Tplyr
Tplyr made clinical summary tables explain where every number came from.
A side-by-side editorial comparison of tensorflow and tidytlg — release velocity, themes, recent moves, and the top alternatives to consider.
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.
A tables-listings-graphs package that reached CRAN and then went quiet.
tidytlg generates clinical tables, listings, and graphs from tidyverse-style pipelines, maintained under the pharmaverse organisation. All four releases in the window are from a single eight-month stretch in 2023, and their content is CRAN preparation, a logging-dependency swap, and multi-file support. Release notes are merge lists rather than described changes.
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.
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.
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.
tidytlg generates clinical tables, listings, and graphs from tidyverse-style pipelines, maintained under the pharmaverse organisation. All four releases in the window are from a single eight-month stretch in 2023, and their content is CRAN preparation, a logging-dependency swap, and multi-file support. Release notes are merge lists rather than described changes.
The visible arc is getting onto CRAN and staying installable — vignette corrections per CRAN comments, a badge, a check fix, and replacing the timber logging package with logrx. The one functional addition is multiple-file support. There has been no release since October 2023, so on this evidence the package is stable or dormant rather than actively developing.
The entries give no signal about planned work; with nothing shipped in roughly two years, the more likely next event is a maintenance release triggered by a dependency or CRAN check than a feature.
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 tensorflow or tidytlg.
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.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
See all tensorflow alternatives → · See all tidytlg alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package, maintenance — within Analytics. tensorflow and tidytlg 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. tensorflow and tidytlg 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 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.
Top tidytlg alternatives in Analytics are ranked by recent ship velocity. Browse the "tidytlg alternatives" section above for the current picks, or visit /alternatives/tidytlg for the full list with editorial commentary on each.