nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Comparison · Analytics
discretefdr vs fastglm
A side-by-side editorial comparison of discretefdr and fastglm — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
discretefdr vs fastglm: at a glance
What is discretefdr?
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.
What is fastglm?
A fast GLM solver stops being one function and becomes a count-model family
fastglm ran C++ IRLS for standard generalized linear models for six years with almost no releases. In May 2026 it added three top-level model types — negative binomial with jointly estimated dispersion, hurdle, and zero-inflated — each with the entire fitting driver in C++ rather than an R loop around a C++ kernel. The following release generalised Firth bias reduction to every standard family across dense, sparse and streaming backends.
discretefdr vs fastglm: editorial side-by-side
discretefdr
0.0ANALYTICS
The discrete-data FDR package is being pared into one piece of a larger multiple-testing suite.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
fastglm
0.0ANALYTICS
A fast GLM solver stops being one function and becomes a count-model family
◆ Current state
fastglm ran C++ IRLS for standard generalized linear models for six years with almost no releases. In May 2026 it added three top-level model types — negative binomial with jointly estimated dispersion, hurdle, and zero-inflated — each with the entire fitting driver in C++ rather than an R loop around a C++ kernel. The following release generalised Firth bias reduction to every standard family across dense, sparse and streaming backends.
◆ Where it's heading
The package changed what it is. Through 0.0.3 it was a drop-in replacement for glm() competing on speed; from 0.1.0 it targets the models people leave base R for — MASS::glm.nb, pscl::hurdle, pscl::zeroinfl — and reimplements their full estimation loops natively. The 0.1.1 follow-up is consolidation on that new surface: Firth generalised past binomial logit, SQUAREM acceleration on the zero-inflation EM driver, and a run of clamping guards and initialization fixes on the families most prone to overflow.
◆ Prediction
The numerical-stability work in 0.1.1 clusters on Tweedie and the inverse and sqrt link families, which suggests those paths are the newest and least exercised — expect further correctness fixes there before new model types.
Alternatives to discretefdr and fastglm
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 discretefdr or fastglm.
susier
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
UCell
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
detectseparation
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
brglm2
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
bayestools
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
See all discretefdr alternatives → · See all fastglm alternatives →
Recent activity from discretefdr and fastglm
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agofastglmFirth generalised to all families, plus SQUAREM and stability fixes
- 3mo agodiscretefdrDeprecated internal calls replaced
- 3mo agofastglmCRAN release 0.1.0 ⚡
- 1y agodiscretefdrDiscrete Benjamini-Yekutieli procedure added
- 1y agodiscretefdrDatasets split out and step-up procedures sped up
- 4y agofastglmC++ headers exposed for linking
- 7y agofastglmFirst CRAN release of the C++ IRLS solver
Frequently asked questions
What is the difference between discretefdr and fastglm?
Both compete on the same themes — r-package — within Analytics. discretefdr and fastglm 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 discretefdr better than fastglm?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. discretefdr and fastglm 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 discretefdr?
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.
What are the best alternatives to fastglm?
Top fastglm alternatives in Analytics are ranked by recent ship velocity. Browse the "fastglm alternatives" section above for the current picks, or visit /alternatives/fastglm for the full list with editorial commentary on each.