tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of cloudml and tern — release velocity, themes, recent moves, and the top alternatives to consider.
Six years since the last functional change, and Google renamed the service it wraps in the release before that
cloudml lets R users train keras, tfestimators and tensorflow models on Google's managed machine learning service, tune hyperparameters there, and deploy the results. Its last release with functional content was 0.6.1 in September 2019, which adapted to Google renaming the service from ml-engine to ai-platform. The only entry since is a 2025 documentation update made to satisfy CRAN.
tern is migrating its entire analysis-function catalogue off make_afun(), one release at a time.
tern builds the clinical-trial tables, listings, and graphs layer on top of rtables — occurrence counts, survival summaries, ANCOVA, incidence rates, subgroup and biomarker tabulations. The visible work across the window is a systematic refactor: dozens of analysis functions rewritten to drop make_afun() and adopt a common analysis-function style driven by rtables' additional_fun_params. Feature additions ride along with it.
cloudml lets R users train keras, tfestimators and tensorflow models on Google's managed machine learning service, tune hyperparameters there, and deploy the results. Its last release with functional content was 0.6.1 in September 2019, which adapted to Google renaming the service from ml-engine to ai-platform. The only entry since is a 2025 documentation update made to satisfy CRAN.
The visible arc is short and stops abruptly. Releases through 2018 tracked the TensorFlow runtime version and patched packaging problems; 0.6.1 added a customCommands hook so users could run OS-level setup before package installation, and adjusted to the service's new name. Then nothing for six years. A 2025 release containing only documentation changes is the standard signal of a package being kept on CRAN rather than being developed.
There is nothing in this feed to support a prediction of functional work. The most likely next event is another CRAN-driven documentation patch, or archival.
tern builds the clinical-trial tables, listings, and graphs layer on top of rtables — occurrence counts, survival summaries, ANCOVA, incidence rates, subgroup and biomarker tabulations. The visible work across the window is a systematic refactor: dozens of analysis functions rewritten to drop make_afun() and adopt a common analysis-function style driven by rtables' additional_fun_params. Feature additions ride along with it.
This is a multi-release architectural migration, not incremental polish. Each release converts another batch of functions, and the count is large — roughly two dozen in the most recent entry alone, after a comparable batch the release before. Alongside it, the denom parameter is being threaded through counting functions and g_lineplot is accumulating layout control, both patterns of standardising arguments that previously varied per function.
The refactor should continue until the make_afun() dependency is gone entirely, with the remaining tabulate_* and biomarker functions the likely next batch; the entries give no date for completion.
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 cloudml or tern.
A tables-listings-graphs package that reached CRAN and then went quiet.
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.
See all cloudml alternatives → · See all tern alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. cloudml and tern 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. cloudml and tern 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 cloudml alternatives in Analytics are ranked by recent ship velocity. Browse the "cloudml alternatives" section above for the current picks, or visit /alternatives/cloudml for the full list with editorial commentary on each.
Top tern alternatives in Analytics are ranked by recent ship velocity. Browse the "tern alternatives" section above for the current picks, or visit /alternatives/tern for the full list with editorial commentary on each.