PurpleAir
The R client for PurpleAir sensors keeps finding its time-averaging was wrong.
Comparison · Analytics
assesslite vs discretefdr
A side-by-side editorial comparison of assesslite and discretefdr — release velocity, themes, recent moves, and the top alternatives to consider.
assesslite vs discretefdr: at a glance
What is assesslite?
Four releases in fifteen hours take causal assumption-checking from resampling to identification
AssessLite attacks the structural assumptions behind a causal finding and returns three-way verdicts — stable, unstable, or not resolvable — feeding proceed, conditional or abstain decisions, with an auditable JSON record validated against a shared schema. It runs natively in R and Python against one spec, with the Python engine reproducing R's coxph(ties=breslow) exactly. The entire 0.1.0-through-0.4.0 arc landed inside a single day in July 2026.
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.
assesslite vs discretefdr: editorial side-by-side
assesslite
0.0ANALYTICS
Four releases in fifteen hours take causal assumption-checking from resampling to identification
◆ Current state
AssessLite attacks the structural assumptions behind a causal finding and returns three-way verdicts — stable, unstable, or not resolvable — feeding proceed, conditional or abstain decisions, with an auditable JSON record validated against a shared schema. It runs natively in R and Python against one spec, with the Python engine reproducing R's coxph(ties=breslow) exactly. The entire 0.1.0-through-0.4.0 arc landed inside a single day in July 2026.
◆ Where it's heading
The releases are cumulative, each restating the previous feature set and adding to it, so read them as one launch rather than four. The direction across that launch is clear: it started with resampling attacks (permutation, holdout, temporal split, subgroup), turned toward causal identification with declared DAGs and the backdoor criterion, then reached into genuinely dependent data with spatial and interference checks. The correctness work moves in step — the 0.3.0 Bonferroni adjustment fixed a holdout rule that was flagging roughly m times too often with m variants.
◆ Prediction
The project has repeatedly shipped what it previously listed as future work within days, so the next release most likely converts another declared gap rather than opening a new front.
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.
Alternatives to assesslite and discretefdr
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 assesslite or discretefdr.
piecepackr
A board game graphics package runs one of the most disciplined deprecation cycles in R.
e2tree
The explainable-ensemble-tree package now measures whether its own explanations are faithful.
contentanalysis
A scientific-text analysis package moved from counting citations to classifying argument structure.
relialearnr
The teaching arm of an R reliability suite keeps pace with whatever its analysis siblings ship.
reliaplotr
The Weibull plotting package renamed itself, then handed its charts to AI assistants.
See all assesslite alternatives → · See all discretefdr alternatives →
Recent activity from assesslite and discretefdr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoassessliteAssessLite 0.4.0
- 1mo agoassessliteAssessLite 0.3.0
- 1mo agoassessliteAssessLite 0.2.0 ⚡
- 1mo agoassessliteAssessLite 0.1.0 ⚡
- 3mo agodiscretefdrDeprecated internal calls replaced
- 1y agodiscretefdrDiscrete Benjamini-Yekutieli procedure added
- 1y agodiscretefdrDatasets split out and step-up procedures sped up
Frequently asked questions
What is the difference between assesslite and discretefdr?
They serve adjacent needs but don't currently overlap on shipped themes. assesslite 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.
Is assesslite better than discretefdr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. assesslite 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.
What are the best alternatives to assesslite?
Top assesslite alternatives in Analytics are ranked by recent ship velocity. Browse the "assesslite alternatives" section above for the current picks, or visit /alternatives/assesslite for the full list with editorial commentary on each.
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.