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mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of GeoThinneR and tf — release velocity, themes, recent moves, and the top alternatives to consider.
Spatial thinning grows a result object, and the API breaks to make room for it
GeoThinneR removes spatially redundant occurrence records before species distribution modelling. Version 2.0.0 restructured it around a GeoThinned S3 class with print, summary, plot and trial-accessor methods, replacing the bare logical vectors earlier versions returned, and reorganised the methods into three named strategies — distance, grid and precision — with the search algorithm as a separate argument. The two releases since have added a priority system for choosing which of several tied points to drop.
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.
GeoThinneR removes spatially redundant occurrence records before species distribution modelling. Version 2.0.0 restructured it around a GeoThinned S3 class with print, summary, plot and trial-accessor methods, replacing the bare logical vectors earlier versions returned, and reorganised the methods into three named strategies — distance, grid and precision — with the search algorithm as a separate argument. The two releases since have added a priority system for choosing which of several tied points to drop.
The package is moving from a function that returns an answer to a tool that returns something you can interrogate. Multiple thinning trials are first-class — you can ask for the largest, fetch a specific one, summarise one — and the recent work is about making the choice among tied candidates controllable rather than random. Dependency discipline runs alongside: the R-tree method was dropped when its package was not on CRAN, and spatial coverage degrades to NA rather than failing when s2 is missing.
The priority mechanism now covers all three strategies and the last release was an overflow fix in the local kd-tree path at large sizes, so scale is where the pressure is. More work on the distance methods at large N is the likelier next step than another strategy.
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 GeoThinneR 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.
See all GeoThinneR alternatives → · See all tf alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. GeoThinneR 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. GeoThinneR 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 GeoThinneR alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "GeoThinneR alternatives" section above for the current picks, or visit /alternatives/geothinner 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.