mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of gofedf and writeAlizer — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Six months of releases and not one of them touched the scoring models
writeAlizer generates predicted writing-quality scores from features produced by Coh-Metrix, ReaderBench and GAMET, downloading its trained scoring models on demand. Every release in this window — nine of them between September 2025 and February 2026 — is about that download path rather than the scoring: classed error conditions, checksum verification, an offline mode, a mockable artifact directory, and dependency reporting for the model families a user actually invokes.
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.
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.
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.
writeAlizer generates predicted writing-quality scores from features produced by Coh-Metrix, ReaderBench and GAMET, downloading its trained scoring models on demand. Every release in this window — nine of them between September 2025 and February 2026 — is about that download path rather than the scoring: classed error conditions, checksum verification, an offline mode, a mockable artifact directory, and dependency reporting for the model families a user actually invokes.
The package is being made safe to distribute. CRAN's policy on packages that reach the internet drove the first wave — graceful failure, tests that preflight their URLs and skip, examples seeded from a local mock model — and 1.7.0 turned the accumulated fixes into structure with named error classes for each failure mode. Only 1.7.2 adds anything a user would ask for: filename handling for Coh-Metrix and GAMET outputs that arrive as paths.
With the artifact registry hardened and documented, the pressure that produced nine releases in six months should ease, and attention can return to the models themselves — the vignette on scoring-model development added in 1.7.2 hints at that. Nothing here promises new models.
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 writeAlizer.
mice can finally predict, not just estimate, from multiply imputed data.
A market-microstructure toolkit that keeps adding estimators as the papers land.
A vowel-analysis package trimming dependencies after an email address got it archived.
The R half of the EMU speech database system, fixing what was quietly broken.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
See all gofedf alternatives → · See all writeAlizer alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. gofedf and writeAlizer 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. gofedf and writeAlizer 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.
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.
Top writeAlizer alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "writeAlizer alternatives" section above for the current picks, or visit /alternatives/writealizer for the full list with editorial commentary on each.