mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of melodi and tf — release velocity, themes, recent moves, and the top alternatives to consider.
INSEE's statistics API gets a French R client that keeps meeting its edge cases
Rmelodi is InseeFrLab's R client for the Melodi APIs, which serve French official statistics. It reached 1.0.0 in February 2026 with the technical call parameters moved out of function arguments and into options(), and a per-request row limit raised to 100,000 on the server side. Everything since has been dataset-specific: field names that vary between datasets, geography labels, performance.
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.
Rmelodi is InseeFrLab's R client for the Melodi APIs, which serve French official statistics. It reached 1.0.0 in February 2026 with the technical call parameters moved out of function arguments and into options(), and a per-request row limit raised to 100,000 on the server side. Everything since has been dataset-specific: field names that vary between datasets, geography labels, performance.
The work is convergence with an API that is still moving. Version 0.3.0 added label lookups so codes become readable; 1.0.0 centralised configuration; 1.0.1 and 1.0.2 each fix a place where a real dataset does not match the assumed shape — get_range_geo() needing an extra label field, then the consumer price index series naming its value column differently from every other dataset. Release notes are in French, which is consistent with the audience.
The 1.0.x pattern is one dataset-shape exception per release, which suggests the client is still discovering how much the Melodi datasets vary rather than converging on a general parser. Expect more of the same until the variation is handled generically.
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 melodi or tf.
mice can finally predict, not just estimate, from multiply imputed data.
A market-microstructure toolkit that keeps adding estimators as the papers land.
A vowel-analysis package trimming dependencies after an email address got it archived.
The R half of the EMU speech database system, fixing what was quietly broken.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. melodi 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. melodi 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 melodi alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "melodi alternatives" section above for the current picks, or visit /alternatives/melodi 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.