tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of excluder and posteriordb — release velocity, themes, recent moves, and the top alternatives to consider.
A Qualtrics data-cleaning package that has been in maintenance mode since its CRAN acceptance.
excluder marks, checks, and excludes online-survey rows that fail quality criteria — duplicate responses, suspicious IP or geolocation, screen resolution, completion duration, preview rows. The mark_*/check_*/exclude_* verb trio and the column-renaming helpers are the whole public surface. Recent releases are dependency chasing and test robustness rather than new exclusion criteria.
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.
excluder marks, checks, and excludes online-survey rows that fail quality criteria — duplicate responses, suspicious IP or geolocation, screen resolution, completion duration, preview rows. The mark_*/check_*/exclude_* verb trio and the column-renaming helpers are the whole public surface. Recent releases are dependency chasing and test robustness rather than new exclusion criteria.
The package is stable and its maintenance load comes from things it does not control: the {iptools} package leaving CRAN, {tidyselect} deprecating the .data pronoun, IP-geolocation tests breaking when the underlying address data shifts. Much of that work is about staying installable, not about better exclusions. Note that several of these entries were backfilled into the feed within the same two-minute window and are not in version order.
The next release will most likely be another dependency or CRAN-check response rather than a new exclusion criterion, following the pattern of the last three.
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.
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 excluder or posteriordb.
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 excluder alternatives → · See all posteriordb alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. excluder and posteriordb 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. excluder and posteriordb 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 excluder alternatives in Analytics are ranked by recent ship velocity. Browse the "excluder alternatives" section above for the current picks, or visit /alternatives/excluder for the full list with editorial commentary on each.
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.