rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of glcdp and tf — release velocity, themes, recent moves, and the top alternatives to consider.
glcdp reaches 1.0.0 with a stable schema contract behind its data explorer.
glcdp imports light-logger data packages published to the GLC standard, driven by declared schemas rather than format-specific readers. 0.9.3 added glc_explore(), a Shiny application for browsing the registry and exporting an annotated reproducible script. 1.0.0 promotes schema 3.0.2 to the default and primary stable import contract and makes schema-declared variable types and factor-level order drive the import itself.
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.
glcdp imports light-logger data packages published to the GLC standard, driven by declared schemas rather than format-specific readers. 0.9.3 added glc_explore(), a Shiny application for browsing the registry and exporting an annotated reproducible script. 1.0.0 promotes schema 3.0.2 to the default and primary stable import contract and makes schema-declared variable types and factor-level order drive the import itself.
The package is serving two audiences from one model. Programmatic users get glc_collect(), extract_metadata() and add_metadata(), with imports that reject file groups whose factor labels or level order disagree. Interactive users get an Explorer that filters by device, wearing position, modality, role and data state, pages large inventories at 100 rows, and caches remote metadata for immutable revisions. Schemas 1.0.0 and 2.0.0 stay reachable as barebones legacy paths.
With 3.0.2 named the primary stable contract and the older schemas explicitly labelled legacy, retiring the barebones paths is the most likely next structural move. The notes give no indication of work beyond the current schema line.
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 glcdp or tf.
The recursive-computation engine under massProps grows the accessors its consumer needed
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
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-packages — within Infra & APIs. glcdp is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. glcdp is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
Top glcdp alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "glcdp alternatives" section above for the current picks, or visit /alternatives/glcdp 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.