tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
Comparison · Analytics
bundle vs datapack
A side-by-side editorial comparison of bundle and datapack — release velocity, themes, recent moves, and the top alternatives to consider.
bundle vs datapack: 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 datapack?
The DataONE bundler learned to edit packages in 2017 and has coasted on that ever since
datapack assembles heterogeneous data files and metadata into a single transportable bundle, serialised as an OAI-ORE resource map and BagIt archive, for deposit into repositories like DataONE. Its functional surface settled with the 1.3.x line, which made assembled packages editable rather than write-once. Since then the releases have been sparse and defensive: SHA-256 as the default checksum in 1.4.0, BagIt spec conformance in 1.4.1, and a 2025 patch that states outright it contains no new features.
bundle vs datapack: 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.
datapack
0.0ANALYTICS
The DataONE bundler learned to edit packages in 2017 and has coasted on that ever since
◆ Current state
datapack assembles heterogeneous data files and metadata into a single transportable bundle, serialised as an OAI-ORE resource map and BagIt archive, for deposit into repositories like DataONE. Its functional surface settled with the 1.3.x line, which made assembled packages editable rather than write-once. Since then the releases have been sparse and defensive: SHA-256 as the default checksum in 1.4.0, BagIt spec conformance in 1.4.1, and a 2025 patch that states outright it contains no new features.
◆ Where it's heading
The arc runs from assembly to correctness of the resulting archive. Later releases keep tightening the metadata the resource map must carry — dc:creator always present, dcterms:modified always updated, the package correctly flagged as modified after any access-policy change — because a bundle whose provenance record is subtly wrong is worse than one that fails outright. The three-year gap between 1.4.1 and 1.4.2, and the latter's CRAN-note content, place this package firmly in preservation.
◆ Prediction
Expect the next release, if any, to be another CRAN-compliance patch rather than functional work. The 1.4.2 note that it contains no new features is the clearest statement in the feed about where this package sits.
Alternatives to bundle and datapack
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 datapack.
Tplyr
Tplyr made clinical summary tables explain where every number came from.
rlistings
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
hoardr
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
riem
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all bundle alternatives → · See all datapack alternatives →
Recent activity from bundle and datapack
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 8mo agobundlexgboost bundling updated for newer model versions
- 10mo agodatapackCRAN documentation and CI cleanup
- 1y agobundledbarts BART models become bundleable
- 2y agobundleRecipes steps inside workflows now bundle correctly
- 3y agobundleFirst CRAN release
- 4y agodatapackBagIt serialisation brought in line with the current spec
- 5y agodatapackSHA-256 becomes the default checksum algorithm
- 6y agodatapackResource map metadata guaranteed; removeRelationships() added
- 8y agodatapackupdateMetadata no longer drops package relationships
- 9y agodatapackAssembled data packages become editable in place ⚡
Frequently asked questions
What is the difference between bundle and datapack?
They serve adjacent needs but don't currently overlap on shipped themes. bundle and datapack 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 bundle better than datapack?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bundle and datapack 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 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 datapack?
Top datapack alternatives in Analytics are ranked by recent ship velocity. Browse the "datapack alternatives" section above for the current picks, or visit /alternatives/datapack for the full list with editorial commentary on each.