nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Comparison · Analytics
discretefdr vs rATTAINS
A side-by-side editorial comparison of discretefdr and rATTAINS — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
discretefdr vs rATTAINS: 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 rATTAINS?
The R client for EPA water quality data spent two releases undoing its own promises about data shape.
rATTAINS wraps the EPA's ATTAINS API, which holds state water quality assessments and impaired-waters listings. The package reached 1.0.0 by promising stable, consistently rectangled return structures, then walked that promise back in 1.1.0 when it dropped the dependency doing the rectangling. As of 1.2.0 it also requires an API key, because ATTAINS itself began requiring one in May 2026.
discretefdr vs rATTAINS: 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.
rATTAINS
0.0ANALYTICS
The R client for EPA water quality data spent two releases undoing its own promises about data shape.
◆ Current state
rATTAINS wraps the EPA's ATTAINS API, which holds state water quality assessments and impaired-waters listings. The package reached 1.0.0 by promising stable, consistently rectangled return structures, then walked that promise back in 1.1.0 when it dropped the dependency doing the rectangling. As of 1.2.0 it also requires an API key, because ATTAINS itself began requiring one in May 2026.
◆ Where it's heading
The direction is toward a thinner, lower-maintenance wrapper. Caching went in 0.1.4 when hoardr was archived, tidyjson and janitor went earlier, tibblify went in 1.1.0, and each removal handed a little more data-shaping responsibility back to the user — the current advice is to pass .unnest = FALSE and rectangle the results with whatever tidying package you prefer. Release cadence is slow and mostly reactive: upstream API terms, archived dependencies, and compatibility with test tooling account for most of the log. The package's centre of gravity is staying installable and honest about what ATTAINS returns rather than smoothing it over.
◆ Prediction
Given the pattern, the next release is likelier to be a compatibility or upstream-driven fix than new endpoint coverage; how the API key requirement affects users in scripted and CI contexts is the obvious open question the entries do not yet answer.
Alternatives to discretefdr and rATTAINS
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 rATTAINS.
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 rATTAINS alternatives →
Recent activity from discretefdr and rATTAINS
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agorATTAINSATTAINS now requires an API key, and the package follows
- 3mo agodiscretefdrDeprecated internal calls replaced
- 8mo agorATTAINSThe tibblify dependency goes, and with it the stable data shapes ⚡
- 1y agorATTAINSTest suite updated for vcr v2
- 1y agodiscretefdrDiscrete Benjamini-Yekutieli procedure added
- 1y agodiscretefdrDatasets split out and step-up procedures sped up
- 3y agorATTAINS1.0.0 commits to stable return structures via tibblify
- 3y agorATTAINSCaching removed after hoardr was archived
- 4y agorATTAINSRequests retry on timeout, with offline detection
Frequently asked questions
What is the difference between discretefdr and rATTAINS?
Both compete on the same themes — r-package — within Analytics. discretefdr and rATTAINS 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 rATTAINS?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. discretefdr and rATTAINS 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 rATTAINS?
Top rATTAINS alternatives in Analytics are ranked by recent ship velocity. Browse the "rATTAINS alternatives" section above for the current picks, or visit /alternatives/rattains for the full list with editorial commentary on each.