exametrika
A test-theory package that grew into a graphical-model toolkit, now spending its releases paying down the API debt that growth created.
A side-by-side editorial comparison of cocoon and dqcheckr — release velocity, themes, recent moves, and the top alternatives to consider.
A statistics-formatting helper in maintenance mode, tracking R-devel one fix at a time
cocoon formats statistical output for manuscripts, converting model and test objects into publication-ready strings. Its surface settled early: format_stats() is a generic that dispatches on object class, introduced in 0.1.0 to supersede the earlier format_corr() and format_ttest(), and extended in 0.2.0 to cover aov, lm, glm and the lme4 and lmerTest mixed-model families. The two releases since have been single-issue compatibility fixes against changes to wilcox.test() in R-devel.
dqcheckr adds drift analysis, then removes the YAML a user had to hand-write.
dqcheckr runs configurable data-quality checks over files and DuckDB tables, driven by YAML dataset configs and recording results as snapshots. The 0.2.0 release added the ability to compare two historical snapshots and report per-column statistical drift, schema changes and trend charts, extending the tool from point-in-time checking into change over time. The most recent tag, 0.3.0, attacks the other friction point by generating the config itself from a sniff pass over the data.
cocoon formats statistical output for manuscripts, converting model and test objects into publication-ready strings. Its surface settled early: format_stats() is a generic that dispatches on object class, introduced in 0.1.0 to supersede the earlier format_corr() and format_ttest(), and extended in 0.2.0 to cover aov, lm, glm and the lme4 and lmerTest mixed-model families. The two releases since have been single-issue compatibility fixes against changes to wilcox.test() in R-devel.
The package reached feature completeness for its stated job quickly and has been in maintenance since early 2025. Both 0.2.1 and 0.3.1 address the same upstream moving part - how wilcox.test() computes exact versus asymptotic distributions in development versions of R - which is the shape of a package whose own code is stable and whose risk lives entirely in what it wraps. Nothing in the recent entries points at new statistical object types.
Further releases are likely to stay reactive, triggered by R-devel or dependency changes rather than by new formatting methods, unless a specific model class is requested.
dqcheckr runs configurable data-quality checks over files and DuckDB tables, driven by YAML dataset configs and recording results as snapshots. The 0.2.0 release added the ability to compare two historical snapshots and report per-column statistical drift, schema changes and trend charts, extending the tool from point-in-time checking into change over time. The most recent tag, 0.3.0, attacks the other friction point by generating the config itself from a sniff pass over the data.
Both moves point the same way: reduce what the operator has to write and know. Config generation removes the hand-authored YAML that gated first use, list_runs() and validate_config() make an existing setup inspectable, and the snapshot comparison turns accumulated run history into a second product surface. Check coverage keeps widening underneath — outlier detection, composite keys, row-count and file-size ceilings — and the reporting layer moved from rmarkdown to Quarto, with existing 0.1.x databases auto-migrated on first run.
Expect the generated configs and the drift reports to converge, so a sniffed config can seed thresholds from the snapshot history rather than from defaults, plus continued growth in the numbered QC check catalogue.
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 cocoon or dqcheckr.
A test-theory package that grew into a graphical-model toolkit, now spending its releases paying down the API debt that growth created.
nuggets keeps compounding on the 2.0 rewrite — more pattern families, lighter install.
projoint spent a year on CRAN paperwork, then shipped a correctness fix it flagged itself.
eratosthenes spends 0.1.0 hardening inputs rather than adding chronology methods.
An actuarial mainstay spends its releases on CI plumbing, not on new mathematics.
EDAForge is a data-quality auditor renamed mid-flight, still finding its CRAN footing.
See all cocoon alternatives → · See all dqcheckr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. dqcheckr 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. dqcheckr 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 cocoon alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "cocoon alternatives" section above for the current picks, or visit /alternatives/cocoon for the full list with editorial commentary on each.
Top dqcheckr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "dqcheckr alternatives" section above for the current picks, or visit /alternatives/dqcheckr for the full list with editorial commentary on each.