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Comparison · Analytics

DoseFinding vs tabnet

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

DoseFinding vs tabnet: at a glance

FeatureDoseFindingtabnet
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesdose-response, mcp-mod, clinical-trials, model-averagingtabular-deep-learning, torch, tidymodels, parsnip
Last editorial update1h ago3h ago
WebsiteVisit →Visit →

What is DoseFinding?

New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing

DoseFinding implements MCP-Mod and related dose-response methodology for clinical trial design and analysis. In 2024 it changed hands — Marius Thomas took over as maintainer, Novartis was recorded as copyright holder and funder, and the package moved to the openpharma GitHub organisation with roxygen documentation and a proper NEWS file. The two releases since have added substantive methodology: model averaging for dose-response fitting in 1.3-1, and conditional and predictive power for interim analyses in 1.4-1.

Read the full DoseFinding 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 →

DoseFinding vs tabnet: editorial side-by-side

D
DoseFinding
ANALYTICS
0.0

New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing

◆ Current state

DoseFinding implements MCP-Mod and related dose-response methodology for clinical trial design and analysis. In 2024 it changed hands — Marius Thomas took over as maintainer, Novartis was recorded as copyright holder and funder, and the package moved to the openpharma GitHub organisation with roxygen documentation and a proper NEWS file. The two releases since have added substantive methodology: model averaging for dose-response fitting in 1.3-1, and conditional and predictive power for interim analyses in 1.4-1.

◆ Where it's heading

The pattern before the handover was maintenance — R-devel compliance, a bug fix, a link. After it, each release carries a named methodological addition with an acknowledged contributor, plus documentation to match: a longitudinal analysis vignette shipped alongside the interim power work. Housekeeping continues underneath, mostly clearing deprecated ggplot2 interfaces, aes_string in one release and qplot in the next.

◆ Prediction

Given the last two releases each added one method with a supporting vignette, expect the next to follow the same shape. Both additions so far extend the package beyond fixed dose-response fitting, so adaptive and interim methodology is the more likely direction.

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 DoseFinding 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 DoseFinding or tabnet.

See all DoseFinding alternatives → · See all tabnet alternatives →

Recent activity from DoseFinding 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 agoDoseFindingConditional and predictive power for interim analyses
  5. 1y agotabnetBugfix release for R 4.5 and dials tuning
  6. 1y agoDoseFindingModel averaging arrives for dose-response fitting
  7. 1y agoDoseFindingPackage moves to openpharma under new maintainership
  8. 2y agotabnetCase weights and warm-start parameters via parsnip
  9. 2y agotabnetHierarchical multi-label classification via data.tree
  10. 2y agoDoseFindingCompliance update for R-devel
  11. 3y agoDoseFindingpowMCTBinCount bug fix

Frequently asked questions

What is the difference between DoseFinding 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 DoseFinding 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 DoseFinding?

Top DoseFinding alternatives in Analytics are ranked by recent ship velocity. Browse the "DoseFinding alternatives" section above for the current picks, or visit /alternatives/dosefinding 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.