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crul vs tabnet

A side-by-side editorial comparison of crul and tabnet — release velocity, themes, recent moves, and the top alternatives to consider.

crul vs tabnet: at a glance

Featurecrultabnet
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themeshttp-client, async, mocking, ropenscitabular-deep-learning, torch, tidymodels, parsnip
Last editorial update1h ago3h ago
WebsiteVisit →Visit →

What is crul?

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.

Read the full crul trajectory →

What is tabnet?

A tabular deep-learning model in R that keeps widening what counts as a tabular task.

tabnet ports the TabNet attentive tabular architecture to R on torch, wired into tidymodels through parsnip so it slots into workflows, tuning, and case weights like any other engine. The model surface has grown well past plain supervised fitting: unsupervised pretraining, missing values in predictors, multi-outcome fitting, hierarchical multi-label classification, and built-in explainability via tabnet_explain(). The 0.9.x line has been consolidating rather than adding, with 0.9.0 finally making hierarchical classification work correctly by accounting for the ancestor matrix.

Read the full tabnet trajectory →

crul vs tabnet: editorial side-by-side

C
crul
ANALYTICS
0.0

crul took mocking back from webmockr and made it a property of the client itself

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

T
tabnet
ANALYTICS
2.5

A tabular deep-learning model in R that keeps widening what counts as a tabular task.

◆ Current state

tabnet ports the TabNet attentive tabular architecture to R on torch, wired into tidymodels through parsnip so it slots into workflows, tuning, and case weights like any other engine. The model surface has grown well past plain supervised fitting: unsupervised pretraining, missing values in predictors, multi-outcome fitting, hierarchical multi-label classification, and built-in explainability via tabnet_explain(). The 0.9.x line has been consolidating rather than adding, with 0.9.0 finally making hierarchical classification work correctly by accounting for the ancestor matrix.

◆ Where it's heading

Two threads run through the release history. The first is task surface — each minor version tends to admit a class of problem the model previously could not express, from missing data to hierarchy to imbalanced binary outcomes. The second is torch-level performance and correctness, visible in the torch_ignite_adam default that cut pretraining time roughly 30% and the fix for optimizers frozen after checkpointing on cuda and mps. Tidymodels integration is treated as a first-class obligation, with parsnip breaking changes tracked release by release.

◆ Prediction

The hierarchical path is the least finished: 0.5.0 introduced it and 0.9.0 only just made it effective, so the next releases most likely extend evaluation and explainability to hierarchical fits rather than adding another task type.

Alternatives to crul and tabnet

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 crul or tabnet.

See all crul alternatives → · See all tabnet alternatives →

Recent activity from crul and tabnet

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 21d agotabnetvip dependency moves to r-universe
  2. 2mo agotabnetHierarchical classification made effective, augment() added
  3. 6mo agotabnetentmax15 and sparsemax15 masks, AUM loss for imbalanced data
  4. 1y agocrulMocking becomes a client parameter, independent of webmockr
  5. 1y agotabnetBugfix release for R 4.5 and dials tuning
  6. 2y agocrulAsync requests become mockable through webmockr
  7. 2y agotabnetCase weights and warm-start parameters via parsnip
  8. 2y agocrulDocumentation fixes and test helper tweak
  9. 2y agotabnetHierarchical multi-label classification via data.tree
  10. 3y agocrulHTTP retries reach the async classes
  11. 3y agocrulClearer error for mismatched urls and disk lengths
  12. 4y agocrulAsyncQueue gains response accessors; results print as a summary

Frequently asked questions

What is the difference between crul and tabnet?

They serve adjacent needs but don't currently overlap on shipped themes. tabnet is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is crul better than tabnet?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tabnet is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to crul?

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.

What are the best alternatives to tabnet?

Top tabnet alternatives in Analytics are ranked by recent ship velocity. Browse the "tabnet alternatives" section above for the current picks, or visit /alternatives/tabnet for the full list with editorial commentary on each.