TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of cTMed and fastml — release velocity, themes, recent moves, and the top alternatives to consider.
Continuous-time mediation effects get standardized centrality, six years into steady patch work
cTMed computes direct, indirect and total effects for continuous-time mediation models, with delta-method, Monte Carlo and bootstrap variants of each. Development is a long run of patch releases from the jeksterslab account, roughly every two months, each adding a function or two. The latest adds standardized centrality measures and allows a diagonal sigma across ten standardized estimators.
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.
cTMed computes direct, indirect and total effects for continuous-time mediation models, with delta-method, Monte Carlo and bootstrap variants of each. Development is a long run of patch releases from the jeksterslab account, roughly every two months, each adding a function or two. The latest adds standardized centrality measures and allows a diagonal sigma across ten standardized estimators.
The package is filling out a matrix rather than changing shape: for each effect type there is a delta-method, a Monte Carlo and a bootstrap path, and each release closes another cell. The 2025 releases were largely externally forced — an Armadillo 15.0.x transition at CRAN, a citation addition after the Psychological Methods paper landed — which suggests the statistical core has been settled since the 1.0.6 standardization revision.
The diagonal-sigma option has now reached the standardized estimators; extending it to the remaining unstandardized variants is the obvious next cell to fill.
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 cTMed 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 cTMed alternatives → · See all fastml alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. cTMed is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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. cTMed is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top cTMed alternatives in Analytics are ranked by recent ship velocity. Browse the "cTMed alternatives" section above for the current picks, or visit /alternatives/ctmed 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.