mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of smam and writeAlizer — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Six months of releases and not one of them touched the scoring models
writeAlizer generates predicted writing-quality scores from features produced by Coh-Metrix, ReaderBench and GAMET, downloading its trained scoring models on demand. Every release in this window — nine of them between September 2025 and February 2026 — is about that download path rather than the scoring: classed error conditions, checksum verification, an offline mode, a mockable artifact directory, and dependency reporting for the model families a user actually invokes.
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.
writeAlizer generates predicted writing-quality scores from features produced by Coh-Metrix, ReaderBench and GAMET, downloading its trained scoring models on demand. Every release in this window — nine of them between September 2025 and February 2026 — is about that download path rather than the scoring: classed error conditions, checksum verification, an offline mode, a mockable artifact directory, and dependency reporting for the model families a user actually invokes.
The package is being made safe to distribute. CRAN's policy on packages that reach the internet drove the first wave — graceful failure, tests that preflight their URLs and skip, examples seeded from a local mock model — and 1.7.0 turned the accumulated fixes into structure with named error classes for each failure mode. Only 1.7.2 adds anything a user would ask for: filename handling for Coh-Metrix and GAMET outputs that arrive as paths.
With the artifact registry hardened and documented, the pressure that produced nine releases in six months should ease, and attention can return to the models themselves — the vignette on scoring-model development added in 1.7.2 hints at that. Nothing here promises new models.
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 smam or writeAlizer.
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 smam alternatives → · See all writeAlizer alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. smam and writeAlizer 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. smam and writeAlizer 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 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.
Top writeAlizer alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "writeAlizer alternatives" section above for the current picks, or visit /alternatives/writealizer for the full list with editorial commentary on each.