massProps
A mass-properties rollup spends a year on documentation and follows its sibling's API
A side-by-side editorial comparison of rollupTree and tf — release velocity, themes, recent moves, and the top alternatives to consider.
The recursive-computation engine under massProps grows the accessors its consumer needed
rollupTree performs recursive computations over tree and DAG structures — the generic engine that its author's massProps package uses to roll mass properties up an assembly breakdown. It is small and moves slowly: five releases in a year, of which two are README and vignette work. The current surface added row-level get and set accessors by key and by id at 0.4.0.
tf gave functional data a second dimension: curves whose values are vectors.
tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.
rollupTree performs recursive computations over tree and DAG structures — the generic engine that its author's massProps package uses to roll mass properties up an assembly breakdown. It is small and moves slowly: five releases in a year, of which two are README and vignette work. The current surface added row-level get and set accessors by key and by id at 0.4.0.
The package develops in response to its one visible consumer. The 0.4.0 accessors appeared in January 2026 and massProps switched to them thirteen days later; 0.4.1 then fixed missing column names in the setters, which is the kind of defect only real use surfaces. Before that, 0.3.0's default_validate_dag() extended validation past strict trees to directed acyclic graphs, widening what structures the engine will accept.
On the established pattern the next release will be whatever massProps needs next, discovered by using it. A DAG validator suggests non-tree structures are in scope, but nothing in these notes says that path is being pushed further.
tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.
The package is widening what a functional observation can be, then porting the toolkit onto it. Registration arrived first in 0.4.0 for univariate curves and immediately gained an srvf_mv method for aligning components jointly, and tfb_mfpc() ports principal component analysis to the multivariate case with a single set of scores shared across components. Alongside that runs steady dependency shedding — mvtnorm and pracma both replaced by inlined samplers that reproduce prior draws bit-for-bit, glue dropped for cli in the previous release — and an unusually long tail of NA-handling and edge-case fixes, several caught in pre-release review of the new classes.
The new classes ship with FPCA, registration and shape alignment but the release notes describe tidyfun::tf_unnest() as the consumer of one new export, so the visible next step is the rest of the tidyfun stack catching up to vector-valued columns. Expect follow-up patches on the vctrs casting paths, which is where most of this release's late fixes clustered.
Other Infra & APIs 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 rollupTree or tf.
A mass-properties rollup spends a year on documentation and follows its sibling's API
Six months of releases and not one of them touched the scoring models
A cognitive-science sampling package ships once, then goes quiet for eighteen months
A Bayesian volatility sampler in its maintenance decade, paying for its own speed
A black-box interpreter reaches CRAN, then learns multi-class and survival responses
Spatial thinning grows a result object, and the API breaks to make room for it
See all rollupTree alternatives → · See all tf alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. rollupTree and tf 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. rollupTree and tf 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 Infra & APIs products to evaluate alongside.
Top rollupTree alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "rollupTree alternatives" section above for the current picks, or visit /alternatives/rolluptree for the full list with editorial commentary on each.
Top tf alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tf alternatives" section above for the current picks, or visit /alternatives/tf for the full list with editorial commentary on each.