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Comparison · Analytics

fastml vs rempsyc

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

fastml vs rempsyc: at a glance

Featurefastmlrempsyc
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesautoml, tidymodels, survival analysis, cross-validationapa-formatting, psychology-research, statistical-tables, ggplot2
Last editorial update1h ago44m 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 rempsyc?

Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.

rempsyc produces APA-formatted tables and figures for psychology research — nice_table() for results tables, plus plotting helpers for scatter plots, violin plots, densities and simple slopes. Its releases are CRAN submissions that bundle a long run of development versions, so each entry reads as a digest rather than a single change. The most recent, 0.2.0, added point labelling and per-group correlation statistics to nice_scatter and fixed nice_lm() failing on factor covariates with more than two levels.

Read the full rempsyc trajectory →

fastml vs rempsyc: 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.

R
rempsyc
ANALYTICS
0.0

Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.

◆ Current state

rempsyc produces APA-formatted tables and figures for psychology research — nice_table() for results tables, plus plotting helpers for scatter plots, violin plots, densities and simple slopes. Its releases are CRAN submissions that bundle a long run of development versions, so each entry reads as a digest rather than a single change. The most recent, 0.2.0, added point labelling and per-group correlation statistics to nice_scatter and fixed nice_lm() failing on factor covariates with more than two levels.

◆ Where it's heading

Two forces drive this package and neither is its own roadmap. The first is APA style: when the 7th edition advised against beta for standardized coefficients, the package switched its output to italic b with an asterisk. The second is the surrounding ecosystem — formatting is aligned to what lavaanExtra and afex produce, contrast handling was delegated to easystats' modelbased, and Excel correlation matrix export was handed entirely to the correlation package to cut maintenance.

◆ Prediction

The pattern of delegating functionality to specialist packages while keeping the formatting layer is well established and likely continues. Because releases bundle many small dev versions, the next one will probably again mix plotting refinements with fixes surfaced by upstream changes.

Alternatives to fastml and rempsyc

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 rempsyc.

See all fastml alternatives → · See all rempsyc alternatives →

Recent activity from fastml and rempsyc

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

  1. 8mo agofastmlVersion 0.7.5
  2. 11mo agorempsycPoint labels and per-group correlations added to nice_scatter
  3. 1y agofastmlEngine-specific tuning, imbalance handling and explainability
  4. 1y agofastmlSingle-workflow evaluation fix
  5. 1y agofastmlVersion 0.5.0
  6. 1y agorempsycExcel correlation export delegated to the correlation package
  7. 2y agorempsycTable spacing control and a fix for name collision with afex
  8. 2y agorempsycStandardized coefficients switch to APA 7th edition b* notation
  9. 2y agorempsycLegend and standardization-check fixes
  10. 2y agorempsycnice_table starts coercing model objects automatically

Frequently asked questions

What is the difference between fastml and rempsyc?

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

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

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