TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of animovement and fastml — release velocity, themes, recent moves, and the top alternatives to consider.
animovement stopped being a package and became a metapackage over seven focused ones.
animovement handles animal movement data — tracking output from pose-estimation and centroid trackers, cleaned into a standard form. Its 0.7.3 release, the first GitHub tag since November 2024, bundles the 0.5 through 0.7 development series and records a structural change: the codebase was split into aniframe, aniread, aniprocess, anicheck, animetric, anivis and anispace, which animovement now bundles and re-exports. The package has done this before at smaller scale, having renamed itself from trackballr in 0.2.0 to match a widened scope.
fastml added survival modelling and leakage-proof resampling, moving past classification and regression.
A tidymodels-based AutoML wrapper that trains, tunes and compares many engines from one call. The 0.6.x line added engine-specific tuning parameters, class-imbalance handling, early stopping and DALEX-based explainability. The 0.7.5 release is far larger: a full survival analysis task with its own engines, MICE imputation and integrated Brier scoring, plus unbiased nested cross-validation, grouped, blocked and rolling resampling helpers, fold-wise imputation, recipe leakage checks, and a sandbox for user-supplied preprocessing.
animovement handles animal movement data — tracking output from pose-estimation and centroid trackers, cleaned into a standard form. Its 0.7.3 release, the first GitHub tag since November 2024, bundles the 0.5 through 0.7 development series and records a structural change: the codebase was split into aniframe, aniread, aniprocess, anicheck, animetric, anivis and anispace, which animovement now bundles and re-exports. The package has done this before at smaller scale, having renamed itself from trackballr in 0.2.0 to match a widened scope.
Development has moved to the constituent packages, which release far more often than animovement itself — aniframe, aniread and aniprocess have each shipped multiple times in 2026 while animovement tagged once. That makes animovement a stable install surface rather than where the work happens, and the ani_df data class plus the frame-rate to sampling-rate terminology change are the contracts holding the suite together. Optional dependencies are handled through animovement_install_suggested() against r-universe and Bioconductor mirrors.
With the split done and the constituent packages iterating independently, animovement releases are likely to become periodic roll-ups of the suite rather than carriers of new functionality.
A tidymodels-based AutoML wrapper that trains, tunes and compares many engines from one call. The 0.6.x line added engine-specific tuning parameters, class-imbalance handling, early stopping and DALEX-based explainability. The 0.7.5 release is far larger: a full survival analysis task with its own engines, MICE imputation and integrated Brier scoring, plus unbiased nested cross-validation, grouped, blocked and rolling resampling helpers, fold-wise imputation, recipe leakage checks, and a sandbox for user-supplied preprocessing.
The package is moving from convenience wrapper to something that has to be defensible statistically. Nested cross-validation, fold-wise rather than up-front imputation, and explicit leakage checks are all corrections to the shortcuts that make AutoML easy and its scores optimistic. Survival adds a third task type alongside classification and regression, and it arrived with its own metrics rather than being bolted onto the existing ones. Note the entry body is cut off at 8,000 characters, so the release is larger than what is shown.
Expect the remaining survival engines to fill in and the sandboxing of custom preprocessing to tighten, since both were still being iterated on within this same release's commit list.
Other Analytics 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 animovement or fastml.
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
College football's open data client hit v2 — and now reports how many API calls you have left.
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
A spatial-statistics utility package exists to be depended on, and is built accordingly.
See all animovement alternatives → · See all fastml alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r package — within Analytics. animovement and fastml 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. animovement and fastml 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 Analytics products to evaluate alongside.
Top animovement alternatives in Analytics are ranked by recent ship velocity. Browse the "animovement alternatives" section above for the current picks, or visit /alternatives/animovement for the full list with editorial commentary on each.
Top fastml alternatives in Analytics are ranked by recent ship velocity. Browse the "fastml alternatives" section above for the current picks, or visit /alternatives/fastml for the full list with editorial commentary on each.