rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of fastpos and tf — release velocity, themes, recent moves, and the top alternatives to consider.
Dormant three years, then a two-line release nobody using it would notice
fastpos finds the critical point of stability for a Pearson correlation, a simulation problem whose C++ implementation is the entire reason the package exists. Its substantive work concluded in 2022 with 0.5.0, the release prepared during R Journal review, which renamed the precision parameters, moved multicore work to pbapply and let users set corridor limits directly. After three years of silence, 0.6.0 removes an internal restriction to cpp11 and changes index_pop to an integer.
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.
fastpos finds the critical point of stability for a Pearson correlation, a simulation problem whose C++ implementation is the entire reason the package exists. Its substantive work concluded in 2022 with 0.5.0, the release prepared during R Journal review, which renamed the precision parameters, moved multicore work to pbapply and let users set corridor limits directly. After three years of silence, 0.6.0 removes an internal restriction to cpp11 and changes index_pop to an integer.
This is a finished piece of research software in low-effort upkeep. The changelog's centre of gravity is the R Journal review process, and once that concluded the package stopped changing. The single release since is toolchain work of the kind that keeps a package compiling rather than anything a user would see.
Expect nothing beyond occasional compilation or CRAN-check fixes unless the accompanying paper draws requests for other correlation types or resampling schemes.
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 fastpos 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.
They serve adjacent needs but don't currently overlap on shipped themes. fastpos 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. fastpos 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 fastpos alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "fastpos alternatives" section above for the current picks, or visit /alternatives/fastpos 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.