mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of anticlust and stochvol — release velocity, themes, recent moves, and the top alternatives to consider.
Anticlustering keeps absorbing constraints — must-link, cannot-link, blocks, missing data
anticlust partitions a set of items into groups that are as similar to each other as possible — assembling matched stimulus sets, balanced experimental conditions, comparable teaching groups. The current surface is one function, anticlustering(), with a growing list of things it will honour: must-link and cannot-link constraints, categorical variables as factors, NAs, blocking by a categorical level, and a choice among exact solvers and heuristics.
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.
anticlust partitions a set of items into groups that are as similar to each other as possible — assembling matched stimulus sets, balanced experimental conditions, comparable teaching groups. The current surface is one function, anticlustering(), with a growing list of things it will honour: must-link and cannot-link constraints, categorical variables as factors, NAs, blocking by a categorical level, and a choice among exact solvers and heuristics.
Two years of releases have gone almost entirely into constraint handling and input tolerance rather than new objectives. The methods list grew by absorbing outside work — the three-phase search of Yang et al. contributed by an external author, a 2PML heuristic for must-link problems, a Gurobi backend for the exact formulations. Meanwhile categories_to_binary() has been rewritten twice, which is where the recent bugs have come from.
The constraint types now interact combinatorially — blocks, must-link, cannot-link, missing values and each objective — and the last two releases were both regressions in the encoding layer underneath them. Consolidating that layer is the more likely next move than another solver.
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 anticlust 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 anticlust 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. anticlust 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. anticlust 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 anticlust alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "anticlust alternatives" section above for the current picks, or visit /alternatives/anticlust 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.