tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of posteriordb and Tplyr — release velocity, themes, recent moves, and the top alternatives to consider.
A reference posterior database that hit 1.0 with a paper, and is now graded on the statistics it ships.
posteriordb distributes Bayesian models with data and reference posterior draws so inference algorithms can be benchmarked against a common target. It reached 1.0.0 alongside a published paper, and ships both R and Python access. Recent work is about the metadata around the draws — licences, machine-readable dataset descriptors, and additional summary statistics.
Tplyr made clinical summary tables explain where every number came from.
Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.
posteriordb distributes Bayesian models with data and reference posterior draws so inference algorithms can be benchmarked against a common target. It reached 1.0.0 alongside a published paper, and ships both R and Python access. Recent work is about the metadata around the draws — licences, machine-readable dataset descriptors, and additional summary statistics.
The database is maturing from a model collection into a citable benchmark asset: licence information per model, a Croissant metadata file for dataset discovery, and summary statistics like mean squared value and lag-1 autocorrelation that let users judge whether reference draws are good enough for their comparison. Earlier releases were about content and correctness; current ones are about making the content machine-readable and verifiable.
Further work should continue on draw-quality diagnostics and metadata rather than model count, since the last two releases both added ways to assess the reference draws instead of adding posteriors.
Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.
Post-1.0 work has been about completing the metadata story and filling gaps in layer composition rather than adding table types — metadata for missing subjects, add_anti_join(), missing-subject rows, data limiting, and fixes to nested count layers where an inner value appears under several outer groups. Releases cluster tightly after a major version, then go quiet, and the window ends with a patch issued days after the release it corrects.
Further releases will most likely continue closing traceability and nested-layer edge cases rather than introducing new layer types, following the pattern of both post-1.0 feature releases.
Other Analytics 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 posteriordb or Tplyr.
A tables-listings-graphs package that reached CRAN and then went quiet.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
See all posteriordb alternatives → · See all Tplyr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. posteriordb and Tplyr 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. posteriordb and Tplyr 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 Analytics products to evaluate alongside.
Top posteriordb alternatives in Analytics are ranked by recent ship velocity. Browse the "posteriordb alternatives" section above for the current picks, or visit /alternatives/posteriordb for the full list with editorial commentary on each.
Top Tplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "Tplyr alternatives" section above for the current picks, or visit /alternatives/tplyr for the full list with editorial commentary on each.