mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of gofedf and stochvol — 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.
A Bayesian volatility sampler in its maintenance decade, paying for its own speed
stochvol runs MCMC for stochastic volatility models, with a C++ sampler underneath an R interface. Five years of releases in this window contain no new models: the work is compiler and dependency compatibility, CRAN check notes, and a steady trickle of corrections to the sampler itself. Its methodological milestone, the Journal of Statistical Software paper, is recorded in a 2021 tag.
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.
stochvol runs MCMC for stochastic volatility models, with a C++ sampler underneath an R interface. Five years of releases in this window contain no new models: the work is compiler and dependency compatibility, CRAN check notes, and a steady trickle of corrections to the sampler itself. Its methodological milestone, the Journal of Statistical Software paper, is recorded in a 2021 tag.
This is what a finished computational package looks like. The formula interface arrived at 3.1.0 and nothing has been added since; what changes is the ground underneath — RcppArmadillo major versions, UBSan checks, error-handling conventions moving from Rf_error to Rcpp::stop for correct memory management. The recurring pattern worth watching is that several releases fix real errors in the sampler's proposal distributions, found by users and by CRAN's own instrumented checks rather than by the maintainer.
Nothing in these notes suggests new methodology. Expect the next release when RcppArmadillo or a CRAN check flavour forces one, and treat any bug report against the samplers as the more consequential event.
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 stochvol.
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 stochvol 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 stochvol 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 stochvol 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 stochvol alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "stochvol alternatives" section above for the current picks, or visit /alternatives/stochvol for the full list with editorial commentary on each.