tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of brulee and crul — release velocity, themes, recent moves, and the top alternatives to consider.
tidymodels' torch backend grew from MLPs into a tabular deep learning suite with foundation models.
brulee fits neural networks for tidymodels on torch, and 1.0.0 redefined what that means: alongside the original MLP it now ships Regularization Learning Networks, ResNet with skip connections and batch normalization, AutoInt with columnwise attention, SAINT with row and column attention, and Chronos2, a foundational forecasting model. GPU acceleration arrived in the same release with automatic CUDA selection and opt-in MPS. Version 1.1.0 added TabICL, an open-source tabular foundation model, and 1.1.1 spent its time cleaning up the consequences of shipping models that need weight downloads.
crul took mocking back from webmockr and made it a property of the client itself
crul is the R6-based HTTP client underneath much of rOpenSci's package stack, covering synchronous requests, three flavours of async, pagination and retries. Its 1.6.0 release in July 2025 changed where test mocking lives: each client — HttpClient, Async, AsyncVaried — now takes a mocking parameter at initialisation or per method, and the standalone mock() function is deprecated. Mocking used to be something webmockr switched on from outside; it is now a setting on the client.
brulee fits neural networks for tidymodels on torch, and 1.0.0 redefined what that means: alongside the original MLP it now ships Regularization Learning Networks, ResNet with skip connections and batch normalization, AutoInt with columnwise attention, SAINT with row and column attention, and Chronos2, a foundational forecasting model. GPU acceleration arrived in the same release with automatic CUDA selection and opt-in MPS. Version 1.1.0 added TabICL, an open-source tabular foundation model, and 1.1.1 spent its time cleaning up the consequences of shipping models that need weight downloads.
The package has crossed from a torch convenience wrapper into a catalog of current tabular architectures, and the recent releases show it absorbing what that costs. Pretrained weights meant a 400MB download, so 1.1.1 stopped fetching them on attach and moved the cache to the platform-appropriate R_user_dir location. Numerical robustness is the other constant thread — 64-bit tensors, Gaussian initialization, gradient clipping extended architecture by architecture, and a ResNet batch-normalization bug where a single-row trailing batch produced all-NA predictions.
Gradient clipping has been rolled out one architecture at a time and TabICL is the newest arrival, so the likely next step is bringing the foundation models to parity with the trained ones on device selection, prediction types, and the tuning surface.
crul is the R6-based HTTP client underneath much of rOpenSci's package stack, covering synchronous requests, three flavours of async, pagination and retries. Its 1.6.0 release in July 2025 changed where test mocking lives: each client — HttpClient, Async, AsyncVaried — now takes a mocking parameter at initialisation or per method, and the standalone mock() function is deprecated. Mocking used to be something webmockr switched on from outside; it is now a setting on the client.
The async surface has been the growth area for years — retries reached Async, AsyncVaried, AsyncQueue and HttpRequest in 1.4, AsyncQueue gained the response accessors in 1.2, and 1.5.0 wired async requests up to webmockr. The 1.6.0 change reverses that direction of dependency, and it landed within a minute of webmockr's own release severing its tie to vcr. Read together, the rOpenSci HTTP stack is being deliberately untangled so each package can be used without the others.
With mock() deprecated rather than removed, the next major release is the likely point of deletion. Expect the remaining work to follow the same decoupling theme rather than adding request features.
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 brulee or crul.
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.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. brulee and crul 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. brulee and crul 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 brulee alternatives in Analytics are ranked by recent ship velocity. Browse the "brulee alternatives" section above for the current picks, or visit /alternatives/brulee for the full list with editorial commentary on each.
Top crul alternatives in Analytics are ranked by recent ship velocity. Browse the "crul alternatives" section above for the current picks, or visit /alternatives/crul for the full list with editorial commentary on each.