mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of stochvol and tidyprompt — release velocity, themes, recent moves, and the top alternatives to consider.
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.
An R prompting framework hands its provider plumbing to ellmer and inherits MCP tools
tidyprompt composes LLM prompts out of stackable 'prompt wraps' — answer_as_json(), answer_as_category(), answer_using_tools() — and validates what comes back. Its recent history is one decision: stop maintaining a provider layer. llm_provider_ellmer() arrived experimental, then became the path through which structured output, tool calling and streaming are done natively. The newest release adds dataframe and numeric extraction wraps and lets send_prompt() take an ellmer chat object directly.
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.
tidyprompt composes LLM prompts out of stackable 'prompt wraps' — answer_as_json(), answer_as_category(), answer_using_tools() — and validates what comes back. Its recent history is one decision: stop maintaining a provider layer. llm_provider_ellmer() arrived experimental, then became the path through which structured output, tool calling and streaming are done natively. The newest release adds dataframe and numeric extraction wraps and lets send_prompt() take an ellmer chat object directly.
Two lines run together. One is catalogue growth — every release adds a wrap for another answer shape. The other is consolidation onto ellmer, and that is where the leverage is: because ellmer tool definitions are what mcptools::mcp_tools() returns, tidyprompt gained access to Model Context Protocol servers without writing an MCP client. Its own Gemini provider is already marked superseded. Note the feed's stamps lie — 0.1.0, 0.2.0 and 0.3.0 were all published within two hours of each other in reverse version order.
The remaining first-party providers are the obvious next thing to fold in: the Gemini one is already superseded, and the Ollama and OpenAI providers carry the same duplicated plumbing. Expect the wrap catalogue to keep growing on top of an increasingly ellmer-only base.
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 stochvol or tidyprompt.
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 stochvol alternatives → · See all tidyprompt alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. stochvol and tidyprompt 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. stochvol and tidyprompt 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 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.
Top tidyprompt alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tidyprompt alternatives" section above for the current picks, or visit /alternatives/tidyprompt for the full list with editorial commentary on each.