r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
Comparison · Analytics
cloudml vs RBesT
A side-by-side editorial comparison of cloudml and RBesT — release velocity, themes, recent moves, and the top alternatives to consider.
cloudml vs RBesT: at a glance
What is cloudml?
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.
What is RBesT?
RBesT is teaching its Bayesian decision rules to answer two-sided questions.
RBesT builds meta-analytic-predictive priors — the machinery for borrowing historical control data into a new trial — and evaluates the operating characteristics of decisions made with them. The stable line has spent several releases on effective sample size: ESS for normal mixtures via a new `family` argument, boundary corrections when no responses or no non-responses are observed, and stabilised ELIR computations. The 1.9-0 release candidate extends the normal, binomial and Poisson outcome functions to two-sided decisions.
cloudml vs RBesT: editorial side-by-side
cloudml
0.0ANALYTICS
Six years since the last functional change, and Google renamed the service it wraps in the release before that
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
RBesT
0.0ANALYTICS
RBesT is teaching its Bayesian decision rules to answer two-sided questions.
◆ Current state
RBesT builds meta-analytic-predictive priors — the machinery for borrowing historical control data into a new trial — and evaluates the operating characteristics of decisions made with them. The stable line has spent several releases on effective sample size: ESS for normal mixtures via a new `family` argument, boundary corrections when no responses or no non-responses are observed, and stabilised ELIR computations. The 1.9-0 release candidate extends the normal, binomial and Poisson outcome functions to two-sided decisions.
◆ Where it's heading
Two currents run through the changelog. One is ESS hardening — nearly every release since 1.7-4 fixes another edge case where the ELIR calculation aborted or returned something unstable, which is what happens when a quantity used to justify prior strength to regulators gets scrutinised. The other is Stan and brms integration debt: array syntax updates, a minimum Stan version bump, truncated prior generation for `mixstanvar`, deterministic EM. The RC's contributor list shows a second active maintainer, and the work is broader than any recent stable release.
◆ Prediction
The release candidate covers all three outcome families and has already absorbed a round of review comments, so the next step is most likely the 1.9-0 CRAN release itself rather than further feature work.
Alternatives to cloudml and RBesT
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 RBesT.
DoseFinding
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
webmockr
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
crul
crul took mocking back from webmockr and made it a property of the client itself
dendroNetwork
Six releases, six identical bodies — the feed carries the package abstract instead of release notes
chattr
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
See all cloudml alternatives → · See all RBesT alternatives →
Recent activity from cloudml and RBesT
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 5mo agoRBesTTwo-sided decisions across normal, binomial and Poisson outcomes ⚡
- 0y agocloudmlDocumentation updated for CRAN
- 1y agoRBesTJSON read and write for mixture objects
- 1y agoRBesTess() fixed inside apply functions
- 1y agoRBesTESS for normal mixtures in the exponential family
- 1y agoRBesTTruncated mixture priors for brms, plus faster Stan models
- 2y agoRBesTStan array syntax update and CRAN system requirements
- 6y agocloudmlai-platform command adopted; custom pre-install commands added
- 7y agocloudmlDefault runtime moves to TensorFlow 1.9
- 8y agocloudmlPatch for CRAN results and a packrat error
- 8y agocloudmlCloud training, GPU jobs, tuning and deployment from R
Frequently asked questions
What is the difference between cloudml and RBesT?
They serve adjacent needs but don't currently overlap on shipped themes. cloudml and RBesT 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 cloudml better than RBesT?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cloudml and RBesT 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 cloudml?
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.
What are the best alternatives to RBesT?
Top RBesT alternatives in Analytics are ranked by recent ship velocity. Browse the "RBesT alternatives" section above for the current picks, or visit /alternatives/rbest for the full list with editorial commentary on each.