nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Comparison · Analytics
discretefdr vs reliagrowr
A side-by-side editorial comparison of discretefdr and reliagrowr — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
discretefdr vs reliagrowr: 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 reliagrowr?
A reliability growth package put its models behind an MCP server for AI assistants to call.
ReliaGrowR fits reliability growth models to failure data — Crow-AMSAA and Duane, with maximum likelihood estimation, confidence bounds, prediction, and reliability demonstration test planning. The last year widened it well past growth curves into repairable systems: parametric non-homogeneous Poisson process fitting with automatic change point detection, non-parametric mean cumulative function estimation, and system exposure calculation. The most recent release adds goodness-of-fit statistics and exposes the package's functions as Model Context Protocol tools.
discretefdr vs reliagrowr: 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.
reliagrowr
0.0ANALYTICS
A reliability growth package put its models behind an MCP server for AI assistants to call.
◆ Current state
ReliaGrowR fits reliability growth models to failure data — Crow-AMSAA and Duane, with maximum likelihood estimation, confidence bounds, prediction, and reliability demonstration test planning. The last year widened it well past growth curves into repairable systems: parametric non-homogeneous Poisson process fitting with automatic change point detection, non-parametric mean cumulative function estimation, and system exposure calculation. The most recent release adds goodness-of-fit statistics and exposes the package's functions as Model Context Protocol tools.
◆ Where it's heading
Two arcs run in parallel. The statistical one is a steady march from plotting a growth curve to modelling recurrent failures properly — segmented NHPP models that detect their own change points, Nelson-Aalen estimation, Cramér-von Mises and Kolmogorov-Smirnov statistics for judging the fits. The interface one is newer and more unusual: the package now ships an MCP server, and its sibling plotting package followed with one two weeks later, so this is a deliberate direction across the maintainer's reliability suite rather than a single experiment. Naming and S3 conventions were cleaned up early, which is what made a uniform tool surface plausible later.
◆ Prediction
Given the sibling packages moved to MCP within weeks of each other, the remaining tools in the suite are the obvious next candidates; on the statistical side, goodness-of-fit having just arrived suggests model comparison and selection helpers are the natural follow-on.
Alternatives to discretefdr and reliagrowr
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 reliagrowr.
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 reliagrowr alternatives →
Recent activity from discretefdr and reliagrowr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agoreliagrowrReliability growth models exposed as MCP tools ⚡
- 3mo agodiscretefdrDeprecated internal calls replaced
- 4mo agoreliagrowrRepairable systems analysis arrives: NHPP, MCF, exposure
- 4mo agoreliagrowrMaximum likelihood fitting and failure simulation
- 8mo agoreliagrowrReliaGrowR 0.3.2
- 9mo agoreliagrowrMore plotting and printing options for RGA and Duane models
- 10mo agoreliagrowrS3 methods replace the ad hoc plotting functions
- 1y agodiscretefdrDiscrete Benjamini-Yekutieli procedure added
- 1y agodiscretefdrDatasets split out and step-up procedures sped up
Frequently asked questions
What is the difference between discretefdr and reliagrowr?
Both compete on the same themes — r-package — within Analytics. discretefdr and reliagrowr 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 reliagrowr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. discretefdr and reliagrowr 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 reliagrowr?
Top reliagrowr alternatives in Analytics are ranked by recent ship velocity. Browse the "reliagrowr alternatives" section above for the current picks, or visit /alternatives/reliagrowr for the full list with editorial commentary on each.