tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
Comparison · Analytics
DoseFinding vs tensorflow
A side-by-side editorial comparison of DoseFinding and tensorflow — release velocity, themes, recent moves, and the top alternatives to consider.
DoseFinding vs tensorflow: at a glance
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.
What is tensorflow?
The R binding to TensorFlow now spends nearly every release on install plumbing.
The R tensorflow package is a thin binding whose release notes have, for several years, been dominated by one problem: getting a working Python TensorFlow onto the user's machine. Recent releases hand that job progressively to reticulate — 2.20.0 adds py_require_tensorflow(), which makes the long-standing install_tensorflow() call unnecessary in most cases. The remaining content is version-default bumps, GPU detection fixes, and compatibility work against NumPy 2.0 and R-devel.
DoseFinding vs tensorflow: editorial side-by-side
DoseFinding
0.0ANALYTICS
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.
tensorflow
0.0ANALYTICS
The R binding to TensorFlow now spends nearly every release on install plumbing.
◆ Current state
The R tensorflow package is a thin binding whose release notes have, for several years, been dominated by one problem: getting a working Python TensorFlow onto the user's machine. Recent releases hand that job progressively to reticulate — 2.20.0 adds py_require_tensorflow(), which makes the long-standing install_tensorflow() call unnecessary in most cases. The remaining content is version-default bumps, GPU detection fixes, and compatibility work against NumPy 2.0 and R-devel.
◆ Where it's heading
Two arcs run through these entries. The first is dependency resolution moving from imperative (call install_tensorflow(), which builds a venv and pip-installs CUDA) to declarative (declare the requirement, let reticulate resolve it). The second is the quiet handover of the modelling layer: 2.16.0 switched the suggested high-level package from keras to keras3, leaving this package as the low-level tensor and installer surface rather than the place users spend their time.
◆ Prediction
The next release will most likely track a TensorFlow version bump plus whatever reticulate's requirement-resolution API changes, and continue trimming install_tensorflow()'s responsibilities. The entries give no indication of new modelling capability landing here rather than in keras3.
Alternatives to DoseFinding and tensorflow
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 tensorflow.
Tplyr
Tplyr made clinical summary tables explain where every number came from.
rlistings
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
hoardr
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
riem
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all DoseFinding alternatives → · See all tensorflow alternatives →
Recent activity from DoseFinding and tensorflow
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 11mo agotensorflowpy_require_tensorflow() replaces manual install_tensorflow()
- 1y agoDoseFindingConditional and predictive power for interim analyses
- 1y agoDoseFindingModel averaging arrives for dose-response fitting ⚡
- 1y agoDoseFindingPackage moves to openpharma under new maintainership
- 2y agotensorflowSuggests keras3 over keras; auto-installs CUDA on Linux
- 2y agotensorflowTracks TensorFlow 2.15 and newer reticulate
- 2y agoDoseFindingCompliance update for R-devel
- 2y agotensorflowInstalls the CUDA runtime itself; only the driver is manual
- 3y agotensorflowInstalls into a dedicated r-tensorflow environment by default
- 3y agoDoseFindingpowMCTBinCount bug fix
- 3y agotensorflowR doubles now convert to float64 tensors, not float32
Frequently asked questions
What is the difference between DoseFinding and tensorflow?
They serve adjacent needs but don't currently overlap on shipped themes. DoseFinding and tensorflow 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.
Is DoseFinding better than tensorflow?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. DoseFinding and tensorflow 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.
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 tensorflow?
Top tensorflow alternatives in Analytics are ranked by recent ship velocity. Browse the "tensorflow alternatives" section above for the current picks, or visit /alternatives/tensorflow for the full list with editorial commentary on each.