mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of GeoThinneR and smam — 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.
Animal-movement models in R, where new stochastic processes arrive years apart.
smam fits statistical models of animal movement, covering moving-resting processes with and without measurement error, moving-resting-handling, and moving-moving processes, with simulation, point estimation and variance estimation for each. The last three releases are pure upkeep: guarding Rf_error calls after an Rcpp update, a maintainer email change, and a compiler warning fix. The substantive work in this window is 0.7.0, which added estimate and vcov generics across all fit functions, and 0.6.0, which added the moving-moving process.
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.
smam fits statistical models of animal movement, covering moving-resting processes with and without measurement error, moving-resting-handling, and moving-moving processes, with simulation, point estimation and variance estimation for each. The last three releases are pure upkeep: guarding Rf_error calls after an Rcpp update, a maintainer email change, and a compiler warning fix. The substantive work in this window is 0.7.0, which added estimate and vcov generics across all fit functions, and 0.6.0, which added the moving-moving process.
This package grows by adding process models, and it does so rarely. Between the moving-moving process in 2021 and now, the only interface-level change has been the 0.7.0 generics that gave every fit function a common way to retrieve estimates and their covariance, which is consolidation of an accumulated collection rather than expansion of it. The three releases since are entirely reactive to toolchain and CRAN pressure, and they arrive in step with the maintainer's other package coga, which received the same Rcpp guard within twenty minutes on the same day.
Expect further releases to be CRAN and Rcpp maintenance unless a new movement process is published, which is what has historically prompted a minor version here. The generics added in 0.7.0 give any future process model a ready-made interface to slot into.
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 smam.
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 smam 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 smam 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 smam 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 smam alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "smam alternatives" section above for the current picks, or visit /alternatives/smam for the full list with editorial commentary on each.