rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of artoo and tf — release velocity, themes, recent moves, and the top alternatives to consider.
artoo makes any-to-any clinical dataset conversion lossless by construction.
artoo reads and writes SAS XPORT, CDISC Dataset-JSON, NDJSON, Parquet and RDS around one canonical metadata model, so a conversion between any two formats carries labels, CDISC types, lengths, display formats, controlled-terminology references and sort keys intact. It is pure R with no SAS or Java runtime. It reached CRAN at 0.1.1 and has spent 0.1.2 and 0.1.3 on the character-encoding edges.
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.
artoo reads and writes SAS XPORT, CDISC Dataset-JSON, NDJSON, Parquet and RDS around one canonical metadata model, so a conversion between any two formats carries labels, CDISC types, lengths, display formats, controlled-terminology references and sort keys intact. It is pure R with no SAS or Java runtime. It reached CRAN at 0.1.1 and has spent 0.1.2 and 0.1.3 on the character-encoding edges.
Recent work is all about text that does not survive a format change. 0.1.3 adds an invalid_encoding dimension to artoo_checks() that flags bytes which are not valid UTF-8 before a writer aborts on them, accepts the SAS OEM/DOS encoding names, and gives the writers on_invalid = "translit" and "fold" so smart punctuation and accented characters resolve to pinned ASCII instead of failing. The fold tables ship as data specifically so the result is identical on every platform, which is the same determinism argument behind C-locale row sorting in 0.1.0.
The new WLATIN1-to-UTF-8 migration article and the width warning on write_xpt() point the next attention at length and truncation semantics rather than at additional formats. These notes name no further target format.
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 artoo 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. artoo 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. artoo 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 Infra & APIs products to evaluate alongside.
Top artoo alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "artoo alternatives" section above for the current picks, or visit /alternatives/artoo 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.