mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of BAS and mLLMCelltype — release velocity, themes, recent moves, and the top alternatives to consider.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
BAS performs Bayesian variable selection and model averaging for linear and generalized linear models, sampling from a model space too large to enumerate. The visible history splits cleanly: 2023-2024 added sampling machinery — an adaptive independent MCMC sampler with Horvitz-Thompson estimation, hereditary-constraint counting — while 2.0.0 in late 2025 reworked how sampler output is allocated in C. The 2.0.2 patch is a PROTECT fix for rchk warnings.
Consensus cell-type annotation that keeps adding LLM providers, and keeps fixing how they fail.
mLLMCelltype annotates scRNA-seq clusters by polling several LLMs and reconciling their answers into a consensus label, shipping as paired R and Python packages. The 2.0 line has settled into a rhythm: broaden the provider roster, then harden the parsing and retry paths that decide whether a given provider's answer survives into the consensus. Version 2.0.8 is pure reliability work, disabling DeepSeek V4's thinking mode because it exhausted the response budget before labels were returned, and raising non-streaming timeouts to 120 seconds.
BAS performs Bayesian variable selection and model averaging for linear and generalized linear models, sampling from a model space too large to enumerate. The visible history splits cleanly: 2023-2024 added sampling machinery — an adaptive independent MCMC sampler with Horvitz-Thompson estimation, hereditary-constraint counting — while 2.0.0 in late 2025 reworked how sampler output is allocated in C. The 2.0.2 patch is a PROTECT fix for rchk warnings.
Memory is the binding constraint and the releases say so directly. The hereditary-constraint counter, the GROW option, and the replacement of over-allocation with resizing all attack the same problem: n.models is a guess, and guessing high wastes memory on problems where few unique models are actually visited. The 2.0.0 work was additionally forced by R tightening its C API against non-API calls like SETLENGTH, a constraint every C-heavy CRAN package has been absorbing. Method development has been quiet since 1.7.x.
The 1.7.5 notes call the hereditary-constraint counting a first step and say future updates will cover other constraint types, including polynomials, which remain unhandled. That is the one concrete commitment in this history, though nothing since has returned to it.
mLLMCelltype annotates scRNA-seq clusters by polling several LLMs and reconciling their answers into a consensus label, shipping as paired R and Python packages. The 2.0 line has settled into a rhythm: broaden the provider roster, then harden the parsing and retry paths that decide whether a given provider's answer survives into the consensus. Version 2.0.8 is pure reliability work, disabling DeepSeek V4's thinking mode because it exhausted the response budget before labels were returned, and raising non-streaming timeouts to 120 seconds.
The centre of gravity has moved from adding models to defending against them. Recent notes read as a catalogue of ways an LLM response can be malformed: numbered lists, preamble headers, annotation-internal colons, a mid-list Unknown, thinking blocks that precede the answer, rate limits returned as HTTP 200 with an error buried in the body. Each of those could previously shift or drop a cluster's annotation, which for a consensus tool is the failure that matters most. Provider additions now land as routine catalogue growth rather than a change in what the package can do.
Expect the next release to continue the reliability arc with more provider-specific timeout and parsing guards, and a CRAN publication of 2.0.8 to close the gap the notes themselves flag. Whether return_reasoning grows from an option into the default per-cluster evidence record is the open question these entries do not yet answer.
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 BAS or mLLMCelltype.
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.
tidyplots keeps rebuilding its own foundations rather than layering around them.
A GENCODE annotation toolkit spent its first year getting out of CRAN's way.
See all BAS alternatives → · See all mLLMCelltype alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. mLLMCelltype is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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. mLLMCelltype is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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 BAS alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "BAS alternatives" section above for the current picks, or visit /alternatives/bas for the full list with editorial commentary on each.
Top mLLMCelltype alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "mLLMCelltype alternatives" section above for the current picks, or visit /alternatives/mllmcelltype for the full list with editorial commentary on each.