ggpointless
ggpointless keeps adding the ggplot2 layers nobody else bothered to write.
A side-by-side editorial comparison of radiatR and tf — release velocity, themes, recent moves, and the top alternatives to consider.
A circular-statistics toolkit for animal movement, shipped and then tightened in three weeks.
radiatR reads movement trajectories, plots them on circular axes, computes kinematics such as speed and path sinuosity, and runs the circular statistics that go with them, including tests of mean direction, symmetry and unimodality. It arrived as a first public release on 9 July 2026 with an accompanying Shiny app, and has had two releases since at roughly weekly intervals. The 0.1.x line is still setting its boundaries: 0.1.2 removes a loader dialect and makes previously silent data problems into errors.
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.
radiatR reads movement trajectories, plots them on circular axes, computes kinematics such as speed and path sinuosity, and runs the circular statistics that go with them, including tests of mean direction, symmetry and unimodality. It arrived as a first public release on 9 July 2026 with an accompanying Shiny app, and has had two releases since at roughly weekly intervals. The 0.1.x line is still setting its boundaries: 0.1.2 removes a loader dialect and makes previously silent data problems into errors.
Three releases in twelve days show a package hardening in public rather than accreting features. The direction of travel is toward refusing bad input instead of quietly working around it: non-finite coordinate rows now error by default rather than being dropped silently, combining Tracks objects rejects colliding trajectory ids and conflicting calibration metadata instead of merging them and discarding one side, and the Shiny app clears prior state before reading a new upload. The statistical surface is growing in parallel, but within the scope the first release already claimed.
Expect the remaining goodness-of-fit gap the notes name explicitly, Jones-Pewsey, to be filled in a later release, and the error-on-bad-input treatment to reach the parts of the loader it has not yet covered. A CRAN submission is the natural next step for a package this young, though nothing in these entries commits to one.
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 radiatR or tf.
ggpointless keeps adding the ggplot2 layers nobody else bothered to write.
mpactr spent two spring releases normalizing case in metadata after users kept tripping on it.
surveytidy taught every dplyr verb to operate on a whole collection of surveys at once.
surveycore declared its API stable with every survey design type covered.
PEIMAN2 cut its annotation database loose from its release cycle without breaking CRAN.
prospectr spent its biggest release in years fixing spectra it had been quietly mangling.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. radiatR 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. radiatR 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 radiatR alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "radiatR alternatives" section above for the current picks, or visit /alternatives/radiatr 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.