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Comparison · Analytics
assesslite vs fastml
A side-by-side editorial comparison of assesslite and fastml — release velocity, themes, recent moves, and the top alternatives to consider.
assesslite vs fastml: at a glance
What is assesslite?
Four releases in fifteen hours take causal assumption-checking from resampling to identification
AssessLite attacks the structural assumptions behind a causal finding and returns three-way verdicts — stable, unstable, or not resolvable — feeding proceed, conditional or abstain decisions, with an auditable JSON record validated against a shared schema. It runs natively in R and Python against one spec, with the Python engine reproducing R's coxph(ties=breslow) exactly. The entire 0.1.0-through-0.4.0 arc landed inside a single day in July 2026.
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.
assesslite vs fastml: editorial side-by-side
assesslite
0.0ANALYTICS
Four releases in fifteen hours take causal assumption-checking from resampling to identification
◆ Current state
AssessLite attacks the structural assumptions behind a causal finding and returns three-way verdicts — stable, unstable, or not resolvable — feeding proceed, conditional or abstain decisions, with an auditable JSON record validated against a shared schema. It runs natively in R and Python against one spec, with the Python engine reproducing R's coxph(ties=breslow) exactly. The entire 0.1.0-through-0.4.0 arc landed inside a single day in July 2026.
◆ Where it's heading
The releases are cumulative, each restating the previous feature set and adding to it, so read them as one launch rather than four. The direction across that launch is clear: it started with resampling attacks (permutation, holdout, temporal split, subgroup), turned toward causal identification with declared DAGs and the backdoor criterion, then reached into genuinely dependent data with spatial and interference checks. The correctness work moves in step — the 0.3.0 Bonferroni adjustment fixed a holdout rule that was flagging roughly m times too often with m variants.
◆ Prediction
The project has repeatedly shipped what it previously listed as future work within days, so the next release most likely converts another declared gap rather than opening a new front.
fastml
0.0ANALYTICS
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.
Alternatives to assesslite and fastml
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 assesslite or fastml.
cfbfastr
College football's open data client hit v2 — and now reports how many API calls you have left.
rnpn
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
GeneNMF
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
rempsyc
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
sdsfun
A spatial-statistics utility package exists to be depended on, and is built accordingly.
See all assesslite alternatives → · See all fastml alternatives →
Recent activity from assesslite and fastml
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoassessliteAssessLite 0.4.0
- 1mo agoassessliteAssessLite 0.3.0
- 1mo agoassessliteAssessLite 0.2.0 ⚡
- 1mo agoassessliteAssessLite 0.1.0 ⚡
- 8mo agofastmlVersion 0.7.5 ⚡
- 1y agofastmlEngine-specific tuning, imbalance handling and explainability
- 1y agofastmlSingle-workflow evaluation fix
- 1y agofastmlVersion 0.5.0
Frequently asked questions
What is the difference between assesslite and fastml?
They serve adjacent needs but don't currently overlap on shipped themes. assesslite 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.
Is assesslite better than fastml?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. assesslite 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.
What are the best alternatives to assesslite?
Top assesslite alternatives in Analytics are ranked by recent ship velocity. Browse the "assesslite alternatives" section above for the current picks, or visit /alternatives/assesslite for the full list with editorial commentary on each.
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.