r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
Comparison · Analytics
bundle vs rbmi
A side-by-side editorial comparison of bundle and rbmi — release velocity, themes, recent moves, and the top alternatives to consider.
bundle vs rbmi: at a glance
What is bundle?
Four releases in three years, each one teaching the serializer about a model type it couldn't carry
bundle solves a narrow, real problem: many R model objects hold pointers to external state — compiled boosters, Java handles, torch tensors — that do not survive being saved and reloaded in another session. It wraps them so they do. The package has shipped four releases since 2022, and the shape of each is the same: extend coverage to another model class, or repair coverage that an upstream release broke.
What is rbmi?
Reference-based multiple imputation for trials, now shipping without Bayesian support by default.
rbmi implements reference-based multiple imputation for longitudinal clinical trial data with missing values — the estimand machinery regulators expect for handling intercurrent events and dropout. The consequential recent change was 1.3.0 moving rstan from a hard dependency to Suggests, which takes Bayesian imputation out of the default install. Since then the work has been documentation and nomenclature discipline: 1.6.1 standardized on MNAR over a mixed NMAR/MNAR vocabulary and deprecated the nmar.rm argument accordingly.
bundle vs rbmi: editorial side-by-side
bundle
0.0ANALYTICS
Four releases in three years, each one teaching the serializer about a model type it couldn't carry
◆ Current state
bundle solves a narrow, real problem: many R model objects hold pointers to external state — compiled boosters, Java handles, torch tensors — that do not survive being saved and reloaded in another session. It wraps them so they do. The package has shipped four releases since 2022, and the shape of each is the same: extend coverage to another model class, or repair coverage that an upstream release broke.
◆ Where it's heading
Coverage is the product, so the release cadence is set by the ecosystem rather than by a roadmap. dbarts arrived in 0.1.2, along with extra work to preserve xgboost's nfeatures and feature_names through a round trip; 0.1.3 exists because xgboost changed its model format again. The 0.1.1 fix — recipes steps nested inside workflows — points at the same underlying issue one level up, where the object needing bundling is buried inside a tidymodels pipeline rather than passed directly.
◆ Prediction
Expect the next release to follow the same trigger: either a new parsnip engine that carries external pointers, or another upstream format change in one of the engines already covered. xgboost has now forced two of the four releases.
rbmi
2.5ANALYTICS
Reference-based multiple imputation for trials, now shipping without Bayesian support by default.
◆ Current state
rbmi implements reference-based multiple imputation for longitudinal clinical trial data with missing values — the estimand machinery regulators expect for handling intercurrent events and dropout. The consequential recent change was 1.3.0 moving rstan from a hard dependency to Suggests, which takes Bayesian imputation out of the default install. Since then the work has been documentation and nomenclature discipline: 1.6.1 standardized on MNAR over a mixed NMAR/MNAR vocabulary and deprecated the nmar.rm argument accordingly.
◆ Where it's heading
The package is optimizing for adoption friction over feature breadth. Dropping a compiled Stan dependency from the default install, deprecating a bespoke seed argument in favor of base set.seed(), and aligning lsmeans() behavior and weight naming with emmeans all point the same direction — behave like a conventional R package rather than a specialized one. Documentation work in 1.6.1 covering @return on every exported function and executable examples reads as preparation for validation scrutiny rather than user demand.
◆ Prediction
Given the FAQ vignette's validation statement and the recent documentation completeness pass, the next work is more likely qualification and estimand documentation than new imputation methods.
Alternatives to bundle and rbmi
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 bundle or rbmi.
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
Recent activity from bundle and rbmi
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 22d agorbmiMNAR nomenclature standardized, documentation completed
- 8mo agobundlexgboost bundling updated for newer model versions
- 1y agobundledbarts BART models become bundleable
- 1y agorbmirstan demoted to Suggests, Bayesian imputation now opt-in ⚡
- 2y agorbmirbmi v1.2.5
- 2y agobundleRecipes steps inside workflows now bundle correctly
- 3y agobundleFirst CRAN release
Frequently asked questions
What is the difference between bundle and rbmi?
They serve adjacent needs but don't currently overlap on shipped themes. rbmi is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is bundle better than rbmi?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rbmi is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to bundle?
Top bundle alternatives in Analytics are ranked by recent ship velocity. Browse the "bundle alternatives" section above for the current picks, or visit /alternatives/bundle for the full list with editorial commentary on each.
What are the best alternatives to rbmi?
Top rbmi alternatives in Analytics are ranked by recent ship velocity. Browse the "rbmi alternatives" section above for the current picks, or visit /alternatives/rbmi for the full list with editorial commentary on each.