browse the "discretefdr alternatives" section above for the current picks, or visit /alternatives/discretefdr for the full list with editorial commentary on each.