rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of prova and stochvol — release velocity, themes, recent moves, and the top alternatives to consider.
prova adds expected-utility calculation on top of its Bayesian inference core.
prova does Bayesian nonparametric inference in R — probabilities through Pr() and qPr(), mutual information, quantile plots. Five releases in about two weeks renamed its central argument, collapsed two plotting functions into one, and then in v2.3.0 introduced exputility() for expected utilities and their revisability, with plot() and print() methods attached from the start.
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.
prova does Bayesian nonparametric inference in R — probabilities through Pr() and qPr(), mutual information, quantile plots. Five releases in about two weeks renamed its central argument, collapsed two plotting functions into one, and then in v2.3.0 introduced exputility() for expected utilities and their revisability, with plot() and print() methods attached from the start.
Two arcs run in parallel. One compresses the API: learnt= became K=, flexiplot() and plotquantiles() merged into pplot(), and omitting arguments such as Y=, X= and K= got simpler. The other extends reach — mutualinfoF() for finite-domain variates, quantile accuracy reported alongside mutual information, and now a decision-theoretic layer sitting on the inference the package already did.
exputility() shipping with print() and plot() methods matches how the probability and mutual-information classes were treated, so utilities are likely to get the same class-based handling as they mature. The notes do not say whether decision analysis extends beyond expected utility.
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 prova or stochvol.
The recursive-computation engine under massProps grows the accessors its consumer needed
A mass-properties rollup spends a year on documentation and follows its sibling's API
Six months of releases and not one of them touched the scoring models
A cognitive-science sampling package ships once, then goes quiet for eighteen months
A black-box interpreter reaches CRAN, then learns multi-class and survival responses
Spatial thinning grows a result object, and the API breaks to make room for it
See all prova alternatives → · See all stochvol alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — bayesian-inference — within Infra & APIs. prova is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. 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. prova is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
Top prova alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "prova alternatives" section above for the current picks, or visit /alternatives/prova 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.