mlr3mbo
mlr3mbo picked its defaults from a benchmark study, not from taste
Comparison · Analytics
dtplyr vs fable
A side-by-side editorial comparison of dtplyr and fable — release velocity, themes, recent moves, and the top alternatives to consider.
dtplyr vs fable: at a glance
What is dtplyr?
dtplyr stopped hijacking data.table objects and became an opt-in translator
dtplyr converts dplyr and tidyr code into data.table syntax, and 1.3.0 redrew its boundary: verbs no longer dispatch to dtplyr translations just because dtplyr is loaded, so lazy_dt() has to be called explicitly. Since then the work has been translation coverage — reframe(), case_match(), consecutive_id() — plus a long tail of correctness fixes in grouping and .by.
What is fable?
fable keeps widening its model shelf, one econometric class at a time
fable is the tidyverts forecasting engine, and its releases are almost entirely about which model families it can express. The 0.4.x line added the vector-error-correction and VARIMA classes plus impulse-response methods; 0.5.0 adds fractional differencing via ARFIMA. Between those, the releases are CRAN-check patches and documentation passes.
dtplyr vs fable: editorial side-by-side
dtplyr
0.0ANALYTICS
dtplyr stopped hijacking data.table objects and became an opt-in translator
◆ Current state
dtplyr converts dplyr and tidyr code into data.table syntax, and 1.3.0 redrew its boundary: verbs no longer dispatch to dtplyr translations just because dtplyr is loaded, so lazy_dt() has to be called explicitly. Since then the work has been translation coverage — reframe(), case_match(), consecutive_id() — plus a long tail of correctness fixes in grouping and .by.
◆ Where it's heading
The package is trailing dplyr's own feature releases rather than leading them, adding each new verb once it settles upstream. Performance work is targeted at specific verbs where data.table has a faster primitive: setorder() for arrange(), reference drops for select(), rleid() for consecutive_id(). Release cadence has thinned considerably since 2023.
◆ Prediction
Expect further one-for-one translations as dplyr adds verbs, and continued fixes around .by and non-standard column names; the entries show no sign of a broader redesign.
fable
0.0ANALYTICS
fable keeps widening its model shelf, one econometric class at a time
◆ Current state
fable is the tidyverts forecasting engine, and its releases are almost entirely about which model families it can express. The 0.4.x line added the vector-error-correction and VARIMA classes plus impulse-response methods; 0.5.0 adds fractional differencing via ARFIMA. Between those, the releases are CRAN-check patches and documentation passes.
◆ Where it's heading
The package is closing the gap with the older forecast package's model coverage while keeping the tidy model-specification grammar. Each substantive release is a new model class plus the generate()/IRF() plumbing to make it behave like the existing ones. Maintenance releases cluster around CRAN policy and ggplot2/tsibble compatibility rather than internal rewrites.
◆ Prediction
Expect the next substantive release to add another model class or extend generate()/IRF() coverage to the classes that still lack them, rather than change the modelling interface.
Alternatives to dtplyr and fable
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 dtplyr or fable.
loo
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
mlr3fselect
mlr3fselect turned feature selection into an asynchronous, distributable job
lime
lime survives on compatibility patches years after its research moment
mlr3measures
mlr3measures is systematically retrofitting sample weights across every metric
mlr3cluster
mlr3cluster went from a handful of clusterers to covering the field
See all dtplyr alternatives → · See all fable alternatives →
Recent activity from dtplyr and fable
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 6mo agofablefable adds ARFIMA and fractional differencing ⚡
- 11mo agodtplyrreframe(), case_match() and consecutive_id() gain translations
- 1y agofableIndexing and generate() fixes for VECM models
- 1y agofableVECM and VARIMA models land, plus IRF for VAR and ARIMA ⚡
- 2y agofablePatch for C++ R header changes
- 2y agofableCRAN check patch with generate() fixes
- 3y agodtplyrcrayon dependency dropped
- 3y agodtplyrVerbs stop auto-dispatching; lazy_dt() now required ⚡
- 3y agofableTSLM forecasts gain Student's t intervals
- 3y agodtplyrdtplyr 1.2.2
- 4y agodtplyrdtplyr 1.2.1
- 4y agodtplyrEight tidyr verbs gain data.table translations
Frequently asked questions
What is the difference between dtplyr and fable?
They serve adjacent needs but don't currently overlap on shipped themes. dtplyr and fable 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 dtplyr better than fable?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dtplyr and fable 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 dtplyr?
Top dtplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "dtplyr alternatives" section above for the current picks, or visit /alternatives/dtplyr for the full list with editorial commentary on each.
What are the best alternatives to fable?
Top fable alternatives in Analytics are ranked by recent ship velocity. Browse the "fable alternatives" section above for the current picks, or visit /alternatives/fable for the full list with editorial commentary on each.