rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of massProps and tf — release velocity, themes, recent moves, and the top alternatives to consider.
A mass-properties rollup spends a year on documentation and follows its sibling's API
massProps computes mass, centre of mass and inertia tensors — and their uncertainties — over tree-structured assemblies, the calculation systems engineers run on a spacecraft or vehicle breakdown. It sits on top of rollupTree, the same author's generic recursive-computation engine, and its most recent release exists only to adopt accessor functions that sibling added two weeks earlier.
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.
massProps computes mass, centre of mass and inertia tensors — and their uncertainties — over tree-structured assemblies, the calculation systems engineers run on a spacecraft or vehicle breakdown. It sits on top of rollupTree, the same author's generic recursive-computation engine, and its most recent release exists only to adopt accessor functions that sibling added two weeks earlier.
Every release in the window is documentation, benchmarks, or keeping in step with rollupTree. The inertia tensor and radius-of-gyration uncertainty equations have been reformatted and re-explained three separate times, which suggests the hard part of this package is not the computation but making the covariance treatment legible to the engineers meant to trust it. Development effort clearly sits in the engine underneath, not here.
The pattern is unambiguous: rollupTree ships an API change and massProps follows within a fortnight. Whatever the engine does next is what appears here next, and on this evidence it will arrive as a one-line release note.
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 massProps or tf.
The recursive-computation engine under massProps grows the accessors its consumer needed
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 massProps 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. massProps 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. massProps 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 massProps alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "massProps alternatives" section above for the current picks, or visit /alternatives/massprops 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.