TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
Comparison · Analytics
fastml vs labelled
A side-by-side editorial comparison of fastml and labelled — release velocity, themes, recent moves, and the top alternatives to consider.
fastml vs labelled: at a glance
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.
What is labelled?
The bridge between Stata/SPSS labelled data and tidy R keeps widening, one integration at a time.
labelled manages variable labels, value labels and user-defined missing values on data imported from Stata, SPSS and SAS, filling the gap between those formats' metadata and R's native types. Recent releases have pushed outward from the core label accessors: survey design objects from the survey package are now supported throughout, look_for() results can be rendered as formatted gt tables, and dictionary data frames convert in both directions. Error messaging moved wholesale to cli in 2.14.0.
fastml vs labelled: editorial side-by-side
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.
labelled
0.0ANALYTICS
The bridge between Stata/SPSS labelled data and tidy R keeps widening, one integration at a time.
◆ Current state
labelled manages variable labels, value labels and user-defined missing values on data imported from Stata, SPSS and SAS, filling the gap between those formats' metadata and R's native types. Recent releases have pushed outward from the core label accessors: survey design objects from the survey package are now supported throughout, look_for() results can be rendered as formatted gt tables, and dictionary data frames convert in both directions. Error messaging moved wholesale to cli in 2.14.0.
◆ Where it's heading
The arc is toward being usable wherever labelled data ends up, not just where it is loaded. Each recent release either extends support to another object type — survey designs, packed columns, plain vectors, tibbles with list columns — or adds a conversion path between labels and some other representation. The look_for() search function has become a second centre of gravity alongside the label accessors, accumulating its own output formats and long-format conversions.
◆ Prediction
The pattern of adding compatibility with one more object type or output format per release is stable and likely continues. Nothing in these entries indicates a change to the underlying haven_labelled representation the package is built on.
Alternatives to fastml and labelled
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 labelled.
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 fastml alternatives → · See all labelled alternatives →
Recent activity from fastml and labelled
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 8mo agofastmlVersion 0.7.5 ⚡
- 9mo agolabelledFormatted look_for() tables and two-way dictionary conversion
- 11mo agolabelledSurvey design objects supported across the package
- 1y agofastmlEngine-specific tuning, imbalance handling and explainability
- 1y agofastmlSingle-workflow evaluation fix
- 1y agolabelledRegression in set_variable_labels() corrected
- 1y agofastmlVersion 0.5.0
- 1y agolabelledcli adopted for all messaging; null_action gains options
- 2y agolabelledCustom functions can rewrite variable and value labels in bulk
- 3y agolabelledPacked columns supported and label attributes exposed directly
Frequently asked questions
What is the difference between fastml and labelled?
They serve adjacent needs but don't currently overlap on shipped themes. fastml and labelled 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 labelled?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fastml and labelled 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 labelled?
Top labelled alternatives in Analytics are ranked by recent ship velocity. Browse the "labelled alternatives" section above for the current picks, or visit /alternatives/labelled for the full list with editorial commentary on each.