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Comparison · Infra & APIs

gofedf vs samplr

A side-by-side editorial comparison of gofedf and samplr — release velocity, themes, recent moves, and the top alternatives to consider.

gofedf vs samplr: at a glance

Featuregofedfsamplr
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themesgoodness-of-fit, empirical-distribution, hypothesis-testing, extensibilitycognitive-science, sampling-algorithms, mcmc, dormant
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is gofedf?

A two-test goodness-of-fit package opens itself up to any weight function

gofedf runs goodness-of-fit tests built on the empirical distribution function. Its three releases trace a short, clean arc: existence in 2023, then p-values computed from an analytical solution of the integral equation in 2024, then in 2026 a user-supplied weight function that replaces the fixed menu. Cramer-von Mises and Anderson-Darling are now two points in a family rather than the two options.

Read the full gofedf trajectory →

What is samplr?

A cognitive-science sampling package ships once, then goes quiet for eighteen months

samplr compares human performance against sampling algorithms, giving cognitive scientists the MCMC machinery to test whether people behave like samplers. Its entire public history is three releases: a 1.0.0 in August 2024, a floating-point fix eighteen seconds later, and then nothing until a February 2026 patch. The release notes are unusually thin even by CRAN standards.

Read the full samplr trajectory →

gofedf vs samplr: editorial side-by-side

G
gofedf
INFRA · APIS
0.0

A two-test goodness-of-fit package opens itself up to any weight function

◆ Current state

gofedf runs goodness-of-fit tests built on the empirical distribution function. Its three releases trace a short, clean arc: existence in 2023, then p-values computed from an analytical solution of the integral equation in 2024, then in 2026 a user-supplied weight function that replaces the fixed menu. Cramer-von Mises and Anderson-Darling are now two points in a family rather than the two options.

◆ Where it's heading

The package is generalising rather than accumulating. Each release removed a hard-coded decision: first how eigenvalues are computed, offering both the analytical route and a matrix approximation; then which weight function defines the statistic at all. The maintainer's own framing in 1.1.0 is a contrast against what earlier versions would not let you do, which is the shape of a package aiming to become a framework.

◆ Prediction

An arbitrary weight function is the extensibility point that matters for EDF tests; what it lacks is calibration guidance, since Type I error behaviour was the argued benefit of the analytical eigenvalue route. Documented recommendations or diagnostics for user-chosen weights are the natural follow-up, though the entries do not announce one.

S
samplr
INFRA · APIS
0.0

A cognitive-science sampling package ships once, then goes quiet for eighteen months

◆ Current state

samplr compares human performance against sampling algorithms, giving cognitive scientists the MCMC machinery to test whether people behave like samplers. Its entire public history is three releases: a 1.0.0 in August 2024, a floating-point fix eighteen seconds later, and then nothing until a February 2026 patch. The release notes are unusually thin even by CRAN standards.

◆ Where it's heading

The feed shows a package that shipped and stopped. The 2024 tags were both created in one sitting and say almost nothing; the 2026 release is a row-count bug in Mean_Variance() bundled with citation metadata, a dropped dependency and http-to-https link fixes — the housekeeping profile of a package being kept alive for the paper that cites it rather than actively developed.

◆ Prediction

Adding citation information to the README is usually the move of a maintainer expecting the package to be referenced rather than extended. On this cadence the next release is more likely another CRAN-hygiene patch than new algorithms; there is not enough in these notes to say otherwise.

Alternatives to gofedf and samplr

Other Infra & APIs 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 gofedf or samplr.

See all gofedf alternatives → · See all samplr alternatives →

Recent activity from gofedf and samplr

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 4mo agogofedfUser-supplied weight functions for Cramer-von Mises tests
  2. 5mo agosamplrMean_Variance() row count corrected after eighteen months
  3. 1y agogofedfp-values from an analytical eigenvalue solution
  4. 1y agosamplrFloating point comparison fix
  5. 1y agosamplrsamplr 1.0.0
  6. 2y agogofedfFirst release of the gofedf package

Frequently asked questions

What is the difference between gofedf and samplr?

They serve adjacent needs but don't currently overlap on shipped themes. gofedf and samplr 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 gofedf better than samplr?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. gofedf and samplr 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 Infra & APIs products to evaluate alongside.

What are the best alternatives to gofedf?

Top gofedf alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "gofedf alternatives" section above for the current picks, or visit /alternatives/gofedf for the full list with editorial commentary on each.

What are the best alternatives to samplr?

Top samplr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "samplr alternatives" section above for the current picks, or visit /alternatives/samplr for the full list with editorial commentary on each.