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fastml vs TidyDensity

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

fastml vs TidyDensity: at a glance

FeaturefastmlTidyDensity
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesautoml, tidymodels, survival analysis, cross-validationstatistical-distributions, random-generation, parameter-estimation, tidyverse
Last editorial update1h ago46m 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 TidyDensity?

A distribution catalogue that grows by one family at a time, and rarely breaks anything.

TidyDensity generates tidy-format random data from statistical distributions, with parameter estimation, AIC calculation, summary tables and automatic plotting for each one. Its releases follow a fixed template — breaking changes, new features, minor fixes — and the breaking section is usually empty. Growth comes distribution by distribution: Bernoulli, Burr, triangular, chi-square, zero-truncated negative binomial and others each arrive with a matching set of param_estimate, aic and stats_tbl helpers.

Read the full TidyDensity trajectory →

fastml vs TidyDensity: 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.

T
TidyDensity
ANALYTICS
0.0

A distribution catalogue that grows by one family at a time, and rarely breaks anything.

◆ Current state

TidyDensity generates tidy-format random data from statistical distributions, with parameter estimation, AIC calculation, summary tables and automatic plotting for each one. Its releases follow a fixed template — breaking changes, new features, minor fixes — and the breaking section is usually empty. Growth comes distribution by distribution: Bernoulli, Burr, triangular, chi-square, zero-truncated negative binomial and others each arrive with a matching set of param_estimate, aic and stats_tbl helpers.

◆ Where it's heading

The package is filling out a matrix rather than changing shape — every new distribution gets the same four or five companion functions, so the surface grows predictably and the design does not. What variation exists comes from utilities that work across distributions: MCMC sampling, bootstrap helpers, time series conversion, distribution comparison. The two genuine breaking changes in this window were both internal reworks, moving generation onto data.table and rewriting quantile normalization for speed.

◆ Prediction

The established pattern of adding a distribution with its full helper set is the most likely continuation. Recent releases have been small, suggesting the catalogue is approaching the distributions its author considers worth covering.

Alternatives to fastml and TidyDensity

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 TidyDensity.

See all fastml alternatives → · See all TidyDensity alternatives →

Recent activity from fastml and TidyDensity

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

  1. 8mo agofastmlVersion 0.7.5
  2. 11mo agoTidyDensityquantile_normalize rewritten, changing its output
  3. 1y agofastmlEngine-specific tuning, imbalance handling and explainability
  4. 1y agoTidyDensityDocumentation corrections for two distribution functions
  5. 1y agofastmlSingle-workflow evaluation fix
  6. 1y agofastmlVersion 0.5.0
  7. 2y agoTidyDensityZero-truncated distributions and AIC helpers added in bulk
  8. 2y agoTidyDensityMCMC sampling and quantile normalization join the utilities
  9. 2y agoTidyDensityGeneration moves to data.table; native pipe raises the R floor
  10. 2y agoTidyDensityDistributions convertible to time series objects

Frequently asked questions

What is the difference between fastml and TidyDensity?

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

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

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