afcharts
UK government chart styling in ggplot2, chasing ggplot2 v4 and stretching its palette to five.
A side-by-side editorial comparison of coga and mLLMCelltype — release velocity, themes, recent moves, and the top alternatives to consider.
A gamma-convolution density package that reached completion in 2018 and has coasted since.
coga computes densities, distribution functions and random numbers for convolutions of gamma distributions, with the numerical work in C++ through Rcpp. It has been feature-complete since 1.0.0 in 2018, and every release in the seven years since has been maintenance: a documentation alias for CRAN, a compiler warning, a maintainer email change, and an Rcpp update requiring Rf_error calls to be guarded. The one functional addition in that period, in 1.1.0, was an unexported function added explicitly for research use.
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.
coga computes densities, distribution functions and random numbers for convolutions of gamma distributions, with the numerical work in C++ through Rcpp. It has been feature-complete since 1.0.0 in 2018, and every release in the seven years since has been maintenance: a documentation alias for CRAN, a compiler warning, a maintainer email change, and an Rcpp update requiring Rf_error calls to be guarded. The one functional addition in that period, in 1.1.0, was an unexported function added explicitly for research use.
This is a finished package being kept alive rather than developed. The releases track external pressure exactly: CRAN documentation requirements, compiler warnings, Rcpp API changes. Its maintenance is visibly shared with smam, the same maintainer's animal-movement package, which received the same email update, the same format-security fix and the same Rcpp guard within a minute or twenty of coga each time. Neither package is being extended; both are being kept installable.
Expect nothing but CRAN and toolchain maintenance, arriving whenever Rcpp or R's check requirements change, and arriving alongside smam. There is no signal in these entries of planned functional work.
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 coga or mLLMCelltype.
UK government chart styling in ggplot2, chasing ggplot2 v4 and stretching its palette to five.
Animal-movement models in R, where new stochastic processes arrive years apart.
A basic DNA and RNA sequence toolkit that went quiet for three years, then jumped to 2.0.
Package citation for R documents, quietly growing to meet Quarto.
Accessible government spreadsheets in R, rebuilt on openxlsx2 and renamed along the way.
Automated scale shortening with lavaan, spending its releases repairing its own search algorithms.
See all coga 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 coga alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "coga alternatives" section above for the current picks, or visit /alternatives/coga 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.