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fastml vs spEDM

A side-by-side editorial comparison of fastml and spEDM — release velocity, themes, recent moves, and the top alternatives to consider.

fastml vs spEDM: at a glance

FeaturefastmlspEDM
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesautoml, tidymodels, survival analysis, cross-validationcausal-inference, spatial-analysis, empirical-dynamic-modeling, r-package
Last editorial update55m ago2h ago
WebsiteVisit →Visit →

What is fastml?

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.

Read the full fastml trajectory →

What is spEDM?

Spatial causal discovery in R, one exposed method per release

spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.

Read the full spEDM trajectory →

fastml vs spEDM: editorial side-by-side

F
fastml
ANALYTICS
0.0

fastml added survival modelling and leakage-proof resampling, moving past classification and regression.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

S
spEDM
ANALYTICS
0.0

Spatial causal discovery in R, one exposed method per release

◆ Current state

spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.

◆ Where it's heading

The cadence is steady and predictable: each release surfaces one more EDM method as an R-level API with a vignette, then spends the rest of its notes on parameter-handling consistency across the generics. Breaking changes are frequent and deliberate — argument renames, parameter reordering, NA-handling defaults — which reads as a package still settling its interface while the method surface expands. Shared changes appear in tEDM within days, so interface churn lands on both packages at once.

◆ Prediction

Expect the next release to expose another causality variant at the R level with an accompanying vignette, and to continue renaming or reordering parameters toward consistency across the spatial and temporal packages.

Alternatives to fastml and spEDM

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 fastml or spEDM.

See all fastml alternatives → · See all spEDM alternatives →

Recent activity from fastml and spEDM

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 4mo agospEDMData slicing for large-scale pattern causality, plus API breaks
  2. 6mo agospEDMspEDM 1.11
  3. 6mo agospEDMSpatially convergent partial cross mapping reaches the R API
  4. 8mo agofastmlVersion 0.7.5
  5. 8mo agospEDMRaster cross mapping with anisotropic embedding
  6. 11mo agospEDMConfigurable distance metrics and multithreaded distance computation
  7. 1y agofastmlEngine-specific tuning, imbalance handling and explainability
  8. 1y agospEDMSpatial logistic map exposed at the R level
  9. 1y agofastmlSingle-workflow evaluation fix
  10. 1y agofastmlVersion 0.5.0

Frequently asked questions

What is the difference between fastml and spEDM?

They serve adjacent needs but don't currently overlap on shipped themes. fastml and spEDM 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.

Is fastml better than spEDM?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fastml and spEDM 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.

What are the best alternatives to fastml?

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.

What are the best alternatives to spEDM?

Top spEDM alternatives in Analytics are ranked by recent ship velocity. Browse the "spEDM alternatives" section above for the current picks, or visit /alternatives/spedm for the full list with editorial commentary on each.