tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of rlistings and tensorflow — release velocity, themes, recent moves, and the top alternatives to consider.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
rlistings renders clinical-trial subject listings and paginates them for regulatory output, sitting alongside rtables on the shared formatters engine. The releases in this window are dominated by pagination correctness: repeated key columns across pages, splitting by a variable, ordered-factor handling, column gaps, and font metrics. Development is a large rotating contributor set inside the insightsengineering organisation.
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.
rlistings renders clinical-trial subject listings and paginates them for regulatory output, sitting alongside rtables on the shared formatters engine. The releases in this window are dominated by pagination correctness: repeated key columns across pages, splitting by a variable, ordered-factor handling, column gaps, and font metrics. Development is a large rotating contributor set inside the insightsengineering organisation.
The package is progressively delegating pagination to formatters rather than implementing it — paginate_listing() was refactored to call formatters' paginate_to_mpfs() directly, and truetype font support arrived through a new formatters API. That reduces duplicated logic but ties the package's page-break behaviour to a dependency it shares with rtables. Feature work beyond pagination is thin: better error messages for unsupported column classes, a cheatsheet.
Expect pagination fidelity to remain the focus, with changes arriving as formatters exposes more of its layout machinery rather than as rlistings-native features.
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.
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 rlistings or tensorflow.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
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 rlistings alternatives → · See all tensorflow alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. rlistings 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rlistings 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.
Top rlistings alternatives in Analytics are ranked by recent ship velocity. Browse the "rlistings alternatives" section above for the current picks, or visit /alternatives/rlistings for the full list with editorial commentary on each.
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.