tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of orbital and webmockr — release velocity, themes, recent moves, and the top alternatives to consider.
Turning fitted tidymodels into SQL, one model family at a time — and the boosting engines just landed.
orbital converts a fitted tidymodels workflow into a database expression so prediction runs where the data lives, no R session in the loop. Its value is entirely a function of coverage, and 0.5.0 was the largest coverage release yet: catboost and lightgbm boosted trees, rpart decision trees, earth-backed MARS, glmnet multinomial regression, and both randomForest and ranger random forests, all for numeric, class, and probability predictions. The 0.5.1 follow-up is corrective, fixing SQL that Snowflake and other engines rejected because it cast booleans directly to numeric.
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
webmockr intercepts HTTP requests in R tests and returns stubbed responses, and since 1.0.0 it covers all three clients that matter — httr, httr2 and crul. The releases through 2025 have been about shedding dependencies: internal R6 classes unexported in 2.1.0, crul demoted from Imports to Suggests in the same release, and the mutual dependency with vcr severed in 2.2.0. The package now installs and runs without pulling in the rest of the rOpenSci HTTP stack.
orbital converts a fitted tidymodels workflow into a database expression so prediction runs where the data lives, no R session in the loop. Its value is entirely a function of coverage, and 0.5.0 was the largest coverage release yet: catboost and lightgbm boosted trees, rpart decision trees, earth-backed MARS, glmnet multinomial regression, and both randomForest and ranger random forests, all for numeric, class, and probability predictions. The 0.5.1 follow-up is corrective, fixing SQL that Snowflake and other engines rejected because it cast booleans directly to numeric.
The package has been working outward in rings: recipe preprocessing steps first, then model types, then post-processing via the tailor package in 0.4.0, with show_query() added so users can inspect what actually gets sent. Recent releases show the constraint shifting from R-side translation to SQL dialect compatibility — the bugs now are about what a specific database will accept, not whether a model can be expressed. estimate_orbital_size() in 0.5.1 acknowledges the other practical limit, since generated expressions can grow large enough to matter before you generate them.
With the major boosting and ensemble engines covered, expect the next releases to keep chasing dialect-specific SQL correctness across warehouses rather than adding model families.
webmockr intercepts HTTP requests in R tests and returns stubbed responses, and since 1.0.0 it covers all three clients that matter — httr, httr2 and crul. The releases through 2025 have been about shedding dependencies: internal R6 classes unexported in 2.1.0, crul demoted from Imports to Suggests in the same release, and the mutual dependency with vcr severed in 2.2.0. The package now installs and runs without pulling in the rest of the rOpenSci HTTP stack.
Two arcs run in sequence. The first is coverage — multiple queued responses in 0.7.0, basic auth mocking, async through crul, then httr2 — building out what can be stubbed. The second, starting with 2.0.0, is correctness and independence: stubs are now deleted if an error occurs mid-construction rather than lingering half-built, partial matching arrives for bodies and queries, and the dependency graph is pruned release by release. The 2.2.0 split from vcr landed within a minute of crul's release taking mocking control into its own clients.
RequestPattern is documented as still exported in 2.1.0 but slated for removal, so the next major release is where that lands. Expect continued dependency pruning rather than new client support — the three clients that exist are already covered.
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 orbital or webmockr.
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 orbital alternatives → · See all webmockr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. orbital and webmockr 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. orbital and webmockr 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 orbital alternatives in Analytics are ranked by recent ship velocity. Browse the "orbital alternatives" section above for the current picks, or visit /alternatives/orbital for the full list with editorial commentary on each.
Top webmockr alternatives in Analytics are ranked by recent ship velocity. Browse the "webmockr alternatives" section above for the current picks, or visit /alternatives/webmockr for the full list with editorial commentary on each.