tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of posteriordb and tinkr — 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.
Markdown round-tripping through XML, where every release is another thing it learned not to mangle.
tinkr parses Markdown into XML, lets callers manipulate it with XPath, and writes it back out — the yarn R6 class is the whole interface. Its development is defined by a single hard problem: surviving the round trip without corrupting syntax the XML representation does not natively model. Maintenance is shared between two active contributors and release cadence is roughly annual.
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.
tinkr parses Markdown into XML, lets callers manipulate it with XPath, and writes it back out — the yarn R6 class is the whole interface. Its development is defined by a single hard problem: surviving the round trip without corrupting syntax the XML representation does not natively model. Maintenance is shared between two active contributors and release cadence is roughly annual.
Each release extends the set of constructs that get protected across the round trip — curly braces, escaped brackets, inline math, dollar signs used as currency, French-style dollars, fenced divs, frontmatter. The 0.3.0 notes also add get_protected(), append_md(), and prepend_md(), which is the first sign of building a manipulation API on top of the protection machinery rather than only widening it.
Expect the protection list to keep growing toward full Quarto syntax coverage, which the first release named as the long-term goal and which fenced divs and frontmatter both move toward.
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 tinkr.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
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.
See all posteriordb alternatives → · See all tinkr 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 tinkr 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 tinkr 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 tinkr alternatives in Analytics are ranked by recent ship velocity. Browse the "tinkr alternatives" section above for the current picks, or visit /alternatives/tinkr for the full list with editorial commentary on each.