tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
fable vs loo
A side-by-side editorial comparison of fable and loo — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
fable vs loo: at a glance
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.
What is loo?
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
loo computes leave-one-out cross-validation and model comparison for Bayesian models in the Stan ecosystem. Two releases in this window changed what users actually read: 2.7.0 replaced the fixed Pareto-k thresholds with sample-size-dependent ones and dropped the middle category, and 2.10.0 reshaped loo_compare's output into a data.frame with new uncertainty columns. The releases between are diagnostic robustness fixes and moment-matching corrections.
fable vs loo: editorial side-by-side
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.
loo
2.5ANALYTICS
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
◆ Current state
loo computes leave-one-out cross-validation and model comparison for Bayesian models in the Stan ecosystem. Two releases in this window changed what users actually read: 2.7.0 replaced the fixed Pareto-k thresholds with sample-size-dependent ones and dropped the middle category, and 2.10.0 reshaped loo_compare's output into a data.frame with new uncertainty columns. The releases between are diagnostic robustness fixes and moment-matching corrections.
◆ Where it's heading
The package is being brought in line with the current PSIS literature rather than extended with new features, and the practical effect is that the numbers practitioners quote in papers keep changing meaning. Work is increasingly delegated to posterior for shared computations, and the project has added contributor process, benchmarks and a published AI contribution policy.
◆ Prediction
Expect further work on comparison diagnostics — the p_worse and diag_* columns are new enough that their defaults and documentation will likely be revised next.
Alternatives to fable and loo
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 fable or loo.
textrecipes
Text features finally stay sparse all the way to the model.
probably
The package that made calibration a step instead of an afterthought.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
Recent activity from fable and loo
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 20d agoloopsis_smooth_tail revert and simplify arg restored
- 1mo agolooloo_compare returns a data.frame with new uncertainty columns ⚡
- 6mo agofablefable adds ARFIMA and fractional differencing ⚡
- 7mo agolooStacking overflow fixes and posterior-based ESS
- 1y agofableIndexing and generate() fixes for VECM models
- 1y agofableVECM and VARIMA models land, plus IRF for VAR and ARIMA ⚡
- 2y agolooMore robust Pareto-k diagnostics and moment matching
- 2y agofablePatch for C++ R header changes
- 2y agolooPareto-k thresholds now depend on sample size ⚡
- 2y agofableCRAN check patch with generate() fixes
- 3y agolooLOO predictive metrics and CRPS scoring functions
- 3y agofableTSLM forecasts gain Student's t intervals
Frequently asked questions
What is the difference between fable and loo?
Both compete on the same themes — r-stats — within Analytics. loo is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is fable better than loo?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. loo is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
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.
What are the best alternatives to loo?
Top loo alternatives in Analytics are ranked by recent ship velocity. Browse the "loo alternatives" section above for the current picks, or visit /alternatives/loo for the full list with editorial commentary on each.