tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of cloudml and geotargets — 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.
Geospatial targets grew from two raster helpers into a tiling and multi-backend pipeline layer.
geotargets extends the targets pipeline framework with target factories that know how to serialise geospatial objects — terra rasters and vectors, stars arrays, raster collections, and VRT references. It completed rOpenSci review and transferred ownership during 0.3.0. Writing behaviour is now configurable through per-target arguments and package-level options.
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.
geotargets extends the targets pipeline framework with target factories that know how to serialise geospatial objects — terra rasters and vectors, stars arrays, raster collections, and VRT references. It completed rOpenSci review and transferred ownership during 0.3.0. Writing behaviour is now configurable through per-target arguments and package-level options.
The arc runs from 'targets can hold a SpatRaster' to 'targets can hold a tiled, dynamically branched raster workflow with controlled datatype and driver.' Recent work is about giving users control over how objects hit disk — datatype, driver, metadata sidecars, pass-through arguments to the underlying writers — which is where correctness problems in geospatial pipelines actually live. External contributors are driving a visible share of it.
Expect continued work on write-path fidelity and format coverage rather than new target types, since the last two releases both resolved metadata and driver defaults that were silently losing information.
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 geotargets.
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 geotargets 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 geotargets 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 geotargets 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 geotargets alternatives in Analytics are ranked by recent ship velocity. Browse the "geotargets alternatives" section above for the current picks, or visit /alternatives/geotargets for the full list with editorial commentary on each.