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The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of surveycore and tf — release velocity, themes, recent moves, and the top alternatives to consider.
surveycore declared its API stable with every survey design type covered.
surveycore is the estimation engine of a two-package survey stack, handling design objects and variance estimation while surveytidy supplies the dplyr verbs on top. The June release marks it 1.0.0 and states the API is complete and stable across Taylor series linearization, replicate weights, two-phase and non-probability designs, with means, totals, frequencies, quantiles, ratios, correlations, regression, t-tests, ANOVA and effective sample size all in place.
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.
surveycore is the estimation engine of a two-package survey stack, handling design objects and variance estimation while surveytidy supplies the dplyr verbs on top. The June release marks it 1.0.0 and states the API is complete and stable across Taylor series linearization, replicate weights, two-phase and non-probability designs, with means, totals, frequencies, quantiles, ratios, correlations, regression, t-tests, ANOVA and effective sample size all in place.
The last months before 1.0.0 were spent making the awkward designs behave like the ordinary ones. Non-probability designs gained jackknife replicate schemes and got their bootstrap repweights routed through the replicate-weight variance estimator in survey_glm(), matching every other estimation function. The survey_collection abstraction — several surveys treated as one pseudo-data-frame — was tightened rather than extended: divergent grouping across members now errors instead of stitching a patchwork with bind_rows(), and the missing-variable argument was renamed and given a stored default on the collection itself. A documentation audit before 1.0.0 turned up six dispatch and print bugs and corrections across forty-plus files, which is the kind of thing that surfaces when an API is being frozen rather than extended.
A declared-stable API means the next releases should be additive or corrective rather than breaking, and the pre-1.0.0 pattern of breaking renames should stop. The tight version pinning between the two packages means surveytidy releases will keep following surveycore's.
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 surveycore 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
See all surveycore alternatives → · See all tf alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-packages — within Infra & APIs. surveycore 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. surveycore 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 surveycore alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "surveycore alternatives" section above for the current picks, or visit /alternatives/surveycore 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.