mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of midr and tidyprompt — release velocity, themes, recent moves, and the top alternatives to consider.
A black-box interpreter reaches CRAN, then learns multi-class and survival responses
midr explains black-box models by fitting an interpretable surrogate through Maximum Interpretation Decomposition — main effects plus second-order interactions, with exact Shapley values for the surrogate. Two months after its first CRAN release it can take a matrix response, which covers multi-class classification and survival models, and hold collections of fitted interpretations in midlist and midrib objects for comparison.
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.
midr explains black-box models by fitting an interpretable surrogate through Maximum Interpretation Decomposition — main effects plus second-order interactions, with exact Shapley values for the surrogate. Two months after its first CRAN release it can take a matrix response, which covers multi-class classification and survival models, and hold collections of fitted interpretations in midlist and midrib objects for comparison.
The releases move outward along two axes at once: what can be interpreted, and how much of it fits in memory. Version 0.5.3 rebuilt the fitting path to avoid materialising large design matrices and added a save.memory option; 0.6.0 widened the response from a vector to a matrix and added parametric link functions. Class and argument names were shortened in the same release, so the package is still willing to break itself this early.
With multiple models now held in one object and visualisation methods for them, comparison across models is the surface most likely to fill out next — the collection classes exist but the notes describe manipulation and plotting rather than any comparison metric.
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 midr 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 midr alternatives → · See all tidyprompt alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. midr 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. midr 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 midr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "midr alternatives" section above for the current picks, or visit /alternatives/midr 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.